Instructions to use shareAI/CodeLlama-13b-English-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shareAI/CodeLlama-13b-English-Chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shareAI/CodeLlama-13b-English-Chat", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shareAI/CodeLlama-13b-English-Chat", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("shareAI/CodeLlama-13b-English-Chat", trust_remote_code=True) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use shareAI/CodeLlama-13b-English-Chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shareAI/CodeLlama-13b-English-Chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shareAI/CodeLlama-13b-English-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shareAI/CodeLlama-13b-English-Chat
- SGLang
How to use shareAI/CodeLlama-13b-English-Chat 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 "shareAI/CodeLlama-13b-English-Chat" \ --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": "shareAI/CodeLlama-13b-English-Chat", "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 "shareAI/CodeLlama-13b-English-Chat" \ --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": "shareAI/CodeLlama-13b-English-Chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use shareAI/CodeLlama-13b-English-Chat with Docker Model Runner:
docker model run hf.co/shareAI/CodeLlama-13b-English-Chat
| license: openrail | |
| datasets: | |
| - shareAI/ShareGPT-Chinese-English-90k | |
| - shareAI/CodeChat | |
| language: | |
| - en | |
| library_name: transformers | |
| tags: | |
| - code | |
| Code: | |
| (just run it, and the model weights will be auto download) | |
| Github:https://github.com/CrazyBoyM/CodeLLaMA-chat | |
| ``` | |
| # from Firefly | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| def main(): | |
| model_name = 'shareAI/CodeLLaMA-chat-13b-Chinese' | |
| device = 'cuda' | |
| max_new_tokens = 500 # max token for reply. | |
| history_max_len = 1000 # max token in history | |
| top_p = 0.9 | |
| temperature = 0.35 | |
| repetition_penalty = 1.0 | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| trust_remote_code=True, | |
| low_cpu_mem_usage=True, | |
| torch_dtype=torch.float16, | |
| device_map='auto' | |
| ).to(device).eval() | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| model_name, | |
| trust_remote_code=True, | |
| use_fast=False | |
| ) | |
| history_token_ids = torch.tensor([[]], dtype=torch.long) | |
| user_input = input('User:') | |
| while True: | |
| input_ids = tokenizer(user_input, return_tensors="pt", add_special_tokens=False).input_ids | |
| eos_token_id = torch.tensor([[tokenizer.eos_token_id]], dtype=torch.long) | |
| user_input_ids = torch.concat([input_ids, eos_token_id], dim=1) | |
| history_token_ids = torch.concat((history_token_ids, user_input_ids), dim=1) | |
| model_input_ids = history_token_ids[:, -history_max_len:].to(device) | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| input_ids=model_input_ids, max_new_tokens=max_new_tokens, do_sample=True, top_p=top_p, | |
| temperature=temperature, repetition_penalty=repetition_penalty, eos_token_id=tokenizer.eos_token_id | |
| ) | |
| model_input_ids_len = model_input_ids.size(1) | |
| response_ids = outputs[:, model_input_ids_len:] | |
| history_token_ids = torch.concat((history_token_ids, response_ids.cpu()), dim=1) | |
| response = tokenizer.batch_decode(response_ids) | |
| print("Bot:" + response[0].strip().replace(tokenizer.eos_token, "")) | |
| user_input = input('User:') | |
| if __name__ == '__main__': | |
| main() | |
| ``` |