Text Generation
Transformers
Safetensors
code
gemma
Generated from Trainer
coding
text-generation-inference
Instructions to use MAISAAI/gemma-2b-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MAISAAI/gemma-2b-coder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MAISAAI/gemma-2b-coder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MAISAAI/gemma-2b-coder") model = AutoModelForCausalLM.from_pretrained("MAISAAI/gemma-2b-coder") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MAISAAI/gemma-2b-coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MAISAAI/gemma-2b-coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MAISAAI/gemma-2b-coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MAISAAI/gemma-2b-coder
- SGLang
How to use MAISAAI/gemma-2b-coder 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 "MAISAAI/gemma-2b-coder" \ --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": "MAISAAI/gemma-2b-coder", "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 "MAISAAI/gemma-2b-coder" \ --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": "MAISAAI/gemma-2b-coder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MAISAAI/gemma-2b-coder with Docker Model Runner:
docker model run hf.co/MAISAAI/gemma-2b-coder
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README.md
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model-index:
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- name: gemma-2b-coder
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results: []
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### Example of usage 👩💻
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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model_id = "
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
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def create_prompt(instruction):
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system = "You are a coding assistant that will help the user to resolve the following instruction:"
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instruction = "### Instruction: " + instruction
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return system + "\n" + instruction + "\n\n" + "### Solution:" + "\n"
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def generate(
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instruction,
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max_new_tokens=256,
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- generated_from_trainer
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- code
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- gemma
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model-index:
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- name: gemma-2b-coder
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results: []
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### Example of usage 👩💻
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I recommend install the following version of `torch`:
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```sh
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pip install "torch>=2.1.1" -U
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```
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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model_id = "MAISAAI/gemma-2b-coder"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
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def generate(
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instruction,
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max_new_tokens=256,
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