Text Generation
Transformers
Safetensors
qwen2
text-generation-inference
unsloth
conversational
Eval Results (legacy)
Instructions to use rombodawg/rombos_Replete-Coder-Qwen2-1.5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rombodawg/rombos_Replete-Coder-Qwen2-1.5b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rombodawg/rombos_Replete-Coder-Qwen2-1.5b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("rombodawg/rombos_Replete-Coder-Qwen2-1.5b") model = AutoModelForCausalLM.from_pretrained("rombodawg/rombos_Replete-Coder-Qwen2-1.5b") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rombodawg/rombos_Replete-Coder-Qwen2-1.5b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rombodawg/rombos_Replete-Coder-Qwen2-1.5b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rombodawg/rombos_Replete-Coder-Qwen2-1.5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rombodawg/rombos_Replete-Coder-Qwen2-1.5b
- SGLang
How to use rombodawg/rombos_Replete-Coder-Qwen2-1.5b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "rombodawg/rombos_Replete-Coder-Qwen2-1.5b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rombodawg/rombos_Replete-Coder-Qwen2-1.5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "rombodawg/rombos_Replete-Coder-Qwen2-1.5b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rombodawg/rombos_Replete-Coder-Qwen2-1.5b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use rombodawg/rombos_Replete-Coder-Qwen2-1.5b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rombodawg/rombos_Replete-Coder-Qwen2-1.5b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rombodawg/rombos_Replete-Coder-Qwen2-1.5b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rombodawg/rombos_Replete-Coder-Qwen2-1.5b to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rombodawg/rombos_Replete-Coder-Qwen2-1.5b", max_seq_length=2048, ) - Docker Model Runner
How to use rombodawg/rombos_Replete-Coder-Qwen2-1.5b with Docker Model Runner:
docker model run hf.co/rombodawg/rombos_Replete-Coder-Qwen2-1.5b
Commit ·
a32debd
verified ·
0
Parent(s):
Duplicate from Replete-AI/Replete-Coder-Qwen2-1.5b
Browse files- .gitattributes +35 -0
- README.md +232 -0
- added_tokens.json +6 -0
- config.json +30 -0
- generation_config.json +6 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- special_tokens_map.json +14 -0
- tokenizer.json +0 -0
- tokenizer_config.json +52 -0
- vocab.json +0 -0
.gitattributes
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README.md
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| 1 |
+
---
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| 2 |
+
license: apache-2.0
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| 3 |
+
base_model: Qwen/Qwen2-1.5B
|
| 4 |
+
tags:
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| 5 |
+
- text-generation-inference
|
| 6 |
+
- transformers
|
| 7 |
+
- unsloth
|
| 8 |
+
- qwen2
|
| 9 |
+
datasets:
|
| 10 |
+
- Replete-AI/code_bagel_hermes-2.5
|
| 11 |
+
- Replete-AI/code_bagel
|
| 12 |
+
- Replete-AI/OpenHermes-2.5-Uncensored
|
| 13 |
+
- teknium/OpenHermes-2.5
|
| 14 |
+
- layoric/tiny-codes-alpaca
