Instructions to use mrSoul7766/gemma-2b-it-python-code-gen-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrSoul7766/gemma-2b-it-python-code-gen-adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mrSoul7766/gemma-2b-it-python-code-gen-adapter") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mrSoul7766/gemma-2b-it-python-code-gen-adapter") model = AutoModelForCausalLM.from_pretrained("mrSoul7766/gemma-2b-it-python-code-gen-adapter") 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mrSoul7766/gemma-2b-it-python-code-gen-adapter with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mrSoul7766/gemma-2b-it-python-code-gen-adapter" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mrSoul7766/gemma-2b-it-python-code-gen-adapter", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mrSoul7766/gemma-2b-it-python-code-gen-adapter
- SGLang
How to use mrSoul7766/gemma-2b-it-python-code-gen-adapter 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 "mrSoul7766/gemma-2b-it-python-code-gen-adapter" \ --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": "mrSoul7766/gemma-2b-it-python-code-gen-adapter", "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 "mrSoul7766/gemma-2b-it-python-code-gen-adapter" \ --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": "mrSoul7766/gemma-2b-it-python-code-gen-adapter", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mrSoul7766/gemma-2b-it-python-code-gen-adapter with Docker Model Runner:
docker model run hf.co/mrSoul7766/gemma-2b-it-python-code-gen-adapter
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# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:**
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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# Model Card for Model ID
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This is the Gemma-2b-IT model fine-tuned for the Python code generation task.
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** Mohammed Ashraf
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- **Model type:** google/gemma-2b
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- **Finetuned from model [optional]:** google/gemma-2b-it
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### Model Sources [optional]
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