Duplicate from cyankiwi/North-Mini-Code-1.0-AWQ-INT4
Browse filesCo-authored-by: Ton Cao <cpatonn@users.noreply.huggingface.co>
- .gitattributes +36 -0
- README.md +316 -0
- chat_template.jinja +259 -0
- config.json +196 -0
- generation_config.json +7 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +0 -0
- special_tokens_map.json +30 -0
- tokenizer.json +3 -0
- tokenizer_config.json +328 -0
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README.md
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| 1 |
+
---
|
| 2 |
+
base_model: CohereLabs/North-Mini-Code-1.0
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| 3 |
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inference: false
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| 4 |
+
library_name: transformers
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| 5 |
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license: apache-2.0
|
| 6 |
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tags:
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| 7 |
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- conversational
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| 8 |
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- chat
|
| 9 |
+
- code
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| 10 |
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- agent
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| 11 |
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---
|
| 12 |
+
|
| 13 |
+
<div align="center">
|
| 14 |
+
<img src="https://huggingface.co/buckets/cyankiwi/activation-aware-2.0/resolve/banner/cyankiwi-banner-awq-0.png">
|
| 15 |
+
</div>
|
| 16 |
+
|
| 17 |
+
<div align="left">
|
| 18 |
+
<table align="center" style="border-collapse:collapse; border:none;">
|
| 19 |
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<tr style="border:none;">
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| 20 |
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<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Version</b></td>
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| 21 |
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<td align="left" style="border:none; padding:4px 0;">26.05.01</td>
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| 22 |
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</tr>
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| 23 |
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<tr style="border:none;">
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| 24 |
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<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Calibration</b></td>
|
| 25 |
+
<td align="left" style="border:none; padding:4px 0;">
|
| 26 |
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<a href="https://huggingface.co/datasets/cyankiwi/calibration" target="_blank">STEM and Agentic</a>
|
| 27 |
+
</td>
|
| 28 |
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</tr>
|
| 29 |
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<tr style="border:none;">
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| 30 |
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<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Languages</b></td>
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| 31 |
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<td align="left" style="border:none; padding:4px 0;">
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| 32 |
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<code>EN</code> <code>ZH</code> <code>HI</code> <code>AR</code> <code>RU</code>
|
| 33 |
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<code>JA</code> <code>KO</code> <code>NL</code> <code>FR</code> <code>ES</code>
|
| 34 |
+
</td>
|
| 35 |
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</tr>
|
| 36 |
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<tr style="border:none;">
|
| 37 |
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<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Model Size</b></td>
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| 38 |
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<td align="left" style="border:none; padding:4px 0;">17.20 GB</td>
|
| 39 |
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</tr>
|
| 40 |
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<tr style="border:none;">
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| 41 |
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<td align="right" style="border:none; padding:4px 12px 4px 0;"><b>Contact</b></td>
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| 42 |
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<td align="left" style="border:none; padding:4px 0;">
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| 43 |
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<a href="mailto:ton@cyan.kiwi">Email</a>
|
| 44 |
+
</td>
|
| 45 |
+
</tr>
|
| 46 |
+
</table>
|
| 47 |
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</div>
|
| 48 |
+
|
| 49 |
+
---
|
| 50 |
+
|
| 51 |
+
# **Model Card for North Mini Code**
|
| 52 |
+
|
| 53 |
+
## **Model Summary**
|
| 54 |
+
|
| 55 |
+
North Mini Code is an open weights research release of a 30B-A3B parameter model optimized for code generation, agentic software engineering, and terminal tasks.
|
| 56 |
+
|
| 57 |
+
Developed by: [Cohere](https://cohere.com/) and [Cohere Labs](https://cohere.com/research)
|
| 58 |
+
|
| 59 |
+
* Point of Contact: [**Cohere Labs**](https://cohere.com/research)
|
| 60 |
+
* License: Apache 2.0
|
| 61 |
+
* Model: North Mini Code
|
| 62 |
+
* Model Size: 30B total; 3B active
|
| 63 |
+
* Context length: 256K & 64K max output
|
| 64 |
+
|
| 65 |
+
For more details about this model, please check out our [blog post](https://huggingface.co/blog/CohereLabs/introducing-north-mini-code).
|
| 66 |
+
|
| 67 |
+
**Try North Mini Code**
|
| 68 |
+
|
| 69 |
+
You can try out North Mini Code before downloading the weights in OpenCode and our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereLabs/North-Mini-Code-1.0).
|
| 70 |
+
|
| 71 |
+
**Evaluation**
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
<details>
|
| 76 |
+
<summary><span style="font-size: 80%;"><b>Benchmarking Methodology [CLICK TO EXPAND]</span></b></summary>
|
| 77 |
+
|
| 78 |
+
- <span style="font-size: 80%;">We used SWE-Bench Verified, SWE-Bench Pro, Terminal-Bench v2, and Terminal-Bench Hard to benchmark North Mini Code's agentic coding capabilities. For evaluation harnesses, we used the Swe-Agent harness v1.1.0 for SWE-Bench, and a simple ReAct harness employing a single terminal-use tool based on Harbor's Tmux session implementation for Terminal-Bench v2. For Terminal Bench Hard, we directly used Terminus-2, following the same methodology as the Artificial Analysis Intelligence Index to compare North-Mini-Code-1.0 with the other models. Additionally, we used SciCode and LiveCodeBench v6 as complex code-generation benchmarks outside of tool use.</span>
|
| 79 |
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- <span style="font-size: 80%;">We run each benchmark with 3 different seeds and report the average benchmark performance, using temperature=1.0 and top\_p=0.95. We used publicly reported scores for competitor models, either from original reports or the Artificial Analysis Intelligence Index, where available. Additionally, Gemma4’s scores for agentic coding tasks were reported by [Qwen team](https://qwen.ai/blog?id=qwen3.6-35b-a3b). For benchmark results that any public report is missing, denoted by (\*) in the figure, we run them internally using the recommended model configuration.</span>
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| 80 |
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</details>
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| 81 |
+
|
| 82 |
+
**Usage**
|
| 83 |
+
|
| 84 |
+
Please install transformers from the source repository that includes the necessary changes for this model. We recommend using the following set of sampling parameters for generation: \`temperature=1.0\`, \`top\_p=0.95\`.
|
| 85 |
+
|
| 86 |
+
```py
|
| 87 |
+
# pip install transformers
|
| 88 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM
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| 89 |
+
|
| 90 |
+
model_id = "CohereLabs/North-Mini-Code-1.0"
|
| 91 |
+
tokenizer = AutoTokenizer.from_pretrained(model_id)
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| 92 |
+
model = AutoModelForCausalLM.from_pretrained(model_id)
|
| 93 |
+
|
| 94 |
+
prompt = "Write a python program to check if a string is a palindrome or not."
|
| 95 |
+
|
| 96 |
+
# Format message with the North-Mini-Code-1.0 chat template
|
| 97 |
+
messages = [{"role": "user", "content": prompt}]
|
| 98 |
+
input_ids = tokenizer.apply_chat_template(
|
| 99 |
+
messages,
|
| 100 |
+
tokenize=True,
|
| 101 |
+
add_generation_prompt=True,
|
| 102 |
+
return_tensors="pt",
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| 103 |
+
)
|
| 104 |
+
|
| 105 |
+
gen_tokens = model.generate(
|
| 106 |
+
**input_ids,
|
| 107 |
+
max_new_tokens=1024,
|
| 108 |
+
do_sample=True,
|
| 109 |
+
temperature=1.0,
|
| 110 |
+
top_p=0.95
|
| 111 |
+
)
|
| 112 |
+
|
| 113 |
+
gen_text = tokenizer.decode(gen_tokens[0])
|
| 114 |
+
print(gen_text)
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
You can also use the model directly using transformers `pipeline` abstraction:
|
| 118 |
+
|
| 119 |
+
```py
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| 120 |
+
from transformers import pipeline
|
| 121 |
+
import torch
|
| 122 |
+
|
| 123 |
+
model_id = "CohereLabs/North-Mini-Code-1.0"
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| 124 |
+
|
| 125 |
+
prompt = """Given a list of unique words each of size k and an n sized word, w, where n is a multiple of k,
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| 126 |
+
Write a program in python to determine the number of unique combinations of words in the list that can be concatenated to form an anagram of the word w.
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| 127 |
+
"""
|
| 128 |
+
|
| 129 |
+
pipe = pipeline(
|
| 130 |
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"text-generation",
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| 131 |
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model=model_id,
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| 132 |
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torch_dtype="auto",
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| 133 |
+
device_map="auto",
|
| 134 |
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)
|
| 135 |
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|
| 136 |
+
messages = [
|
| 137 |
+
{"role": "user", "content": f"{prompt}"},
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| 138 |
+
]
|
| 139 |
+
|
| 140 |
+
text = tokenizer.apply_chat_template(
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| 141 |
+
messages,
|
| 142 |
+
tokenize=False,
|
| 143 |
+
add_generation_prompt=True,
|
| 144 |
+
)
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| 145 |
+
|
| 146 |
+
|
| 147 |
+
outputs = pipe(
|
| 148 |
+
messages,
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| 149 |
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max_new_tokens=1024,
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| 150 |
+
do_sample=True,
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| 151 |
+
temperature=1.0,
|
| 152 |
+
top_p=0.95
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| 153 |
+
|
| 154 |
+
)
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| 155 |
+
|
| 156 |
+
print(outputs[0]["generated_text"][-1])
|
| 157 |
+
|
| 158 |
+
```
|
| 159 |
+
|
| 160 |
+
## **Model Details**
|
| 161 |
+
|
| 162 |
+
**Input**: Text only.
|
| 163 |
+
|
| 164 |
+
**Output**: Model generates text.
|
| 165 |
+
|
| 166 |
+
**Model Architecture**: North-Mini-Code-1.0 is a decoder-only Transformer-based sparse Mixture-of-Experts model. It uses an efficient attention implementation, interleaved between sliding-window attention with RoPE and global attention with no positional embeddings, in a 3:1 ratio. The feed-forward block is an MoE block with 128 experts, of which 8 are activated per token. Each expert block is an FFN block with SwiGLU activation. The router applies a sigmoid activation function to the logits before the top-k selection. We also use a single dense layer before the sparse layers. North-Mini-Code-1.0 was post-trained using a two-stage cascaded supervised fine-tuning (SFT) followed by reinforcement learning with verifiable rewards (RLVR), focusing on agentic coding. For more technical details, please check out our [blog post](https://huggingface.co/blog/CohereLabs/introducing-north-mini-code).