|
| 15 |
+
- glaiveai/glaive-code-assistant-v3
|
| 16 |
+
- ajibawa-2023/Code-290k-ShareGPT
|
| 17 |
+
- TIGER-Lab/MathInstruct
|
| 18 |
+
- chargoddard/commitpack-ft-instruct-rated
|
| 19 |
+
- iamturun/code_instructions_120k_alpaca
|
| 20 |
+
- ise-uiuc/Magicoder-Evol-Instruct-110K
|
| 21 |
+
- cognitivecomputations/dolphin-coder
|
| 22 |
+
- nickrosh/Evol-Instruct-Code-80k-v1
|
| 23 |
+
- coseal/CodeUltraFeedback_binarized
|
| 24 |
+
- glaiveai/glaive-function-calling-v2
|
| 25 |
+
- CyberNative/Code_Vulnerability_Security_DPO
|
| 26 |
+
- jondurbin/airoboros-2.2
|
| 27 |
+
- camel-ai
|
| 28 |
+
- lmsys/lmsys-chat-1m
|
| 29 |
+
- CollectiveCognition/chats-data-2023-09-22
|
| 30 |
+
- CoT-Alpaca-GPT4
|
| 31 |
+
- WizardLM/WizardLM_evol_instruct_70k
|
| 32 |
+
- WizardLM/WizardLM_evol_instruct_V2_196k
|
| 33 |
+
- teknium/GPT4-LLM-Cleaned
|
| 34 |
+
- GPTeacher
|
| 35 |
+
- OpenGPT
|
| 36 |
+
- meta-math/MetaMathQA
|
| 37 |
+
- Open-Orca/SlimOrca
|
| 38 |
+
- garage-bAInd/Open-Platypus
|
| 39 |
+
- anon8231489123/ShareGPT_Vicuna_unfiltered
|
| 40 |
+
- Unnatural-Instructions-GPT4
|
| 41 |
+
model-index:
|
| 42 |
+
- name: Replete-Coder-llama3-8b
|
| 43 |
+
results:
|
| 44 |
+
- task:
|
| 45 |
+
name: HumanEval
|
| 46 |
+
type: text-generation
|
| 47 |
+
dataset:
|
| 48 |
+
type: openai_humaneval
|
| 49 |
+
name: HumanEval
|
| 50 |
+
metrics:
|
| 51 |
+
- name: pass@1
|
| 52 |
+
type: pass@1
|
| 53 |
+
value: 0.35365853658536583
|
| 54 |
+
verified: True
|
| 55 |
+
- task:
|
| 56 |
+
name: AI2 Reasoning Challenge
|
| 57 |
+
type: text-generation
|
| 58 |
+
dataset:
|
| 59 |
+
name: AI2 Reasoning Challenge (25-Shot)
|
| 60 |
+
type: ai2_arc
|
| 61 |
+
config: ARC-Challenge
|
| 62 |
+
split: test
|
| 63 |
+
args:
|
| 64 |
+
num_few_shot: 25
|
| 65 |
+
metrics:
|
| 66 |
+
- type: accuracy
|
| 67 |
+
value:
|
| 68 |
+
name: normalized accuracy
|
| 69 |
+
source:
|
| 70 |
+
url: https://www.placeholderurl.com
|
| 71 |
+
name: Open LLM Leaderboard
|
| 72 |
+
- task:
|
| 73 |
+
name: Text Generation
|
| 74 |
+
type: text-generation
|
| 75 |
+
dataset:
|
| 76 |
+
name: HellaSwag (10-Shot)
|
| 77 |
+
type: hellaswag
|
| 78 |
+
split: validation
|
| 79 |
+
args:
|
| 80 |
+
num_few_shot: 10
|
| 81 |
+
metrics:
|
| 82 |
+
- type: accuracy
|
| 83 |
+
value:
|
| 84 |
+
name: normalized accuracy
|
| 85 |
+
source:
|
| 86 |
+
url: https://www.placeholderurl.com
|
| 87 |
+
name: Open LLM Leaderboard
|
| 88 |
+
- task:
|
| 89 |
+
name: Text Generation
|
| 90 |
+
type: text-generation
|
| 91 |
+
dataset:
|
| 92 |
+
name: MMLU (5-Shot)
|
| 93 |
+
type: cais/mmlu
|
| 94 |
+
config: all
|
| 95 |
+
split: test
|
| 96 |
+
args:
|
| 97 |
+
num_few_shot: 5
|
| 98 |
+
metrics:
|
| 99 |
+
- type: accuracy
|
| 100 |
+
value:
|
| 101 |
+
name: accuracy
|
| 102 |
+
source:
|
| 103 |
+
url: https://www.placeholderurl.com
|
| 104 |
+
name: Open LLM Leaderboard
|
| 105 |
+
- task:
|
| 106 |
+
name: Text Generation
|
| 107 |
+
type: text-generation
|
| 108 |
+
dataset:
|
| 109 |
+
name: TruthfulQA (0-shot)
|
| 110 |
+
type: truthful_qa
|
| 111 |
+
config: multiple_choice
|
| 112 |
