Instructions to use Karthik-sr/qwen-atlas-lora-checkpoint with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Karthik-sr/qwen-atlas-lora-checkpoint with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct") model = PeftModel.from_pretrained(base_model, "Karthik-sr/qwen-atlas-lora-checkpoint") - Transformers
How to use Karthik-sr/qwen-atlas-lora-checkpoint with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Karthik-sr/qwen-atlas-lora-checkpoint") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Karthik-sr/qwen-atlas-lora-checkpoint", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Karthik-sr/qwen-atlas-lora-checkpoint with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Karthik-sr/qwen-atlas-lora-checkpoint" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Karthik-sr/qwen-atlas-lora-checkpoint", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Karthik-sr/qwen-atlas-lora-checkpoint
- SGLang
How to use Karthik-sr/qwen-atlas-lora-checkpoint 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 "Karthik-sr/qwen-atlas-lora-checkpoint" \ --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": "Karthik-sr/qwen-atlas-lora-checkpoint", "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 "Karthik-sr/qwen-atlas-lora-checkpoint" \ --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": "Karthik-sr/qwen-atlas-lora-checkpoint", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Karthik-sr/qwen-atlas-lora-checkpoint with Docker Model Runner:
docker model run hf.co/Karthik-sr/qwen-atlas-lora-checkpoint
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +209 -0
- adapter_config.json +46 -0
- adapter_model.safetensors +3 -0
- chat_template.jinja +54 -0
- optimizer.pt +3 -0
- rng_state.pth +3 -0
- scheduler.pt +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +29 -0
- trainer_state.json +374 -0
- training_args.bin +3 -0
.gitattributes
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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| 1 |
+
---
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| 2 |
+
base_model: Qwen/Qwen2.5-7B-Instruct
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| 3 |
+
library_name: peft
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| 4 |
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pipeline_tag: text-generation
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tags:
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- base_model:adapter:Qwen/Qwen2.5-7B-Instruct
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| 7 |
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- lora
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- sft
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- transformers
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- trl
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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| 30 |
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- **Model type:** [More Information Needed]
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| 31 |
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- **Language(s) (NLP):** [More Information Needed]
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| 32 |
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- **License:** [More Information Needed]
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| 33 |
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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| 36 |
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| 37 |
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<!-- Provide the basic links for the model. -->
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| 39 |
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- **Repository:** [More Information Needed]
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| 40 |
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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| 50 |
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[More Information Needed]
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| 52 |
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### Downstream Use [optional]
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| 55 |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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| 56 |
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[More Information Needed]
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| 58 |
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| 59 |
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### Out-of-Scope Use
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| 60 |
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| 61 |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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| 62 |
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| 63 |
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[More Information Needed]
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| 64 |
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| 65 |
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## Bias, Risks, and Limitations
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| 66 |
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| 67 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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| 68 |
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| 69 |
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[More Information Needed]
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| 70 |
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### Recommendations
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| 72 |
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| 73 |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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| 74 |
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| 75 |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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| 76 |
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## How to Get Started with the Model