|
| 167 |
+
|
| 168 |
+
**Context Length:** North-Mini-Code-1.0 supports a context length of 256K & 64K output length.
|
| 169 |
+
|
| 170 |
+
### **Tool Use Capabilities:**
|
| 171 |
+
|
| 172 |
+
North-Mini-Code-1.0 has been specifically trained with tool-use capabilities for agentic coding.
|
| 173 |
+
|
| 174 |
+
Tool use with North-Mini-Code-1.0 is supported through [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating#advanced-tool-use--function-calling) in Transformers. We recommend providing tool descriptions using JSON schema.
|
| 175 |
+
|
| 176 |
+
**Tool Use Example \[CLICK TO EXPAND\]**
|
| 177 |
+
|
| 178 |
+
```py
|
| 179 |
+
# Define tools
|
| 180 |
+
tools = [{
|
| 181 |
+
"type": "function",
|
| 182 |
+
"function": {
|
| 183 |
+
"name": "bash",
|
| 184 |
+
"description": "Execute a bash command in the terminal.",
|
| 185 |
+
"parameters": {
|
| 186 |
+
"type": "object",
|
| 187 |
+
"properties": {
|
| 188 |
+
"command": {
|
| 189 |
+
"description": "The bash command to execute.",
|
| 190 |
+
"type": "string"
|
| 191 |
+
}
|
| 192 |
+
},
|
| 193 |
+
"required": ["command"]
|
| 194 |
+
},
|
| 195 |
+
}
|
| 196 |
+
}]
|
| 197 |
+
|
| 198 |
+
# Define conversation input
|
| 199 |
+
conversation = [{"role": "user", "content": "Find out if there is any json file in this folder"}]
|
| 200 |
+
|
| 201 |
+
|
| 202 |
+
# Get the Tool Use prompt
|
| 203 |
+
input_prompt = tokenizer.apply_chat_template(conversation=conversation, tools=tools, tokenize=False, add_generation_prompt=True, return_tensors="pt")
|
| 204 |
+
|
| 205 |
+
# Tokenize the prompt
|
| 206 |
+
input_ids = tokenizer(input_prompt, return_tensors="pt")
|
| 207 |
+
```
|
| 208 |
+
|
| 209 |
+
You can then generate from this input as normal.
|
| 210 |
+
|
| 211 |
+
North Mini Code, similarly as all the other Cohere agent models released to date, supports [interleaved thinking](https://docs.vllm.ai/en/latest/features/interleaved_thinking/) and works best when turned on. You’re strongly encouraged to pass on all the model-generated thinking contents to future agentic steps, and chat turns for the best model performance. Please refer to the linked vllm doc and see how it’s done.
|
| 212 |
+
|
| 213 |
+
If the model generates thinking content and tool calls, you should add both of them to the chat history like so:
|
| 214 |
+
|
| 215 |
+
```py
|
| 216 |
+
# Pass on the tool_call and thinking
|
| 217 |
+
tool_call = {"name": "bash", "arguments": {"command": "ls -al"}}
|
| 218 |
+
reasoning = "The user wants to find if there are any JSON files in the current folder. I should use the `ls` command to list files and then check if there are any JSON files (files ending with .json). Let me first list the files in the current directory."
|
| 219 |
+
|
| 220 |
+
conversation.append({"role": "assistant", "tool_calls": [{"id": "0", "type": "function", "function": tool_call}], "reasoning": reasoning})
|
| 221 |
+
```
|
| 222 |
+
|
| 223 |
+
and then call the tool and append the result, as a dictionary, with the tool role, like so:
|
| 224 |
+
|
| 225 |
+
```py
|
| 226 |
+
# This needs to be a dictionary
|
| 227 |
+
tool_result = {"stdout": "test.json\ntest.py", "return_code": "0"}
|
| 228 |
+
|
| 229 |
+
# Append tool results
|
| 230 |
+
conversation.append({"role": "tool", "tool_call_id": "0", "content": tool_result})
|
| 231 |
+
```
|
| 232 |
+
|
| 233 |
+
After that, you can `generate()` again to let the model use the tool result in the chat.
|
| 234 |
+
|
| 235 |
+
Note that this was a very brief introduction to tool calling \- for more information the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
|
| 236 |
+
|
| 237 |
+
### **vLLM**
|
| 238 |
+
|
| 239 |
+
You can also run the model in vLLM. Please use vLLM main for North Mini Code until a new release is available, and accurate response parsing also requires installing Cohere’s melody library.
|
| 240 |
+
|
| 241 |
+
```shell
|
| 242 |
+
uv pip install "git+https://github.com/vllm-project/vllm.git"
|
| 243 |
+
uv pip install cohere_melody>=0.9.0
|
| 244 |
+
```
|
| 245 |
+
|
| 246 |
+
Then the vllm server can be started with the following command:
|
| 247 |
+
|
| 248 |
+
```shell
|
| 249 |
+
vllm serve CohereLabs/North-Mini-Code-1.0 \
|
| 250 |
+
-tp 2 \
|
| 251 |
+
--max-model-len 320000 \
|
| 252 |
+
--tool-call-parser cohere_command4 \
|
| 253 |
+
--reasoning-parser cohere_command4 \
|
| 254 |
+
--enable-auto-tool-choice
|
| 255 |
+
```
|
| 256 |
+
|
| 257 |
+
**Use locally deployed North Mini Code in OpenCode:**
|
| 258 |
+
|
| 259 |
+
Please use OpenCode main branch until a new release is available.
|
| 260 |
+
|
| 261 |
+
```shell
|
| 262 |
+
# Example commands to install on linux
|
| 263 |
+
git clone https://github.com/anomalyco/opencode.git cd opencode
|
| 264 |
+
|
| 265 |
+
# Install Bun
|
| 266 |
+
curl -fsSL https://bun.sh/install | bash
|
| 267 |
+
export BUN_INSTALL="$HOME/.bun"
|
| 268 |
+
export PATH="$BUN_INSTALL/bin:$PATH"
|
| 269 |
+
|
| 270 |
+
# node-gyp was needed by a dependency
|
| 271 |
+
bun add -g node-gyp
|
| 272 |
+
|
| 273 |
+
# Install dependencies
|
| 274 |
+
bun install
|
| 275 |
+
|
| 276 |
+
# Build CLI
|
| 277 |
+
bun run --cwd packages/opencode build /usr/bin/install -m 755 \
|
| 278 |
+
./opencode/packages/opencode/dist/opencode-linux-x64/bin/opencode \
|
| 279 |
+
/root/.local/bin/opencode
|
| 280 |
+
```
|
| 281 |
+
|
| 282 |
+
To use locally deployed North Mini Code in Opencode, please use this config which enables interleaved reasoning:
|
| 283 |
+
|
| 284 |
+
```json
|
| 285 |
+
{
|
| 286 |
+
"$schema": "https://opencode.ai/config.json",
|
| 287 |
+
"model": "vllm/CohereLabs/North-Mini-Code-1.0",
|
| 288 |
+
"provider": {
|
| 289 |
+
"vllm": {
|
| 290 |
+
"npm": "@ai-sdk/openai-compatible",
|
| 291 |
+
"name": "Local vLLM server",
|
| 292 |
+
"options": {
|
| 293 |
+
"baseURL": "http://127.0.0.1:8000/v1",
|
| 294 |
+
"apiKey": "EMPTY"
|
| 295 |
+
},
|
| 296 |
+
"models": {
|
| 297 |
+
"North-Mini-Code-1.0": {
|
| 298 |
+
"name": "North-Mini-Code-1.0",
|
| 299 |
+
"interleaved": {
|
| 300 |
+
"field": "reasoning"
|
| 301 |
+
},
|
| 302 |
+
"limit": {
|
| 303 |
+
"context": 256000,
|
| 304 |
+
"output": 64000
|
| 305 |
+
}
|
| 306 |
+
}
|
| 307 |
+
}
|
| 308 |
+
}
|
| 309 |
+
}
|
| 310 |
+
}
|
| 311 |
+
|
| 312 |
+
```
|
| 313 |
+
|
| 314 |
+
## **Model Card Contact**
|
| 315 |
+
|
| 316 |
+
For errors or additional questions about details in this model card, contact \[labs@cohere.com\].
