Instructions to use mlabonne/PyLlama-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlabonne/PyLlama-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mlabonne/PyLlama-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mlabonne/PyLlama-7b") model = AutoModelForCausalLM.from_pretrained("mlabonne/PyLlama-7b") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mlabonne/PyLlama-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mlabonne/PyLlama-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlabonne/PyLlama-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mlabonne/PyLlama-7b
- SGLang
How to use mlabonne/PyLlama-7b 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 "mlabonne/PyLlama-7b" \ --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": "mlabonne/PyLlama-7b", "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 "mlabonne/PyLlama-7b" \ --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": "mlabonne/PyLlama-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mlabonne/PyLlama-7b with Docker Model Runner:
docker model run hf.co/mlabonne/PyLlama-7b
File size: 524 Bytes
ad2f7f1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | {
"auto_mapping": null,
"base_model_name_or_path": "codellama/CodeLlama-7b-hf",
"bias": "none",
"fan_in_fan_out": null,
"inference_mode": true,
"init_lora_weights": true,
"layers_pattern": null,
"layers_to_transform": null,
"lora_alpha": 16,
"lora_dropout": 0.05,
"modules_to_save": null,
"peft_type": "LORA",
"r": 32,
"revision": null,
"target_modules": [
"k_proj",
"v_proj",
"o_proj",
"gate_proj",
"down_proj",
"up_proj",
"q_proj"
],
"task_type": "CAUSAL_LM"
} |