+
split: validation
|
| 113 |
+
args:
|
| 114 |
+
num_few_shot: 0
|
| 115 |
+
metrics:
|
| 116 |
+
- type: multiple_choice_accuracy
|
| 117 |
+
value:
|
| 118 |
+
source:
|
| 119 |
+
url: https://www.placeholderurl.com
|
| 120 |
+
name: Open LLM Leaderboard
|
| 121 |
+
- task:
|
| 122 |
+
name: Text Generation
|
| 123 |
+
type: text-generation
|
| 124 |
+
dataset:
|
| 125 |
+
name: Winogrande (5-shot)
|
| 126 |
+
type: winogrande
|
| 127 |
+
config: winogrande_xl
|
| 128 |
+
split: validation
|
| 129 |
+
args:
|
| 130 |
+
num_few_shot: 5
|
| 131 |
+
metrics:
|
| 132 |
+
- type: accuracy
|
| 133 |
+
value:
|
| 134 |
+
name: accuracy
|
| 135 |
+
source:
|
| 136 |
+
url: https://www.placeholderurl.com
|
| 137 |
+
name: Open LLM Leaderboard
|
| 138 |
+
- task:
|
| 139 |
+
name: Text Generation
|
| 140 |
+
type: text-generation
|
| 141 |
+
dataset:
|
| 142 |
+
name: GSM8k (5-shot)
|
| 143 |
+
type: gsm8k
|
| 144 |
+
config: main
|
| 145 |
+
split: test
|
| 146 |
+
args:
|
| 147 |
+
num_few_shot: 5
|
| 148 |
+
metrics:
|
| 149 |
+
- type: accuracy
|
| 150 |
+
value:
|
| 151 |
+
name: accuracy
|
| 152 |
+
source:
|
| 153 |
+
url: https://www.placeholderurl.com
|
| 154 |
+
name: Open LLM Leaderboard
|
| 155 |
+
|
| 156 |
+
---
|
| 157 |
+
# Replete-Coder-Qwen2-1.5b
|
| 158 |
+
Finetuned by: Rombodawg
|
| 159 |
+
### More than just a coding model!
|
| 160 |
+
Although Replete-Coder has amazing coding capabilities, its trained on vaste amount of non-coding data, fully cleaned and uncensored. Dont just use it for coding, use it for all your needs! We are truly trying to make the GPT killer!
|
| 161 |
+

|
| 162 |
+
|
| 163 |
+
Thank you to TensorDock for sponsoring Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b
|
| 164 |
+
you can check out their website for cloud compute rental below.
|
| 165 |
+
- https://tensordock.com
|
| 166 |
+
__________________________________________________________________________________________________
|
| 167 |
+
Replete-Coder-Qwen2-1.5b is a general purpose model that is specially trained in coding in over 100 coding languages. The data used to train the model contains 25% non-code instruction data and 75% coding instruction data totaling up to 3.9 million lines, roughly 1 billion tokens, or 7.27gb of instruct data. The data used to train this model was 100% uncensored, then fully deduplicated, before training happened.
|
| 168 |
+
|
| 169 |
+
The Replete-Coder models (including Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b) feature the following:
|
| 170 |
+
|
| 171 |
+
- Advanced coding capabilities in over 100 coding languages
|
| 172 |
+
- Advanced code translation (between languages)
|
| 173 |
+
- Security and vulnerability prevention related coding capabilities
|
| 174 |
+
- General purpose use
|
| 175 |
+
- Uncensored use
|
| 176 |
+
- Function calling
|
| 177 |
+
- Advanced math use
|
| 178 |
+
- Use on low end (8b) and mobile (1.5b) platforms
|
| 179 |
+
|
| 180 |
+
Notice: Replete-Coder series of models are fine-tuned on a context window of 8192 tokens. Performance past this context window is not guaranteed.