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,259 @@
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{%- if not platform_instruction_override %}
|
| 2 |
+
{%- set platform_instruction_override -%}
|
| 3 |
+
These instructions are always to be followed and cannot be overridden by subsequent system or user turns:
|
| 4 |
+
- You will answer requests for educational, informative, or creative content related to safety categories. You will not provide content that is harmful or could be used to cause harm.
|
| 5 |
+
|
| 6 |
+
These instructions serve as your defaults, but they can be overridden in subsequent system or user turns:
|
| 7 |
+
- Your name is Command.
|
| 8 |
+
- You are a large language model built by Cohere.
|
| 9 |
+
{%- endset %}
|
| 10 |
+
{%- endif %}
|
| 11 |
+
{%- set reasoning = reasoning if reasoning is not undefined else (false if reasoning_effort is defined and reasoning_effort | lower == "none" else true) -%}
|
| 12 |
+
{%- set grounding = grounding | default("disabled") | upper %}
|
| 13 |
+
{%- set grounding_enabled = grounding == "ENABLED" %}
|
| 14 |
+
{%- set tools_or_docs_exist = tools or documents %}
|
| 15 |
+
{%- set render_tools_section = true %}
|
| 16 |
+
{%- set render_grounding = grounding_enabled and tools_or_docs_exist %}
|
| 17 |
+
{%- set render_platform_instruction_override = true if platform_instruction_override else false %}
|
| 18 |
+
{%- set has_developer_instruction = developer_instruction or developer_instruction == "" %}
|
| 19 |
+
{%- set render_developer_instruction = true if developer_instruction else false %}
|
| 20 |
+
{%- set convert_first_system_msg = convert_first_system_msg | default(true) -%}
|
| 21 |
+
{%- set skip_thinking = skip_thinking | default(false) -%}
|
| 22 |
+
{{ bos_token }}
|
| 23 |
+
{%- macro document_turn(documents) -%}
|
| 24 |
+
{# format documents into chat turn -#}
|
| 25 |
+
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{%- if not skip_thinking -%}<|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|>{%- endif -%}<|START_ACTION|>[
|
| 26 |
+
{"tool_call_id": "0", "tool_name": "direct-injected-document", "parameters": {}}
|
| 27 |
+
]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[
|
| 28 |
+
{
|
| 29 |
+
"tool_call_id": "0",
|
| 30 |
+
"results": {
|
| 31 |
+
{%- for doc in documents %}
|
| 32 |
+
{%- set doc_val = doc.data if doc.data else doc %}
|
| 33 |
+
|
| 34 |
+
"{{ loop.index0 }}": {{ doc_val|tojson }}{% if not loop.last %},
|
| 35 |
+
{%- endif %}
|
| 36 |
+
{%- endfor %}
|
| 37 |
+
|
| 38 |
+
},
|
| 39 |
+
"is_error": null
|
| 40 |
+
}
|
| 41 |
+
]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}
|
| 42 |
+
{%- macro tool_call_id_to_int(messages, tool_call_id) %}
|
| 43 |
+
{%- if regen_tool_call_ids -%}
|
| 44 |
+
{%- set counter = namespace(value=0) %}
|
| 45 |
+
{%- set tool_call_id_seen = namespace(value=false) %}
|
| 46 |
+
{%- for msg in messages %}
|
| 47 |
+
{%- if msg.tool_calls %}
|
| 48 |
+
{%- for tool_call in msg.tool_calls %}
|
| 49 |
+
{%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}
|
| 50 |
+
{{ counter.value }}
|
| 51 |
+
{%- set tool_call_id_seen.value = true %}
|
| 52 |
+
{%- endif %}
|
| 53 |
+
{%- set counter.value = counter.value + 1 %}
|
| 54 |
+
{%- endfor %}
|
| 55 |
+
{%- endif %}
|
| 56 |
+
{%- endfor %}
|
| 57 |
+
{%- else -%}
|
| 58 |
+
{{ tool_call_id }}
|
| 59 |
+
{%- endif -%}
|
| 60 |
+
{%- endmacro %}
|
| 61 |
+
{%- macro format_tool_message(messages, tool_msg) -%}
|
| 62 |
+
{#- format tool message #}{
|
| 63 |
+
"tool_call_id": "{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}",
|
| 64 |
+
"results": {
|
| 65 |
+
{%- if tool_msg.content is mapping or tool_msg.content is string %}
|
| 66 |
+
|
| 67 |
+
{% if tool_msg.content is string -%}
|
| 68 |
+
{%- set text_wrapper = {"content": tool_msg.content} -%}
|
| 69 |
+
{%- else -%}
|
| 70 |
+
{%- set text_wrapper = tool_msg.content -%}
|
| 71 |
+
{%- endif %}
|
| 72 |
+
"0": {{ text_wrapper|tojson }}
|
| 73 |
+
{%- else %}
|
| 74 |
+
{%- for content in tool_msg.content %}
|
| 75 |
+
|
| 76 |
+
"{{ loop.index0 }}": {{ print_tool_content(content) }}{% if not loop.last %},{% endif %}
|
| 77 |
+
{%- endfor %}
|
| 78 |
+
{%- endif %}
|
| 79 |
+
|
| 80 |
+
},
|
| 81 |
+
"is_error": null
|
| 82 |
+
}
|
| 83 |
+
{%- endmacro -%}
|
| 84 |
+
{%- macro print_tool_content(item) %}
|
| 85 |
+
{%- if item.type|lower == "text" -%}
|
| 86 |
+
{%- set text_wrapper = {"content": item.text} -%}
|
| 87 |
+
{{ text_wrapper|tojson }}
|
| 88 |
+
{%- elif item.type|lower == "document" and item.document and "data" in item.document -%}
|
| 89 |
+
{{ item.document.data|tojson }}
|
| 90 |
+
{%- else -%}
|
| 91 |
+
{{ item|tojson }}
|
| 92 |
+
{%- endif -%}
|
| 93 |
+
{%- endmacro %}
|
| 94 |
+
{%- macro print_msg(msg) %}
|
| 95 |
+
{%- if msg is string -%}
|
| 96 |
+
<|START_TEXT|>{{ msg }}<|END_TEXT|>
|
| 97 |
+
{%- elif msg.content is string -%}
|
| 98 |
+
<|START_TEXT|>{{ msg.content }}<|END_TEXT|>
|
| 99 |
+
{%- else %}
|
| 100 |
+
{%- set last_was_text = namespace(value=false) %}
|
| 101 |
+
{%- for content in msg.content %}
|
| 102 |
+
{%- if content.type|lower == "text" -%}
|
| 103 |
+
{%- if not last_was_text.value -%}
|
| 104 |
+
<|START_TEXT|>
|
| 105 |
+
{%- endif -%}
|
| 106 |
+
{{ content.text }}
|
| 107 |
+
{%- if loop.last -%}
|
| 108 |
+
<|END_TEXT|>
|
| 109 |
+
{%- endif %}
|
| 110 |
+
{%- set last_was_text.value = true -%}
|
| 111 |
+
{%- else -%}
|
| 112 |
+
{%- if last_was_text.value -%}
|
| 113 |
+
<|END_TEXT|>
|
| 114 |
+
{%- endif -%}
|
| 115 |
+
{%- set last_was_text.value = false -%}
|
| 116 |
+
{%- endif -%}
|
| 117 |
+
{%- if content.type|lower == "image" -%}
|
| 118 |
+
{%- if content.data -%}
|
| 119 |
+
{{ content.data }}
|
| 120 |
+
{%- else -%}
|
| 121 |
+
<|IMG_PATCH|>
|
| 122 |
+
{%- endif -%}
|
| 123 |
+
{%- endif -%}
|
| 124 |
+
{%- endfor %}
|
| 125 |
+
{%- endif %}
|
| 126 |
+
{%- endmacro %}
|
| 127 |
+
{%- macro print_thinking(msg) %}
|
| 128 |
+
{%- if msg.thinking -%}
|
| 129 |
+
{{ msg.thinking }}
|
| 130 |
+
{%- elif msg.content and msg.content[0].thinking -%}
|
| 131 |
+
{{ msg.content[0].thinking }}
|
| 132 |
+
{%- endif %}
|
| 133 |
+
{%- endmacro %}
|
| 134 |
+
{%- if messages and messages[0]['role']|lower == 'system' and not has_developer_instruction and convert_first_system_msg %}{%- set developer_instruction = messages[0] %}{%- set render_developer_instruction = true %}{%- set initial_instruction_message = true %}{% endif %}
|
| 135 |
+
{%- set json_object = true if response_format and response_format.type == "json_object" else false %}
|
| 136 |
+
{%- set json_schema = (response_format.json_schema or response_format.schema) if response_format %}
|
| 137 |
+
{%- set json_mode = json_object or json_schema %}
|
| 138 |
+
{%- set tool_idx = namespace(value=0) %}
|
| 139 |
+
{%- set tool_ids_seen = namespace(value=[]) %}
|
| 140 |
+
{%- set regen_tool_call_ids = regen_tool_call_ids | default(true) -%}
|
| 141 |
+
{%- set sent_documents = namespace(value=false) -%}
|
| 142 |
+
|
| 143 |
+
{%- if render_tools_section or render_platform_instruction_override or render_grounding or json_mode -%}
|
| 144 |
+
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TEXT|>
|
| 145 |
+
{%- elif not render_developer_instruction -%}
|
| 146 |
+
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>
|
| 147 |
+
{%- endif %}
|
| 148 |
+
|
| 149 |
+
{%- set rendered_platform_turn_chunk = false %}
|
| 150 |
+
|
| 151 |
+
{%- if render_platform_instruction_override -%}
|
| 152 |
+
{{ platform_instruction_override }}
|
| 153 |
+
{% set rendered_platform_turn_chunk = true %}
|
| 154 |
+
{%- else %}
|
| 155 |
+
{%- endif %}
|
| 156 |
+
|
| 157 |
+
{%- if render_grounding -%}
|
| 158 |
+
{%- if rendered_platform_turn_chunk %}
|
| 159 |
+
|
| 160 |
+
{% endif -%}
|
| 161 |
+
Note that both your responses and reflections can be grounded. Grounding means you associate pieces of texts (called "spans") with those specific tool results that support them (called "sources"). And you use a pair of tags "<co>" and "</co>" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as "{tool_call_id}:[{list of result indices}]", before they are joined together by ",". E.g., "<co>span</co: 0:[1,2],1:[0]>" means that "span" is supported by result 1 and 2 from "tool_call_id=0" as well as result 0 from "tool_call_id=1".
