Text Generation
Transformers
PyTorch
code
llama
Generated from Trainer
coding
llama-2
text-generation-inference
Instructions to use Plaban81/codegen-finetuned-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Plaban81/codegen-finetuned-python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Plaban81/codegen-finetuned-python")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Plaban81/codegen-finetuned-python") model = AutoModelForCausalLM.from_pretrained("Plaban81/codegen-finetuned-python", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Plaban81/codegen-finetuned-python with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Plaban81/codegen-finetuned-python" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Plaban81/codegen-finetuned-python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Plaban81/codegen-finetuned-python
- SGLang
How to use Plaban81/codegen-finetuned-python 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 "Plaban81/codegen-finetuned-python" \ --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": "Plaban81/codegen-finetuned-python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Plaban81/codegen-finetuned-python" \ --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": "Plaban81/codegen-finetuned-python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Plaban81/codegen-finetuned-python with Docker Model Runner:
docker model run hf.co/Plaban81/codegen-finetuned-python
Commit History
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Librarian Bot: Add base_model information to model
This pull request aims to enrich the metadata of your model by adding [`codellama/CodeLlama-7b-Python-hf`](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) as a `base_model` field, situated in the `YAML` block of your model's `README.md`. How did we find this information? We extracted this infromation from the `adapter_config.json` file of your model. **Why add this?** Enhancing your model's metadata in this way: - **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub. - **Highlights Impact** - It showcases the contributions and influences different models have within the community. For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer). This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback…4873ec9
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Upload folder using huggingface_hub
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Upload tokenizer
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Upload LlamaForCausalLM
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initial commit
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