|
| 181 |
+
|
| 182 |
+

|
| 183 |
+
_________________________________________________________________________________________________
|
| 184 |
+
|
| 185 |
+
You can find the 25% non-coding instruction below:
|
| 186 |
+
|
| 187 |
+
- https://huggingface.co/datasets/Replete-AI/OpenHermes-2.5-Uncensored
|
| 188 |
+
|
| 189 |
+
And the 75% coding specific instruction data below:
|
| 190 |
+
|
| 191 |
+
- https://huggingface.co/datasets/Replete-AI/code_bagel
|
| 192 |
+
|
| 193 |
+
These two datasets were combined to create the final dataset for training, which is linked below:
|
| 194 |
+
|
| 195 |
+
- https://huggingface.co/datasets/Replete-AI/code_bagel_hermes-2.5
|
| 196 |
+
__________________________________________________________________________________________________
|
| 197 |
+
## Prompt Template: ChatML
|
| 198 |
+
```
|
| 199 |
+
<|im_start|>system
|
| 200 |
+
{}<|im_end|>
|
| 201 |
+
|
| 202 |
+
<|im_start|>user
|
| 203 |
+
{}<|im_end|>
|
| 204 |
+
|
| 205 |
+
<|im_start|>assistant
|
| 206 |
+
{}
|
| 207 |
+
```
|
| 208 |
+
Note: The system prompt varies in training data, but the most commonly used one is:
|
| 209 |
+
```
|
| 210 |
+
Below is an instruction that describes a task, Write a response that appropriately completes the request.
|
| 211 |
+
```
|
| 212 |
+
End token:
|
| 213 |
+
```
|
| 214 |
+
<|endoftext|>
|
| 215 |
+
```
|
| 216 |
+
__________________________________________________________________________________________________
|
| 217 |
+
Thank you to the community for your contributions to the Replete-AI/code_bagel_hermes-2.5 dataset. Without the participation of so many members making their datasets free and open source for any to use, this amazing AI model wouldn't be possible.
|
| 218 |
+
|
| 219 |
+
Extra special thanks to Teknium for the Open-Hermes-2.5 dataset and jondurbin for the bagel dataset and the naming idea for the code_bagel series of datasets. You can find both of their huggingface accounts linked below:
|
| 220 |
+
|
| 221 |
+
- https://huggingface.co/teknium
|
| 222 |
+
- https://huggingface.co/jondurbin
|
| 223 |
+
|
| 224 |
+
Another special thanks to unsloth for being the main method of training for Replete-Coder. Bellow you can find their github, as well as the special Replete-Ai secret sause (Unsloth + Qlora + Galore) colab code document that was used to train this model.
|
| 225 |
+
|
| 226 |
+
- https://github.com/unslothai/unsloth
|
| 227 |
+
- https://colab.research.google.com/drive/1eXGqy5M--0yW4u0uRnmNgBka-tDk2Li0?usp=sharing