|
| 162 |
+
{% set rendered_platform_turn_chunk = true %}
|
| 163 |
+
{%- endif %}
|
| 164 |
+
|
| 165 |
+
{%- if render_tools_section %}
|
| 166 |
+
{%- if rendered_platform_turn_chunk %}
|
| 167 |
+
|
| 168 |
+
{% endif %}
|
| 169 |
+
# Available Tools
|
| 170 |
+
```json
|
| 171 |
+
[
|
| 172 |
+
{% if tools_or_docs_exist %}
|
| 173 |
+
{%- if documents %}
|
| 174 |
+
{"name": "direct-injected-document", "description": "This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!", "parameters": {"type": "object", "properties": {}, "required": []}, "responses": {"200": {"description": "Successfully returned a list of chunked text snippets from the directly uploaded documents.", "content": {"application/json": {"schema": {"type": "array", "items": {"type": "object", "required": ["url", "snippet"], "properties": {"url": {"type": "string", "description": "The url of the uploaded document."}, "snippet": {"type": "string", "description": "The text snippet for the returned document chunk."}}}}}}}}}
|
| 175 |
+
{%- if tools %},
|
| 176 |
+
{% else %}
|
| 177 |
+
|
| 178 |
+
{% endif %}
|
| 179 |
+
{%- endif %}
|
| 180 |
+
{%- for tool in tools %}
|
| 181 |
+
{"name": "{{ tool['function']['name'] }}", "description": "{{ tool['function']['description'] }}", "parameters": {{ tool['function']['parameters']|tojson }}, "responses": null}
|
| 182 |
+
{%- if not loop.last %},{% endif %}
|
| 183 |
+
|
| 184 |
+
{% endfor %}
|
| 185 |
+
{%- else %}
|
| 186 |
+
|
| 187 |
+
{% endif %}
|
| 188 |
+
]
|
| 189 |
+
```
|
| 190 |
+
{%- set rendered_platform_turn_chunk = true %}
|
| 191 |
+
{%- endif -%}
|
| 192 |
+
|
| 193 |
+
{%- if json_mode -%}
|
| 194 |
+
{%- if rendered_platform_turn_chunk %}
|
| 195 |
+
|
| 196 |
+
|
| 197 |
+
{% endif -%}
|
| 198 |
+
When generating JSON objects, do not generate block markers. Generate an object directly without prefixing with ```json. Return only the JSON and nothing else.
|
| 199 |
+
{%- if json_schema %}
|
| 200 |
+
|
| 201 |
+
Your output should adhere to the following json schema:
|
| 202 |
+
{{ json_schema }}
|
| 203 |
+
{%- endif -%}
|
| 204 |
+
{%- set rendered_platform_turn_chunk = true %}
|
| 205 |
+
{%- endif %}
|
| 206 |
+
{%- if rendered_platform_turn_chunk -%}
|
| 207 |
+
<|END_TEXT|><|END_OF_TURN_TOKEN|>
|
| 208 |
+
{%- elif not render_developer_instruction -%}
|
| 209 |
+
<|END_OF_TURN_TOKEN|>
|
| 210 |
+
{%- endif %}
|
| 211 |
+
{%- if render_developer_instruction -%}
|
| 212 |
+
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ print_msg(developer_instruction) }}<|END_OF_TURN_TOKEN|>
|
| 213 |
+
{%- endif %}
|
| 214 |
+
{%- for message in messages %}
|
| 215 |
+
{%- set msg_role_downcased = message.role | lower %}
|
| 216 |
+
{%- if msg_role_downcased == 'system' and (not (loop.first and initial_instruction_message)) -%}
|
| 217 |
+
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ print_msg(message) }}<|END_OF_TURN_TOKEN|>
|
| 218 |
+
{%- elif msg_role_downcased == 'user' -%}
|
| 219 |
+
<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ print_msg(message) }}<|END_OF_TURN_TOKEN|>
|
| 220 |
+
{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}
|
| 221 |
+
{%- elif msg_role_downcased == 'assistant' or msg_role_downcased == 'chatbot' -%}
|
| 222 |
+
<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
|
| 223 |
+
{%- if message.tool_calls %}
|
| 224 |
+
{% if not skip_thinking %}
|
| 225 |
+
{% if message.tool_plan -%}
|
| 226 |
+
<|START_THINKING|>{{ message.tool_plan }}<|END_THINKING|>
|
| 227 |
+
{%- elif message.thinking or (message.content and message.content[0].type == "thinking") -%}
|
| 228 |
+
<|START_THINKING|>{{ print_thinking(message) }}<|END_THINKING|>
|
| 229 |
+
{%- endif %}
|
| 230 |
+
{%- endif %}<|START_ACTION|>[
|
| 231 |
+
{%- for tc in message.tool_calls %}
|
| 232 |
+
|
| 233 |
+
{"tool_call_id": "{%- if regen_tool_call_ids -%}{{ tool_idx.value }}{%- else -%}{{ tc.id }}{%- endif -%}", "tool_name": "{{ tc['function']['name'] }}", "parameters": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}
|
| 234 |
+
{%- set tool_idx.value = tool_idx.value + 1 %}
|
| 235 |
+
{%- endfor %}
|
| 236 |
+
|
| 237 |
+
]<|END_ACTION|><|END_OF_TURN_TOKEN|>
|
| 238 |
+
{%- else -%}
|
| 239 |
+
{% if (message.thinking or (message.content and message.content[0].type == "thinking")) and not skip_thinking -%}
|
| 240 |
+
<|START_THINKING|>{{ print_thinking(message) }}<|END_THINKING|>
|
| 241 |
+
{%- endif -%}
|
| 242 |
+
{{ print_msg(message) }}<|END_OF_TURN_TOKEN|>
|
| 243 |
+
{%- endif %}
|
| 244 |
+
{%- elif msg_role_downcased == 'tool' and message.tool_call_id not in tool_ids_seen.value -%}
|
| 245 |
+
<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[
|
| 246 |
+
{{ format_tool_message(messages, message) }}
|
| 247 |
+
{%- for msg in messages[loop.index0 + 1:] %}
|
| 248 |
+
|
| 249 |
+
{%- if msg.role | lower == 'tool' %},
|
| 250 |
+
{{ format_tool_message(messages, msg) }}
|
| 251 |
+
{%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}
|
| 252 |
+
{%- else %}
|
| 253 |
+
{%- break %}
|
| 254 |
+
{%- endif %}
|
| 255 |
+
{%- endfor %}
|
| 256 |
+
|
| 257 |
+
]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>
|
| 258 |
+
{%- endif %}
|
| 259 |
+
{%- endfor %}{%- if add_generation_prompt -%}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if reasoning %}<|START_THINKING|>{% else %}<|START_THINKING|><|END_THINKING|>{% endif %}{%- endif %}
|
config.json
ADDED
|
@@ -0,0 +1,196 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"Cohere2MoeForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 2,
|
| 8 |
+
"dtype": "float16",
|
| 9 |
+
"eos_token_id": 255001,
|
| 10 |
+
"expert_selection_fn": "sigmoid",
|
| 11 |
+
"first_k_dense_replace": 1,
|
| 12 |
+
"head_dim": 128,
|
| 13 |
+
"hidden_act": "silu",
|
| 14 |
+
"hidden_size": 2048,
|
| 15 |
+
"initializer_range": 0.02,
|
| 16 |
+
"intermediate_size": 768,
|
| 17 |
+
"layer_norm_eps": 1e-05,
|
| 18 |
+
"layer_types": [
|
| 19 |
+
"full_attention",
|
| 20 |
+
"sliding_attention",
|
| 21 |
+
"sliding_attention",
|
| 22 |
+
"sliding_attention",
|
| 23 |
+
"full_attention",
|
| 24 |
+
"sliding_attention",
|
| 25 |
+
"sliding_attention",
|
| 26 |
+
"sliding_attention",
|
| 27 |
+
"full_attention",
|
| 28 |
+
"sliding_attention",
|
| 29 |
+
"sliding_attention",
|
| 30 |
+
"sliding_attention",
|
| 31 |
+
"full_attention",
|
| 32 |
+
"sliding_attention",
|
| 33 |
+
"sliding_attention",
|
| 34 |
+
"sliding_attention",
|
| 35 |
+
"full_attention",
|
| 36 |
+
"sliding_attention",
|
| 37 |
+
"sliding_attention",
|
| 38 |
+
"sliding_attention",
|
| 39 |
+
"full_attention",
|
| 40 |
+
"sliding_attention",
|
| 41 |
+
"sliding_attention",
|
| 42 |
+
"sliding_attention",
|
| 43 |
+
"full_attention",
|
| 44 |
+
"sliding_attention",
|
| 45 |
+
"sliding_attention",
|
| 46 |
+
"sliding_attention",
|
| 47 |
+
"full_attention",
|
| 48 |
+
"sliding_attention",
|
| 49 |
+
"sliding_attention",
|
| 50 |
+
"sliding_attention",
|
| 51 |
+
"full_attention",
|
| 52 |
+
"sliding_attention",
|
| 53 |
+
"sliding_attention",
|
| 54 |
+
"sliding_attention",
|
| 55 |
+
"full_attention",
|
| 56 |
+
"sliding_attention",
|
| 57 |
+
"sliding_attention",
|
| 58 |
+
"sliding_attention",
|
| 59 |
+
"full_attention",
|
| 60 |
+
"sliding_attention",
|
| 61 |
+
"sliding_attention",
|
| 62 |
+
"sliding_attention",
|
| 63 |
+
"full_attention",
|
| 64 |
+
"sliding_attention",
|
| 65 |
+
"sliding_attention",
|
| 66 |
+
"sliding_attention",
|
| 67 |
+
"full_attention"
|
| 68 |
+
],
|
| 69 |
+
"logit_scale": 1.0,
|
| 70 |
+
"max_position_embeddings": 500000,
|
| 71 |
+
"mlp_layer_types": [
|
| 72 |
+
"dense",
|
| 73 |
+
"sparse",
|
| 74 |
+
"sparse",
|
| 75 |
+
"sparse",
|
| 76 |
+
"sparse",
|
| 77 |
+
"sparse",
|
| 78 |
+
"sparse",
|
| 79 |
+
"sparse",
|
| 80 |
+
"sparse",
|
| 81 |
+
"sparse",
|
| 82 |
+
"sparse",
|
| 83 |
+
"sparse",
|
| 84 |
+
"sparse",
|
| 85 |
+
"sparse",
|
| 86 |
+
"sparse",
|
| 87 |
+
"sparse",
|
| 88 |
+
"sparse",
|
| 89 |
+
"sparse",
|
| 90 |
+
"sparse",
|
| 91 |
+
"sparse",
|
| 92 |
+
"sparse",
|
| 93 |
+
"sparse",
|
| 94 |
+
"sparse",
|
| 95 |
+
"sparse",
|
| 96 |
+
"sparse",
|
| 97 |
+
"sparse",
|
| 98 |
+
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|
| 99 |
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|
| 100 |
+
"sparse",
|
| 101 |
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"sparse",
|
| 102 |
+
"sparse",
|
| 103 |
+
"sparse",
|
| 104 |
+
"sparse",
|
| 105 |
+
"sparse",
|
| 106 |
+
"sparse",
|
| 107 |
+
"sparse",
|
| 108 |
+
"sparse",
|
| 109 |
+
"sparse",
|
| 110 |
+
"sparse",
|
| 111 |
+
"sparse",
|
| 112 |
+
"sparse",
|
| 113 |
+
"sparse",
|
| 114 |
+
"sparse",
|
| 115 |
+
"sparse",
|
| 116 |
+
"sparse",
|
| 117 |
+
"sparse",
|
| 118 |
+
"sparse",
|
| 119 |
+
"sparse",
|
| 120 |
+
"sparse"
|
| 121 |
+
],
|
| 122 |
+
"model_type": "cohere2_moe",
|
| 123 |
+
"norm_topk_prob": false,
|
| 124 |
+
"num_attention_heads": 32,
|
| 125 |
+
"num_experts": 128,
|
| 126 |
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"num_experts_per_tok": 8,
|
| 127 |
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"num_hidden_layers": 49,
|
| 128 |
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"num_key_value_heads": 4,
|
| 129 |
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"num_shared_experts": 0,
|
| 130 |
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"pad_token_id": 0,
|
| 131 |
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"prefix_dense_intermediate_size": 3072,
|
| 132 |
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"prefix_dense_sliding_window_pattern": 1,
|
| 133 |
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"quantization_config": {
|
| 134 |
+
"config_groups": {
|
| 135 |
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"group_0": {
|
| 136 |
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"format": "pack-quantized",
|
| 137 |
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"input_activations": null,
|
| 138 |
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"output_activations": null,
