Text Generation
Transformers
Safetensors
English
gemma
code
gemma-2b
finetune
qlora
text-generation-inference
Instructions to use SaikatM/Code-Gemma-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SaikatM/Code-Gemma-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SaikatM/Code-Gemma-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SaikatM/Code-Gemma-v1") model = AutoModelForCausalLM.from_pretrained("SaikatM/Code-Gemma-v1") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SaikatM/Code-Gemma-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SaikatM/Code-Gemma-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SaikatM/Code-Gemma-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SaikatM/Code-Gemma-v1
- SGLang
How to use SaikatM/Code-Gemma-v1 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 "SaikatM/Code-Gemma-v1" \ --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": "SaikatM/Code-Gemma-v1", "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 "SaikatM/Code-Gemma-v1" \ --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": "SaikatM/Code-Gemma-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SaikatM/Code-Gemma-v1 with Docker Model Runner:
docker model run hf.co/SaikatM/Code-Gemma-v1
Update README.md
Browse files
README.md
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#### Training Hyperparameters
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LoraConfig(
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r=4,
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lora_alpha=2,
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target_modules=modules,
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TrainingArguments(
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output_dir="gemma-2b-code-platypus",
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num_train_epochs=1,
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per_device_train_batch_size=4,
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SFTTrainer(
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model=model,
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train_dataset=train_data,
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eval_dataset=test_data,
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#### Training Hyperparameters
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LoraConfig(
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r=4,
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lora_alpha=2,
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target_modules=modules,
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)
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TrainingArguments(
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output_dir="gemma-2b-code-platypus",
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num_train_epochs=1,
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per_device_train_batch_size=4,
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)
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SFTTrainer(
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model=model,
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train_dataset=train_data,
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eval_dataset=test_data,
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