|
| 228 |
+
__________________________________________________________________________________________________
|
| 229 |
+
|
| 230 |
+
## Join the Replete-Ai discord! We are a great and Loving community!
|
| 231 |
+
|
| 232 |
+
- https://discord.gg/ZZbnsmVnjD
|
added_tokens.json
ADDED
|
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|
| 1 |
+
{
|
| 2 |
+
"<|PAD_TOKEN|>": 151646,
|
| 3 |
+
"<|endoftext|>": 151643,
|
| 4 |
+
"<|im_end|>": 151645,
|
| 5 |
+
"<|im_start|>": 151644
|
| 6 |
+
}
|
config.json
ADDED
|
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|
|
|
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|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "rombodawg/Qwen2-1.5b-Reuploaded",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"Qwen2ForCausalLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 151643,
|
| 8 |
+
"eos_token_id": 151643,
|
| 9 |
+
"hidden_act": "silu",
|
| 10 |
+
"hidden_size": 1536,
|
| 11 |
+
"initializer_range": 0.02,
|
| 12 |
+
"intermediate_size": 8960,
|
| 13 |
+
"max_position_embeddings": 131072,
|
| 14 |
+
"max_window_layers": 28,
|
| 15 |
+
"model_type": "qwen2",
|
| 16 |
+
"num_attention_heads": 12,
|
| 17 |
+
"num_hidden_layers": 28,
|
| 18 |
+
"num_key_value_heads": 2,
|
| 19 |
+
"pad_token_id": 151646,
|
| 20 |
+
"rms_norm_eps": 1e-06,
|
| 21 |
+
"rope_theta": 1000000.0,
|
| 22 |
+
"sliding_window": 131072,
|
| 23 |
+
"tie_word_embeddings": true,
|
| 24 |
+
"torch_dtype": "bfloat16",
|
| 25 |
+
"transformers_version": "4.41.2",
|
| 26 |
+
"unsloth_version": "2024.6",
|
| 27 |
+
"use_cache": true,
|
| 28 |
+
"use_sliding_window": false,
|
| 29 |
+
"vocab_size": 151936
|
| 30 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 151643,
|
| 3 |
+
"eos_token_id": 151643,
|
| 4 |
+
"max_new_tokens": 2048,
|
| 5 |
+
"transformers_version": "4.41.2"
|
| 6 |
+
}
|
merges.txt
ADDED
|
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|
|
model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4e9af48980efaa5bf239fdc15bfa05ba3ec5bf005419eaf75e1335887f514c45
|
| 3 |
+
size 3087467144
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,14 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_start|>",
|
| 4 |
+
"<|im_end|>"
|
| 5 |
+
],
|
| 6 |
+
"eos_token": {
|
| 7 |
+
"content": "<|endoftext|>",
|
| 8 |
+
"lstrip": false,
|
| 9 |
+
"normalized": false,
|
| 10 |
+
"rstrip": false,
|
| 11 |
+
"single_word": false
|
| 12 |
+
},
|
| 13 |
+
"pad_token": "<|PAD_TOKEN|>"
|
| 14 |
+
}
|
tokenizer.json
ADDED
|
The diff for this file is too large to render.
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|
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,52 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"added_tokens_decoder": {
|
| 4 |
+
"151643": {
|
| 5 |
+
"content": "<|endoftext|>",
|
| 6 |
+
"lstrip": false,
|
| 7 |
+
"normalized": false,
|
| 8 |
+
"rstrip": false,
|
| 9 |
+
"single_word": false,
|
| 10 |
+
"special": true
|
| 11 |
+
},
|
| 12 |
+
"151644": {
|
| 13 |
+
"content": "<|im_start|>",
|
| 14 |
+
"lstrip": false,
|
| 15 |
+
"normalized": false,
|
| 16 |
+
"rstrip": false,
|
| 17 |
+
"single_word": false,
|
| 18 |
+
"special": true
|
| 19 |
+
},
|
| 20 |
+
"151645": {
|
| 21 |
+
"content": "<|im_end|>",
|
| 22 |
+
"lstrip": false,
|
| 23 |
+
"normalized": false,
|
| 24 |
+
"rstrip": false,
|
| 25 |
+
"single_word": false,
|
| 26 |
+
"special": true
|
| 27 |
+
},
|
| 28 |
+
"151646": {
|
| 29 |
+
"content": "<|PAD_TOKEN|>",
|
| 30 |
+
"lstrip": false,
|
| 31 |
+
"normalized": false,
|
| 32 |
+
"rstrip": false,
|
| 33 |
+
"single_word": false,
|
| 34 |
+
"special": true
|
| 35 |
+
}
|
| 36 |
+
},
|
| 37 |
+
"additional_special_tokens": [
|
| 38 |
+
"<|im_start|>",
|
| 39 |
+
"<|im_end|>"
|
| 40 |
+
],
|
| 41 |
+
"bos_token": null,
|
| 42 |
+
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
| 43 |
+
"clean_up_tokenization_spaces": false,
|
| 44 |
+
"eos_token": "<|endoftext|>",
|
| 45 |
+
"errors": "replace",
|
| 46 |
+
"model_max_length": 131072,
|
| 47 |
+
"pad_token": "<|PAD_TOKEN|>",
|
| 48 |
+
"padding_side": "left",
|
| 49 |
+
"split_special_tokens": false,
|
| 50 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 51 |
+
"unk_token": null
|
| 52 |
+
}
|
vocab.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|