|
| 139 |
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"targets": [
|
| 140 |
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"Linear"
|
| 141 |
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],
|
| 142 |
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"weights": {
|
| 143 |
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"actorder": null,
|
| 144 |
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"block_structure": null,
|
| 145 |
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"dynamic": false,
|
| 146 |
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"group_size": 32,
|
| 147 |
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"num_bits": 4,
|
| 148 |
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"observer": "mse",
|
| 149 |
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"observer_kwargs": {},
|
| 150 |
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"scale_dtype": null,
|
| 151 |
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"strategy": "group",
|
| 152 |
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"symmetric": false,
|
| 153 |
+
"type": "int",
|
| 154 |
+
"zp_dtype": "torch.int8"
|
| 155 |
+
}
|
| 156 |
+
}
|
| 157 |
+
},
|
| 158 |
+
"format": "pack-quantized",
|
| 159 |
+
"global_compression_ratio": null,
|
| 160 |
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"ignore": [
|
| 161 |
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"model.layers.0.self_attn.q_proj",
|
| 162 |
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"model.layers.0.self_attn.k_proj",
|
| 163 |
+
"model.layers.0.self_attn.v_proj",
|
| 164 |
+
"model.layers.0.self_attn.o_proj",
|
| 165 |
+
"model.layers.0.mlp.gate_proj",
|
| 166 |
+
"model.layers.0.mlp.up_proj",
|
| 167 |
+
"model.layers.0.mlp.down_proj",
|
| 168 |
+
"lm_head",
|
| 169 |
+
"re:.*mlp[.]gate"
|
| 170 |
+
],
|
| 171 |
+
"kv_cache_scheme": null,
|
| 172 |
+
"quant_method": "compressed-tensors",
|
| 173 |
+
"quantization_status": "compressed",
|
| 174 |
+
"sparsity_config": {},
|
| 175 |
+
"transform_config": {},
|
| 176 |
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"version": "0.1.dev489+g6d69620"
|
| 177 |
+
},
|
| 178 |
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"rms_norm_eps": 1e-06,
|
| 179 |
+
"rope_parameters": {
|
| 180 |
+
"rope_theta": 50000,
|
| 181 |
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"rope_type": "default"
|
| 182 |
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},
|
| 183 |
+
"rope_scaling": null,
|
| 184 |
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"rope_theta": 50000,
|
| 185 |
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"shared_expert_combination_strategy": "average",
|
| 186 |
+
"sliding_window": 4096,
|
| 187 |
+
"sliding_window_pattern": 4,
|
| 188 |
+
"tie_word_embeddings": true,
|
| 189 |
+
"transformers_version": "5.10.2",
|
| 190 |
+
"use_cache": true,
|
| 191 |
+
"use_gated_activation": true,
|
| 192 |
+
"use_parallel_block": true,
|
| 193 |
+
"use_parallel_embedding": false,
|
| 194 |
+
"use_qk_norm": false,
|
| 195 |
+
"vocab_size": 262144
|
| 196 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,7 @@
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| 1 |
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{
|
| 2 |
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"_from_model_config": true,
|
| 3 |
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"bos_token_id": 2,
|
| 4 |
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"eos_token_id": 255001,
|
| 5 |
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"pad_token_id": 0,
|
| 6 |
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"transformers_version": "5.8.0"
|
| 7 |
+
}
|
model-00001-of-00004.safetensors
ADDED
|
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version https://git-lfs.github.com/spec/v1
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size 5370247488
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model-00002-of-00004.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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size 5371301808
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model-00003-of-00004.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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model-00004-of-00004.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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model.safetensors.index.json
ADDED
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The diff for this file is too large to render.
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|
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,30 @@
|
|
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| 1 |
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{
|
| 2 |
+
"bos_token": {
|
| 3 |
+
"content": "<BOS_TOKEN>",
|
| 4 |
+
"lstrip": false,
|
| 5 |
+
"normalized": false,
|
| 6 |
+
"rstrip": false,
|
| 7 |
+
"single_word": false
|
| 8 |
+
},
|
| 9 |
+
"eos_token": {
|
| 10 |
+
"content": "<|END_OF_TURN_TOKEN|>",
|
| 11 |
+
"lstrip": false,
|
| 12 |
+
"normalized": false,
|
| 13 |
+
"rstrip": false,
|
| 14 |
+
"single_word": false
|
| 15 |
+
},
|
| 16 |
+
"pad_token": {
|
| 17 |
+
"content": "<PAD>",
|
| 18 |
+
"lstrip": false,
|
| 19 |
+
"normalized": false,
|
| 20 |
+
"rstrip": false,
|
| 21 |
+
"single_word": false
|
| 22 |
+
},
|
| 23 |
+
"unk_token": {
|
| 24 |
+
"content": "<UNK>",
|
| 25 |
+
"lstrip": false,
|
| 26 |
+
"normalized": false,
|
| 27 |
+
"rstrip": false,
|
| 28 |
+
"single_word": false
|
| 29 |
+
}
|
| 30 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 3 |
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size 28217141
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,328 @@
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|
|
| 1 |
+
{
|
| 2 |
+
"add_bos_token": true,
|
| 3 |
+
"add_eos_token": false,
|
| 4 |
+
"add_prefix_space": false,
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"legacy": true,
|
| 7 |
+
"spaces_between_special_tokens": false,
|
| 8 |
+
"use_default_system_prompt": false,
|
| 9 |
+
"bos_token": "<BOS_TOKEN>",
|
| 10 |
+
"eos_token": "<|END_OF_TURN_TOKEN|>",
|
| 11 |
+
"pad_token": "<PAD>",
|
| 12 |
+
"unk_token": "<UNK>",
|
| 13 |
+
"tokenizer_class": "TokenizersBackend",
|
| 14 |
+
"model_max_length": 1000000000000000019884624838656,
|
| 15 |
+
"sp_model_kwargs": {},
|
| 16 |
+
"chat_template": [
|
| 17 |
+
{
|
| 18 |
+
"name": "default",
|
| 19 |
+
"template": "{{ bos_token }}{% if documents %}\n{% set tools = [] %}\n{%- macro document_turn(documents) -%}\n{# format documents into chat turn #}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[\n {\"tool_call_id\": \"0\", \"tool_name\": \"direct-injected-document\", \"parameters\": {}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n {\n \"tool_call_id\": \"0\",\n \"results\": {\n{% for doc in documents %}\n \"{{ loop.index0 }}\": {{doc|tojson}}{% if not loop.last %},\n {% endif %}\n{% endfor %}\n\n },\n \"is_error\": null\n }\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}\n{%- macro tool_call_id_to_int(messages, tool_call_id) %}\n{%- set counter = namespace(value=0) %}\n{%- set tool_call_id_seen = namespace(value=false) %}\n{%- for msg in messages %}\n {%- if msg.tool_calls %}\n {%- for tool_call in msg.tool_calls %}\n {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}\n {{ counter.value }}\n {%- set tool_call_id_seen.value = true %}\n {%- endif %}\n {%- set counter.value = counter.value + 1 %}\n {%- endfor %}\n {%- endif %}\n{%- endfor %}\n{%- endmacro %}\n{%- macro format_tool_message(messages, tool_msg) -%}\n{# format tool message #}\n {\n \"tool_call_id\": \"{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}\",\n \"results\": {\n \"0\": {{ tool_msg.content|tojson }}\n },\n \"is_error\": null\n }\n{%- endmacro -%}\n{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}\n{%- set tool_idx = namespace(value=0) %}\n{%- set tool_ids_seen = namespace(value=[]) %}\n{%- set sent_documents = namespace(value=false) %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble\nYou are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.\n\nYour information cutoff date is June 2024.\n\nYou have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.\n{% if tools or documents %}\n\nYou have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests.\n\n## Tool Use\nThink about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first.\n\n0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>.\n You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed.\n NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools.\n\nThen carry out your plan by repeatedly executing the following steps.\n1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing \"tool_name\" and \"parameters\" fields.\n When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>.\n2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results.\n Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its \"tool_call_id\".\n3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>.\n You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded.\n NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user.\n\nYou can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user.\n\n4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>.\n{% if enable_citations %}\n\n## Grounding\nImportantly, note that \"Reflection\" and \"Response\" above can be grounded.\nGrounding means you associate pieces of texts (called \"spans\") with those specific tool results that support them (called \"sources\"). And you use a pair of tags \"<co>\" and \"</co>\" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as \"{tool_call_id}:[{list of result indices}]\", before they are joined together by \",\". E.g., \"<co>span</co: 0:[1,2],1:[0]>\" means that \"span\" is supported by result 1 and 2 from \"tool_call_id=0\" as well as result 0 from \"tool_call_id=1\".\n{% endif %}\n\n## Available Tools\nHere is the list of tools that you have available to you.