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| 78 |
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| 79 |
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Use the code below to get started with the model.
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| 81 |
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[More Information Needed]
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## Training Details
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| 84 |
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### Training Data
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| 86 |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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| 153 |
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| 154 |
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- **Hardware Type:** [More Information Needed]
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| 155 |
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- **Hours used:** [More Information Needed]
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| 156 |
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- **Cloud Provider:** [More Information Needed]
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| 157 |
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- **Compute Region:** [More Information Needed]
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| 158 |
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- **Carbon Emitted:** [More Information Needed]
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| 159 |
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## Technical Specifications [optional]
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| 161 |
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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| 181 |
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**BibTeX:**
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[More Information Needed]
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**APA:**
|
| 187 |
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| 188 |
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[More Information Needed]
|
| 189 |
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|
| 190 |
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## Glossary [optional]
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| 191 |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| 193 |
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[More Information Needed]
|
| 195 |
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## More Information [optional]
|
| 197 |
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|
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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| 203 |
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## Model Card Contact
|
| 205 |
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|
| 206 |
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[More Information Needed]
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| 207 |
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### Framework versions
|
| 208 |
+
|
| 209 |
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- PEFT 0.18.1
|
adapter_config.json
ADDED
|
@@ -0,0 +1,46 @@
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| 1 |
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{
|
| 2 |
+
"alora_invocation_tokens": null,
|
| 3 |
+
"alpha_pattern": {},
|
| 4 |
+
"arrow_config": null,
|
| 5 |
+
"auto_mapping": null,
|
| 6 |
+
"base_model_name_or_path": "Qwen/Qwen2.5-7B-Instruct",
|
| 7 |
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"bias": "none",
|
| 8 |
+
"corda_config": null,
|
| 9 |
+
"ensure_weight_tying": false,
|
| 10 |
+
"eva_config": null,
|
| 11 |
+
"exclude_modules": null,
|
| 12 |
+
"fan_in_fan_out": false,
|
| 13 |
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"inference_mode": true,
|
| 14 |
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"init_lora_weights": true,
|
| 15 |
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"layer_replication": null,
|
| 16 |
+
"layers_pattern": null,
|
| 17 |
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"layers_to_transform": null,
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| 18 |
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"loftq_config": {},
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| 19 |
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"lora_alpha": 32,
|
| 20 |
+
"lora_bias": false,
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| 21 |
+
"lora_dropout": 0.05,
|
| 22 |
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"megatron_config": null,
|
| 23 |
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"megatron_core": "megatron.core",
|
| 24 |
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"modules_to_save": null,
|
| 25 |
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"peft_type": "LORA",
|
| 26 |
+
"peft_version": "0.18.1",
|
| 27 |
+
"qalora_group_size": 16,
|
| 28 |
+
"r": 16,
|
| 29 |
+
"rank_pattern": {},
|
| 30 |
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"revision": null,
|
| 31 |
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"target_modules": [
|
| 32 |
+
"o_proj",
|
| 33 |
+
"gate_proj",
|
| 34 |
+
"down_proj",
|
| 35 |
+
"k_proj",
|
| 36 |
+
"q_proj",
|
| 37 |
+
"up_proj",
|
| 38 |
+
"v_proj"
|
| 39 |
+
],
|
| 40 |
+
"target_parameters": null,
|
| 41 |
+
"task_type": "CAUSAL_LM",
|
| 42 |
+
"trainable_token_indices": null,
|
| 43 |
+
"use_dora": false,
|
| 44 |
+
"use_qalora": false,
|
| 45 |
+
"use_rslora": false
|
| 46 |
+
}
|
adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:905509c9527dab0b81a8730bf9b2aaef71432a69998dde791c66b29dac2b0f8d
|
| 3 |
+
size 80792880
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,54 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{%- if tools %}
|
| 2 |
+
{{- '<|im_start|>system\n' }}
|
| 3 |
+
{%- if messages[0]['role'] == 'system' %}
|
| 4 |
+
{{- messages[0]['content'] }}
|
| 5 |
+
{%- else %}
|
| 6 |
+
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
| 7 |
+
{%- endif %}
|
| 8 |
+
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
