Text Generation
Transformers
Safetensors
mistral
code-llm
mistral-7b
language-model
text-generation-inference
How to use from
SGLangUse 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 "Villian7/01Coder" \
--host 0.0.0.0 \
--port 30000# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Villian7/01Coder",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'Quick Links
Model Card for 01Coder 7B
This model card provides details about a code language model (LLM) based on Mistral 7B architecture. It has been trained on a combination of three datasets: ise-uiuc/Magicoder-OSS-Instruct-75K, HuggingFaceH4/CodeAlpaca_20K, and theblackcat102/evol-codealpaca-v1.
Model Details
Model Description
This model is a language model fine-tuned for code generation tasks, leveraging the Mistral 7B base model architecture. It has been trained on a combination of three datasets, namely Magicoder-OSS-Instruct-75K, CodeAlpaca_20K, and evol-codealpaca-v1. The model aims to assist developers in generating code snippets for various programming tasks, ranging from natural language instructions to specific coding prompts.
- Developed by: Manoj Athreya A
- Model type: Language model (LLM)
- License: [Apache 2.0 License]
- Finetuned from model: Mistral 7B
Intended Uses
- Code generation from natural language prompts.
- Assisting developers in completing code snippets.
- Augmenting code-related tasks with automated generation capabilities.
Limitations and Ethical Considerations
- Bias: As with any language model, biases present in the training data may manifest in the generated code snippets.
- Accuracy: While the model aims to generate accurate code, it may occasionally produce incorrect or suboptimal solutions, especially for complex tasks.
- Security: Generated code should be reviewed for security vulnerabilities, as the model may inadvertently produce insecure implementations.
- Ethical Use: Users are encouraged to employ the model responsibly and ethically, avoiding harmful or malicious use cases.
Recommendations
- Fine-tuning the model on specific domains or tasks may improve its performance.
- Validate generated code in real-world scenarios to ensure its correctness and reliability.
- Provide feedback to continuously improve the model's performance and address any issues encountered during usage.
License
- The source code in this repo is licensed under the Apache 2.0 license.
Version History
- 01-Coder-7Bv0.1
- Downloads last month
- 5
Install from pip and serve model
# Install SGLang from pip: pip install sglang# Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Villian7/01Coder" \ --host 0.0.0.0 \ --port 30000# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Villian7/01Coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'