\nYou can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it.\nEach tool is represented as a JSON object with fields like \"name\", \"description\", \"parameters\" (per JSON Schema), and optionally, \"responses\" (per JSON Schema).\n\n```json\n[\n{% if documents %}\n {\"name\": \"direct-injected-document\", \"description\": \"This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}, \"responses\": {\"200\": {\"description\": \"Successfully returned a list of chunked text snippets from the directly uploaded documents.\", \"content\": {\"application/json\": {\"schema\": {\"type\": \"array\", \"items\": {\"type\": \"object\", \"required\": [\"url\", \"snippet\"], \"properties\": {\"url\": {\"type\": \"string\", \"description\": \"The url of the uploaded document.\"}, \"snippet\": {\"type\": \"string\", \"description\": \"The text snippet for the returned document chunk.\"}}}}}}}}}{%- if tools %},{% endif %}\n\n{% endif %}\n{% for tool in tools %}\n {\"name\": \"{{ tool['function']['name'] }}\", \"description\": \"{{tool['function']['description']}}\", \"parameters\": {{ tool['function']['parameters']|tojson }}, \"responses\": null}{%- if not loop.last %},{% endif %}\n\n{% endfor %}\n]\n```\n\n{% endif %}\n# Default Preamble\nThe following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.\n- Your name is Command.\n- You are a large language model built by Cohere.\n- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.\n- If the input is ambiguous, ask clarifying follow-up questions.\n- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).\n- Use LaTeX to generate mathematical notation for complex equations.\n- When responding in English, use American English unless context indicates otherwise.\n- When outputting responses of more than seven sentences, split the response into paragraphs.\n- Prefer the active voice.\n- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.\n- Use gender-neutral pronouns for unspecified persons.\n- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.\n- Use the third person when asked to write a summary.\n- When asked to extract values from source material, use the exact form, separated by commas.\n- When generating code output, please provide an explanation after the code.\n- When generating code output without specifying the programming language, please generate Python code.\n- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.\n{%- if developer_preamble %}\n\n\n# Developer Preamble\nThe following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.\n{{ developer_preamble }}\n{%- endif -%}\n<|END_OF_TURN_TOKEN|>\n{%- for message in messages %}\n {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>\n {%- elif message.role|lower == 'user' %}\n<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}\n {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[\n {% for tc in message.tool_calls %}\n {\"tool_call_id\": \"{{ tool_idx.value }}\", \"tool_name\": \"{{ tc['function']['name'] }}\", \"parameters\": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}\n\n {% set tool_idx.value = tool_idx.value + 1 %}\n {% endfor %}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %}\n {% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n{{ format_tool_message(messages, message) }}\n {%- for msg in messages[loop.index0 + 1:] %}\n {%- if msg.role|lower == 'tool' %},\n{{ format_tool_message(messages, msg) }}\n {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}\n {%- else %}\n {%- break %}\n {%- endif %}\n {%- endfor %}\n\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>\n {%- endif %}\n{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>\n{%- else -%}\n{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble\n{% if safety_mode|upper == 'STRICT' -%}\nYou are in strict safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will reject requests to generate content related to violence, hate, misinformation or sex to any amount. You will avoid using profanity. You will not provide users with instructions to perform regulated, controlled or illegal activities.\n{%- else -%}\nYou are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.\n{%- endif %}\n\n\nYour information cutoff date is June 2024.\n\nYou have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.\n\n# Default Preamble\nThe following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.\n- Your name is Command.\n- You are a large language model built by Cohere.\n- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.\n- If the input is ambiguous, ask clarifying follow-up questions.\n- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).\n- Use LaTeX to generate mathematical notation for complex equations.\n- When responding in English, use American English unless context indicates otherwise.\n- When outputting responses of more than seven sentences, split the response into paragraphs.\n- Prefer the active voice.\n- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.\n- Use gender-neutral pronouns for unspecified persons.\n- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.\n- Use the third person when asked to write a summary.\n- When asked to extract values from source material, use the exact form, separated by commas.\n- When generating code output, please provide an explanation after the code.\n- When generating code output without specifying the programming language, please generate Python code.\n- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.\n{%- if developer_preamble %}\n\n\n# Developer Preamble\nThe following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.\n{{ developer_preamble }}\n{%- endif -%}\n<|END_OF_TURN_TOKEN|>\n{%- for message in messages %}\n {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>\n {%- elif message.role|lower == 'user' %}\n<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>\n {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>\n {%- endif %}\n{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{%- if add_generation_prompt -%}<|START_RESPONSE|>{%- endif %}\n{% endif %}"
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| 20 |
+
},
|
| 21 |
+
{
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| 22 |
+
"name": "tool_use",
|
| 23 |
+
"template": "{{ bos_token }}{%- macro document_turn(documents) -%}\n{# format documents into chat turn #}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[\n {\"tool_call_id\": \"0\", \"tool_name\": \"direct-injected-document\", \"parameters\": {}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n {\n \"tool_call_id\": \"0\",\n \"results\": {\n{% for doc in documents %}\n \"{{ loop.index0 }}\": {{doc|tojson}}{% if not loop.last %},\n {% endif %}\n{% endfor %}\n\n },\n \"is_error\": null\n }\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}\n{%- macro tool_call_id_to_int(messages, tool_call_id) %}\n{%- set counter = namespace(value=0) %}\n{%- set tool_call_id_seen = namespace(value=false) %}\n{%- for msg in messages %}\n {%- if msg.tool_calls %}\n {%- for tool_call in msg.tool_calls %}\n {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}\n {{ counter.value }}\n {%- set tool_call_id_seen.value = true %}\n {%- endif %}\n {%- set counter.value = counter.value + 1 %}\n {%- endfor %}\n {%- endif %}\n{%- endfor %}\n{%- endmacro %}\n{%- macro format_tool_message(messages, tool_msg) -%}\n{# format tool message #}\n {\n \"tool_call_id\": \"{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}\",\n \"results\": {\n \"0\": {{ tool_msg.content|tojson }}\n },\n \"is_error\": null\n }\n{%- endmacro -%}\n{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}\n{%- set tool_idx = namespace(value=0) %}\n{%- set tool_ids_seen = namespace(value=[]) %}\n{%- set sent_documents = namespace(value=false) %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble\nYou are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.\n\nYour information cutoff date is June 2024.\n\nYou have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.\n{% if tools or documents %}\n\nYou have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests.\n\n## Tool Use\nThink about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first.\n\n0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>.\n You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed.\n NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools.\n\nThen carry out your plan by repeatedly executing the following steps.\n1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing \"tool_name\" and \"parameters\" fields.\n When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>.\n2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results.\n Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its \"tool_call_id\".\n3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>.\n You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded.\n NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user.\n\nYou can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user.\n\n4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>.\n{% if enable_citations %}\n\n## Grounding\nImportantly, note that \"Reflection\" and \"Response\" above can be grounded.\nGrounding means you associate pieces of texts (called \"spans\") with those specific tool results that support them (called \"sources\"). And you use a pair of tags \"<co>\" and \"</co>\" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as \"{tool_call_id}:[{list of result indices}]\", before they are joined together by \",\". E.g., \"<co>span</co: 0:[1,2],1:[0]>\" means that \"span\" is supported by result 1 and 2 from \"tool_call_id=0\" as well as result 0 from \"tool_call_id=1\".\n{% endif %}\n\n## Available Tools\nHere is the list of tools that you have available to you.\nYou can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it.\nEach tool is represented as a JSON object with fields like \"name\", \"description\", \"parameters\" (per JSON Schema), and optionally, \"responses\" (per JSON Schema).