| 9 |
+
{%- for tool in tools %}
|
| 10 |
+
{{- "\n" }}
|
| 11 |
+
{{- tool | tojson }}
|
| 12 |
+
{%- endfor %}
|
| 13 |
+
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
| 14 |
+
{%- else %}
|
| 15 |
+
{%- if messages[0]['role'] == 'system' %}
|
| 16 |
+
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
| 17 |
+
{%- else %}
|
| 18 |
+
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
| 19 |
+
{%- endif %}
|
| 20 |
+
{%- endif %}
|
| 21 |
+
{%- for message in messages %}
|
| 22 |
+
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
| 23 |
+
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
| 24 |
+
{%- elif message.role == "assistant" %}
|
| 25 |
+
{{- '<|im_start|>' + message.role }}
|
| 26 |
+
{%- if message.content %}
|
| 27 |
+
{{- '\n' + message.content }}
|
| 28 |
+
{%- endif %}
|
| 29 |
+
{%- for tool_call in message.tool_calls %}
|
| 30 |
+
{%- if tool_call.function is defined %}
|
| 31 |
+
{%- set tool_call = tool_call.function %}
|
| 32 |
+
{%- endif %}
|
| 33 |
+
{{- '\n<tool_call>\n{"name": "' }}
|
| 34 |
+
{{- tool_call.name }}
|
| 35 |
+
{{- '", "arguments": ' }}
|
| 36 |
+
{{- tool_call.arguments | tojson }}
|
| 37 |
+
{{- '}\n</tool_call>' }}
|
| 38 |
+
{%- endfor %}
|
| 39 |
+
{{- '<|im_end|>\n' }}
|
| 40 |
+
{%- elif message.role == "tool" %}
|
| 41 |
+
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
| 42 |
+
{{- '<|im_start|>user' }}
|
| 43 |
+
{%- endif %}
|
| 44 |
+
{{- '\n<tool_response>\n' }}
|
| 45 |
+
{{- message.content }}
|
| 46 |
+
{{- '\n</tool_response>' }}
|
| 47 |
+
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
| 48 |
+
{{- '<|im_end|>\n' }}
|
| 49 |
+
{%- endif %}
|
| 50 |
+
{%- endif %}
|
| 51 |
+
{%- endfor %}
|
| 52 |
+
{%- if add_generation_prompt %}
|
| 53 |
+
{{- '<|im_start|>assistant\n' }}
|
| 54 |
+
{%- endif %}
|
optimizer.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:25c87b4e1ec0d66ea5f77af5ddd775cf9d13a677c494db051605458541bd1157
|
| 3 |
+
size 161816251
|
rng_state.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:2378a2c3197ef96f2307fb74417b2247392b2cf52a4603f984e139ce954330a4
|
| 3 |
+
size 14645
|
scheduler.pt
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8803f022c60b184179e5954b1ae91f861d29fe5bf97935a67825796df543f419
|
| 3 |
+
size 1465
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
|
| 3 |
+
size 11421892
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,29 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"add_prefix_space": false,
|
| 3 |
+
"backend": "tokenizers",
|
| 4 |
+
"bos_token": null,
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eos_token": "<|im_end|>",
|
| 7 |
+
"errors": "replace",
|
| 8 |
+
"extra_special_tokens": [
|
| 9 |
+
"<|im_start|>",
|
| 10 |
+
"<|im_end|>",
|
| 11 |
+
"<|object_ref_start|>",
|
| 12 |
+
"<|object_ref_end|>",
|
| 13 |
+
"<|box_start|>",
|
| 14 |
+
"<|box_end|>",
|
| 15 |
+
"<|quad_start|>",
|
| 16 |
+
"<|quad_end|>",
|
| 17 |
+
"<|vision_start|>",
|
| 18 |
+
"<|vision_end|>",
|
| 19 |
+
"<|vision_pad|>",
|
| 20 |
+
"<|image_pad|>",
|
| 21 |
+
"<|video_pad|>"
|
| 22 |
+
],
|
| 23 |
+
"is_local": false,
|
| 24 |
+
"model_max_length": 131072,
|
| 25 |
+
"pad_token": "<|endoftext|>",
|
| 26 |
+
"split_special_tokens": false,
|
| 27 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 28 |
+
"unk_token": null
|
| 29 |
+
}
|
trainer_state.json
ADDED
|
@@ -0,0 +1,374 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
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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 |
+
"best_global_step": null,
|
| 3 |
+
"best_metric": null,
|
| 4 |
+
"best_model_checkpoint": null,
|
| 5 |
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"epoch": 3.0,
|
| 6 |
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"eval_steps": 500,
|
| 7 |
+
"global_step": 345,
|
| 8 |
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"is_hyper_param_search": false,
|
| 9 |
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"is_local_process_zero": true,
|
| 10 |
+
"is_world_process_zero": true,
|
| 11 |
+
"log_history": [
|
| 12 |
+
{
|
| 13 |
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"entropy": 1.6703570872545241,
|
| 14 |
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"epoch": 0.087527352297593,
|
| 15 |
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"grad_norm": 0.65625,
|
| 16 |
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"learning_rate": 0.00019478260869565218,
|
| 17 |
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"loss": 2.0278825759887695,
|
| 18 |
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"mean_token_accuracy": 0.612476097419858,
|
| 19 |
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"num_tokens": 18728.0,
|
| 20 |
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"step": 10
|
| 21 |
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},
|
| 22 |
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{
|
| 23 |
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"entropy": 1.2492346689105034,
|
| 24 |
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"epoch": 0.175054704595186,
|
| 25 |
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"grad_norm": 0.75,
|
| 26 |
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"learning_rate": 0.0001889855072463768,
|
| 27 |
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"loss": 1.2333297729492188,
|
| 28 |
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"mean_token_accuracy": 0.7266808465123177,
|
| 29 |
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"num_tokens": 37328.0,
|
| 30 |
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"step": 20
|
| 31 |
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},
|
| 32 |
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{
|
| 33 |
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"entropy": 1.1282426938414574,
|
| 34 |
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"epoch": 0.26258205689277897,
|
| 35 |
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"grad_norm": 0.6796875,
|
| 36 |
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"learning_rate": 0.00018318840579710147,
|
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