Text Generation
Transformers
Safetensors
code
llama
llama-2
text-generation-inference
4-bit precision
gptq
Instructions to use TheBloke/CodeLlama-70B-Python-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/CodeLlama-70B-Python-GPTQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/CodeLlama-70B-Python-GPTQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/CodeLlama-70B-Python-GPTQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/CodeLlama-70B-Python-GPTQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TheBloke/CodeLlama-70B-Python-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/CodeLlama-70B-Python-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/CodeLlama-70B-Python-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/CodeLlama-70B-Python-GPTQ
- SGLang
How to use TheBloke/CodeLlama-70B-Python-GPTQ 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 "TheBloke/CodeLlama-70B-Python-GPTQ" \ --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": "TheBloke/CodeLlama-70B-Python-GPTQ", "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 "TheBloke/CodeLlama-70B-Python-GPTQ" \ --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": "TheBloke/CodeLlama-70B-Python-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/CodeLlama-70B-Python-GPTQ with Docker Model Runner:
docker model run hf.co/TheBloke/CodeLlama-70B-Python-GPTQ
| { | |
| "_name_or_path": "/workspace/process/codellama_codellama-70b-python-hf/source", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 8192, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 28672, | |
| "max_position_embeddings": 4096, | |
| "model_type": "llama", | |
| "num_attention_heads": 64, | |
| "num_hidden_layers": 80, | |
| "num_key_value_heads": 8, | |
| "pad_token_id": 0, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.37.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 32016, | |
| "quantization_config": { | |
| "bits": 4, | |
| "group_size": -1, | |
| "damp_percent": 0.1, | |
| "desc_act": true, | |
| "static_groups": false, | |
| "sym": true, | |
| "true_sequential": true, | |
| "model_name_or_path": null, | |
| "model_file_base_name": "model", | |
| "quant_method": "gptq", | |
| "modules_in_block_to_quantize": [ | |
| [ | |
| "self_attn.k_proj", | |
| "self_attn.v_proj", | |
| "self_attn.q_proj" | |
| ], | |
| [ | |
| "self_attn.o_proj" | |
| ], | |
| [ | |
| "mlp.up_proj", | |
| "mlp.gate_proj" | |
| ], | |
| [ | |
| "mlp.down_proj" | |
| ] | |
| ] | |
| } | |
| } |