\n\n```json\n[\n{% if documents %}\n {\"name\": \"direct-injected-document\", \"description\": \"This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}, \"responses\": {\"200\": {\"description\": \"Successfully returned a list of chunked text snippets from the directly uploaded documents.\", \"content\": {\"application/json\": {\"schema\": {\"type\": \"array\", \"items\": {\"type\": \"object\", \"required\": [\"url\", \"snippet\"], \"properties\": {\"url\": {\"type\": \"string\", \"description\": \"The url of the uploaded document.\"}, \"snippet\": {\"type\": \"string\", \"description\": \"The text snippet for the returned document chunk.\"}}}}}}}}}{%- if tools %},{% endif %}\n\n{% endif %}\n{% for tool in tools %}\n {\"name\": \"{{ tool['function']['name'] }}\", \"description\": \"{{tool['function']['description']}}\", \"parameters\": {{ tool['function']['parameters']|tojson }}, \"responses\": null}{%- if not loop.last %},{% endif %}\n\n{% endfor %}\n]\n```\n\n{% endif %}\n# Default Preamble\nThe following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.\n- Your name is Command.\n- You are a large language model built by Cohere.\n- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.\n- If the input is ambiguous, ask clarifying follow-up questions.\n- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).\n- Use LaTeX to generate mathematical notation for complex equations.\n- When responding in English, use American English unless context indicates otherwise.\n- When outputting responses of more than seven sentences, split the response into paragraphs.\n- Prefer the active voice.\n- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.\n- Use gender-neutral pronouns for unspecified persons.\n- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.\n- Use the third person when asked to write a summary.\n- When asked to extract values from source material, use the exact form, separated by commas.\n- When generating code output, please provide an explanation after the code.\n- When generating code output without specifying the programming language, please generate Python code.\n- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.\n{%- if developer_preamble %}\n\n\n# Developer Preamble\nThe following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.\n{{ developer_preamble }}\n{%- endif -%}\n<|END_OF_TURN_TOKEN|>\n{%- for message in messages %}\n {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>\n {%- elif message.role|lower == 'user' %}\n<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}\n {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[\n {% for tc in message.tool_calls %}\n {\"tool_call_id\": \"{{ tool_idx.value }}\", \"tool_name\": \"{{ tc['function']['name'] }}\", \"parameters\": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}\n\n {% set tool_idx.value = tool_idx.value + 1 %}\n {% endfor %}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %}\n {% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n{{ format_tool_message(messages, message) }}\n {%- for msg in messages[loop.index0 + 1:] %}\n {%- if msg.role|lower == 'tool' %},\n{{ format_tool_message(messages, msg) }}\n {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}\n {%- else %}\n {%- break %}\n {%- endif %}\n {%- endfor %}\n\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>\n {%- endif %}\n{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
|
| 24 |
+
},
|
| 25 |
+
{
|
| 26 |
+
"name": "rag",
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| 27 |
+
"template": "{{ bos_token }}{% set tools = [] %}\n{%- macro document_turn(documents) -%}\n{# format documents into chat turn #}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[\n {\"tool_call_id\": \"0\", \"tool_name\": \"direct-injected-document\", \"parameters\": {}}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n {\n \"tool_call_id\": \"0\",\n \"results\": {\n{% for doc in documents %}\n \"{{ loop.index0 }}\": {{doc|tojson}}{% if not loop.last %},\n {% endif %}\n{% endfor %}\n\n },\n \"is_error\": null\n }\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %}\n{%- macro tool_call_id_to_int(messages, tool_call_id) %}\n{%- set counter = namespace(value=0) %}\n{%- set tool_call_id_seen = namespace(value=false) %}\n{%- for msg in messages %}\n {%- if msg.tool_calls %}\n {%- for tool_call in msg.tool_calls %}\n {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%}\n {{ counter.value }}\n {%- set tool_call_id_seen.value = true %}\n {%- endif %}\n {%- set counter.value = counter.value + 1 %}\n {%- endfor %}\n {%- endif %}\n{%- endfor %}\n{%- endmacro %}\n{%- macro format_tool_message(messages, tool_msg) -%}\n{# format tool message #}\n {\n \"tool_call_id\": \"{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}\",\n \"results\": {\n \"0\": {{ tool_msg.content|tojson }}\n },\n \"is_error\": null\n }\n{%- endmacro -%}\n{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %}\n{%- set tool_idx = namespace(value=0) %}\n{%- set tool_ids_seen = namespace(value=[]) %}\n{%- set sent_documents = namespace(value=false) %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble\nYou are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes.\n\nYour information cutoff date is June 2024.\n\nYou have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages.\n{% if tools or documents %}\n\nYou have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests.\n\n## Tool Use\nThink about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first.\n\n0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>.\n You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed.\n NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools.\n\nThen carry out your plan by repeatedly executing the following steps.\n1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing \"tool_name\" and \"parameters\" fields.\n When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>.\n2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results.\n Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its \"tool_call_id\".\n3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>.\n You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded.\n NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user.\n\nYou can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user.\n\n4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>.\n{% if enable_citations %}\n\n## Grounding\nImportantly, note that \"Reflection\" and \"Response\" above can be grounded.\nGrounding means you associate pieces of texts (called \"spans\") with those specific tool results that support them (called \"sources\"). And you use a pair of tags \"<co>\" and \"</co>\" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as \"{tool_call_id}:[{list of result indices}]\", before they are joined together by \",\". E.g., \"<co>span</co: 0:[1,2],1:[0]>\" means that \"span\" is supported by result 1 and 2 from \"tool_call_id=0\" as well as result 0 from \"tool_call_id=1\".\n{% endif %}\n\n## Available Tools\nHere is the list of tools that you have available to you.\nYou can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it.\nEach tool is represented as a JSON object with fields like \"name\", \"description\", \"parameters\" (per JSON Schema), and optionally, \"responses\" (per JSON Schema).\n\n```json\n[\n{% if documents %}\n {\"name\": \"direct-injected-document\", \"description\": \"This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!\", \"parameters\": {\"type\": \"object\", \"properties\": {}, \"required\": []}, \"responses\": {\"200\": {\"description\": \"Successfully returned a list of chunked text snippets from the directly uploaded documents.\", \"content\": {\"application/json\": {\"schema\": {\"type\": \"array\", \"items\": {\"type\": \"object\", \"required\": [\"url\", \"snippet\"], \"properties\": {\"url\": {\"type\": \"string\", \"description\": \"The url of the uploaded document.\"}, \"snippet\": {\"type\": \"string\", \"description\": \"The text snippet for the returned document chunk.\"}}}}}}}}}{%- if tools %},{% endif %}\n\n{% endif %}\n{% for tool in tools %}\n {\"name\": \"{{ tool['function']['name'] }}\", \"description\": \"{{tool['function']['description']}}\", \"parameters\": {{ tool['function']['parameters']|tojson }}, \"responses\": null}{%- if not loop.last %},{% endif %}\n\n{% endfor %}\n]\n```\n\n{% endif %}\n# Default Preamble\nThe following instructions are your defaults unless specified elsewhere in developer preamble or user prompt.\n- Your name is Command.\n- You are a large language model built by Cohere.\n- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions.\n- If the input is ambiguous, ask clarifying follow-up questions.\n- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks).\n- Use LaTeX to generate mathematical notation for complex equations.\n- When responding in English, use American English unless context indicates otherwise.\n- When outputting responses of more than seven sentences, split the response into paragraphs.\n- Prefer the active voice.\n- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references.\n- Use gender-neutral pronouns for unspecified persons.\n- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list.\n- Use the third person when asked to write a summary.\n- When asked to extract values from source material, use the exact form, separated by commas.\n- When generating code output, please provide an explanation after the code.\n- When generating code output without specifying the programming language, please generate Python code.\n- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer.\n{%- if developer_preamble %}\n\n\n# Developer Preamble\nThe following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions.\n{{ developer_preamble }}\n{%- endif -%}\n<|END_OF_TURN_TOKEN|>\n{%- for message in messages %}\n {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>\n {%- elif message.role|lower == 'user' %}\n<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %}\n {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %}\n<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.tool_plan}}<|END_THINKING|><|START_ACTION|>[\n {% for tc in message.tool_calls %}\n {\"tool_call_id\": \"{{ tool_idx.value }}\", \"tool_name\": \"{{ tc['function']['name'] }}\", \"parameters\": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %}\n\n {% set tool_idx.value = tool_idx.value + 1 %}\n {% endfor %}\n]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %}\n {% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %}\n<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[\n{{ format_tool_message(messages, message) }}\n {%- for msg in messages[loop.index0 + 1:] %}\n {%- if msg.role|lower == 'tool' %},\n{{ format_tool_message(messages, msg) }}\n {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %}\n {%- else %}\n {%- break %}\n {%- endif %}\n {%- endfor %}\n\n]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>\n {%- endif %}\n{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
|
| 28 |
+
}
|
| 29 |
+
],
|
| 30 |
+
"added_tokens_decoder": {
|
| 31 |
+
"0": {
|
| 32 |
+
"content": "<PAD>",
|
| 33 |
+
"lstrip": false,
|
| 34 |
+
"normalized": false,
|
| 35 |
+
"rstrip": false,
|
| 36 |
+
"single_word": false,
|
| 37 |
+
"special": true
|
| 38 |
+
},
|
| 39 |
+
"1": {
|
| 40 |
+
"content": "<MASK_TOKEN>",
|
| 41 |
+
"lstrip": false,
|
| 42 |
+
"normalized": false,
|
| 43 |
+
"rstrip": false,
|
| 44 |
+
"single_word": false,
|
| 45 |
+
"special": true
|
| 46 |
+
},
|
| 47 |
+
"2": {
|
| 48 |
+
"content": "<BOS_TOKEN>",
|
| 49 |
+
"lstrip": false,
|
| 50 |
+
"normalized": false,
|
| 51 |
+
"rstrip": false,
|
| 52 |
+
"single_word": false,
|
| 53 |
+
"special": true
|
| 54 |
+
},
|
| 55 |
+
"3": {
|
| 56 |
+
"content": "<EOS_TOKEN>",
|
| 57 |
+
"lstrip": false,
|
| 58 |
+
"normalized": false,
|
| 59 |
+
"rstrip": false,
|
| 60 |
+
"single_word": false,
|
| 61 |
+
"special": true
|
| 62 |
+
},
|
| 63 |
+
"4": {
|
| 64 |
+
"content": "<UNK>",
|
| 65 |
+
"lstrip": false,
|
| 66 |
+
"normalized": false,
|
| 67 |
+
"rstrip": false,
|
| 68 |
+
"single_word": false,
|
| 69 |
+
"special": true
|
| 70 |
+
},
|
| 71 |
+
"255000": {
|
| 72 |
+
"content": "<|START_OF_TURN_TOKEN|>",
|
| 73 |
+
"lstrip": false,
|
| 74 |
+
"normalized": false,
|
| 75 |
+
"rstrip": false,
|
| 76 |
+
"single_word": false,
|
| 77 |
+
"special": false
|
| 78 |
+
},
|
| 79 |
+
"255001": {
|
| 80 |
+
"content": "<|END_OF_TURN_TOKEN|>",
|
| 81 |
+
"lstrip": false,
|
| 82 |
+
"normalized": false,
|
| 83 |
+
"rstrip": false,
|
| 84 |
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"single_word": false,
|
| 85 |
+
"special": false
|
| 86 |
+
},
|
| 87 |
+
"255002": {
|
| 88 |
+
"content": "<|USER_TOKEN|>",
|
| 89 |
+
"lstrip": false,
|
| 90 |
+
"normalized": false,
|
| 91 |
+
"rstrip": false,
|
| 92 |
+
"single_word": false,
|
| 93 |
+
"special": false
|
| 94 |
+
},
|
| 95 |
+
"255003": {
|
| 96 |
+
"content": "<|CHATBOT_TOKEN|>",
|
| 97 |
+
"lstrip": false,
|
| 98 |
+
"normalized": false,
|
| 99 |
+
"rstrip": false,
|
| 100 |
+
"single_word": false,
|
| 101 |
+
"special": false
|
| 102 |
+
},
|
| 103 |
+
"255004": {
|
| 104 |
+
"content": "<|SYSTEM_TOKEN|>",
|
| 105 |
+
"lstrip": false,
|
| 106 |
+
"normalized": false,
|
| 107 |
+
"rstrip": false,
|
| 108 |
+
"single_word": false,
|
| 109 |
+
"special": false
|
| 110 |
+
},
|
| 111 |
+
"255005": {
|
| 112 |
+
"content": "<|NEW_FILE|>",
|
| 113 |
+
"lstrip": false,
|
| 114 |
+
"normalized": false,
|
| 115 |
+
"rstrip": false,
|
| 116 |
+
"single_word": false,
|
| 117 |
+
"special": true
|
| 118 |
+
},
|
| 119 |
+
"255006": {
|
| 120 |
+
"content": "<|BEGINNING_OF_PREFIX_FIM_TOKEN|>",
|
| 121 |
+
"lstrip": false,
|
| 122 |
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"normalized": false,
|
| 123 |
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|
| 124 |
+
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|
| 125 |
+
"special": true
|
| 126 |
+
},
|
| 127 |
+
"255007": {
|
| 128 |
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"content": "<|BEGINNING_OF_MIDDLE_FIM_TOKEN|>",
|
| 129 |
+
"lstrip": false,
|
| 130 |
+
"normalized": false,
|
| 131 |
+
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|
| 132 |
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"single_word": false,
|
| 133 |
+
"special": true
|
| 134 |
+
},
|
| 135 |
+
"255008": {
|
| 136 |
+
"content": "<|BEGINNING_OF_SUFFIX_FIM_TOKEN|>",
|
| 137 |
+
"lstrip": false,
|
| 138 |
+
"normalized": false,
|
| 139 |
+
"rstrip": false,
|
| 140 |
+
"single_word": false,
|
| 141 |
+
"special": true
|
| 142 |
+
},
|
| 143 |
+
"255009": {
|
| 144 |
+
"content": "<|END_OF_MIDDLE_FIM_TOKEN|>",
|
| 145 |
+
"lstrip": false,
|
| 146 |
+
"normalized": false,
|
| 147 |
+
"rstrip": false,
|
| 148 |
+
"single_word": false,
|
| 149 |
+
"special": true
|
| 150 |
+
},
|
| 151 |
+
"255010": {
|
| 152 |
+
"content": "<|START_THINKING|>",
|
| 153 |
+
"lstrip": false,
|
| 154 |
+
"normalized": false,
|
| 155 |
+
"rstrip": false,
|
| 156 |
+
"single_word": false,
|
| 157 |
+
"special": false
|
| 158 |
+
},
|
| 159 |
+
"255011": {
|
| 160 |
+
"content": "<|END_THINKING|>",
|
| 161 |
+
"lstrip": false,
|
| 162 |
+
"normalized": false,
|
| 163 |
+
"rstrip": false,
|
| 164 |
+
"single_word": false,
|
| 165 |
+
"special": false
|
| 166 |
+
},
|
| 167 |
+
"255012": {
|
| 168 |
+
"content": "<|START_TEXT|>",
|
| 169 |
+
"lstrip": false,
|
| 170 |
+
"normalized": false,
|
| 171 |
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|
| 172 |
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|
| 173 |
+
"special": false
|
| 174 |
+
},
|
| 175 |
+
"255013": {
|
| 176 |
+
"content": "<|END_TEXT|>",
|
| 177 |
+
"lstrip": false,
|
| 178 |
+
"normalized": false,
|
| 179 |
+
"rstrip": false,
|
| 180 |
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|
| 181 |
+
"special": false
|
| 182 |
+
},
|
| 183 |
+
"255014": {
|
| 184 |
+
"content": "<|START_ACTION|>",
|
| 185 |
+
"lstrip": false,
|
| 186 |
+
"normalized": false,
|
| 187 |
+
"rstrip": false,
|
| 188 |
+
"single_word": false,
|
| 189 |
+
"special": false
|
| 190 |
+
},
|
| 191 |
+
"255015": {
|
| 192 |
+
"content": "<|END_ACTION|>",
|
| 193 |
+
"lstrip": false,
|
| 194 |
+
"normalized": false,
|
| 195 |
+
"rstrip": false,
|
| 196 |
+
"single_word": false,
|
| 197 |
+
"special": false
|
| 198 |
+
},
|
| 199 |
+
"255016": {
|
| 200 |
+
"content": "<|START_TOOL_RESULT|>",
|
| 201 |
+
"lstrip": false,
|
| 202 |
+
"normalized": false,
|
| 203 |
+
"rstrip": false,
|
| 204 |
+
"single_word": false,
|
| 205 |
+
"special": false
|
| 206 |
+
},
|
| 207 |
+
"255017": {
|
| 208 |
+
"content": "<|END_TOOL_RESULT|>",
|
| 209 |
+
"lstrip": false,
|
| 210 |
+
"normalized": false,
|
| 211 |
+
"rstrip": false,
|
| 212 |
+
"single_word": false,
|
| 213 |
+
"special": false
|
| 214 |
+
},
|
| 215 |
+
"255018": {
|
| 216 |
+
"content": "<|USER_0_TOKEN|>",
|
| 217 |
+
"lstrip": false,
|
| 218 |
+
"normalized": false,
|
| 219 |
+
"rstrip": false,
|
| 220 |
+
"single_word": false,
|
| 221 |
+
"special": false
|
| 222 |
+
},
|
| 223 |
+
"255019": {
|
| 224 |
+
"content": "<|USER_1_TOKEN|>",
|
| 225 |
+
"lstrip": false,
|
| 226 |
+
"normalized": false,
|
| 227 |
+
"rstrip": false,
|
| 228 |
+
"single_word": false,
|
| 229 |
+
"special": false
|
| 230 |
+
},
|
| 231 |
+
"255020": {
|
| 232 |
+
"content": "<|USER_2_TOKEN|>",
|
| 233 |
+
"lstrip": false,
|
| 234 |
+
"normalized": false,
|
| 235 |
+
"rstrip": false,
|
| 236 |
+
"single_word": false,
|
| 237 |
+
"special": false
|
| 238 |
+
},
|
| 239 |
+
"255021": {
|
| 240 |
+
"content": "<|USER_3_TOKEN|>",
|
| 241 |
+
"lstrip": false,
|
| 242 |
+
"normalized": false,
|
| 243 |
+
"rstrip": false,
|
| 244 |
+
"single_word": false,
|
| 245 |
+
"special": false
|
| 246 |
+
},
|
| 247 |
+
"255022": {
|
| 248 |
+
"content": "<|USER_4_TOKEN|>",
|
| 249 |
+
"lstrip": false,
|
| 250 |
+
"normalized": false,
|
| 251 |
+
"rstrip": false,
|
| 252 |
+
"single_word": false,
|
| 253 |
+
"special": false
|
| 254 |
+
},
|
| 255 |
+
"255023": {
|
| 256 |
+
"content": "<|USER_5_TOKEN|>",
|
| 257 |
+
"lstrip": false,
|
| 258 |
+
"normalized": false,
|
| 259 |
+
"rstrip": false,
|
| 260 |
+
"single_word": false,
|
| 261 |
+
"special": false
|
| 262 |
+
},
|
| 263 |
+
"255024": {
|
| 264 |
+
"content": "<|USER_6_TOKEN|>",
|
| 265 |
+
"lstrip": false,
|
| 266 |
+
"normalized": false,
|
| 267 |
+
"rstrip": false,
|
| 268 |
+
"single_word": false,
|
| 269 |
+
"special": false
|
| 270 |
+
},
|
| 271 |
+
"255025": {
|
| 272 |
+
"content": "<|USER_7_TOKEN|>",
|
| 273 |
+
"lstrip": false,
|
| 274 |
+
"normalized": false,
|
| 275 |
+
"rstrip": false,
|
| 276 |
+
"single_word": false,
|
| 277 |
+
"special": false
|
| 278 |
+
},
|
| 279 |
+
"255026": {
|
| 280 |
+
"content": "<|USER_8_TOKEN|>",
|
| 281 |
+
"lstrip": false,
|
| 282 |
+
"normalized": false,
|
| 283 |
+
"rstrip": false,
|
| 284 |
+
"single_word": false,
|
| 285 |
+
"special": false
|
| 286 |
+
},
|
| 287 |
+
"255027": {
|
| 288 |
+
"content": "<|USER_9_TOKEN|>",
|
| 289 |
+
"lstrip": false,
|
| 290 |
+
"normalized": false,
|
| 291 |
+
"rstrip": false,
|
| 292 |
+
"single_word": false,
|
| 293 |
+
"special": false
|
| 294 |
+
},
|
| 295 |
+
"255028": {
|
| 296 |
+
"content": "<|START_OF_IMG|>",
|
| 297 |
+
"lstrip": false,
|
| 298 |
+
"normalized": false,
|
| 299 |
+
"rstrip": false,
|
| 300 |
+
"single_word": false,
|
| 301 |
+
"special": true
|
| 302 |
+
},
|
| 303 |
+
"255029": {
|
| 304 |
+
"content": "<|END_OF_IMG|>",
|
| 305 |
+
"lstrip": false,
|
| 306 |
+
"normalized": false,
|
| 307 |
+
"rstrip": false,
|
| 308 |
+
"single_word": false,
|
| 309 |
+
"special": true
|
| 310 |
+
},
|
| 311 |
+
"255030": {
|
| 312 |
+
"content": "<|IMG_LINE_BREAK|>",
|
| 313 |
+
"lstrip": false,
|
| 314 |
+
"normalized": false,
|
| 315 |
+
"rstrip": false,
|
| 316 |
+
"single_word": false,
|
| 317 |
+
"special": true
|
| 318 |
+
},
|
| 319 |
+
"255031": {
|
| 320 |
+
"content": "<|IMG_PATCH|>",
|
| 321 |
+
"lstrip": false,
|
| 322 |
+
"normalized": false,
|
| 323 |
+
"rstrip": false,
|
| 324 |
+
"single_word": false,
|
| 325 |
+
"special": true
|
| 326 |
+
}
|
| 327 |
+
}
|
| 328 |
+
}
|