Text Generation
Transformers
PyTorch
baichuan
text generation
RAG
baichuan2
custom_code
text-generation-inference
Instructions to use Aman/selfrag-zh_baichuan2_7b_chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aman/selfrag-zh_baichuan2_7b_chat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Aman/selfrag-zh_baichuan2_7b_chat", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Aman/selfrag-zh_baichuan2_7b_chat", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Aman/selfrag-zh_baichuan2_7b_chat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Aman/selfrag-zh_baichuan2_7b_chat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Aman/selfrag-zh_baichuan2_7b_chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Aman/selfrag-zh_baichuan2_7b_chat
- SGLang
How to use Aman/selfrag-zh_baichuan2_7b_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 "Aman/selfrag-zh_baichuan2_7b_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": "Aman/selfrag-zh_baichuan2_7b_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 "Aman/selfrag-zh_baichuan2_7b_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": "Aman/selfrag-zh_baichuan2_7b_chat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Aman/selfrag-zh_baichuan2_7b_chat with Docker Model Runner:
docker model run hf.co/Aman/selfrag-zh_baichuan2_7b_chat
| { | |
| "_from_model_config": true, | |
| "_name_or_path": "../outputs/Baichuan2_7B_Chat_ext", | |
| "architectures": [ | |
| "BaichuanForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_baichuan.BaichuanConfig", | |
| "AutoModelForCausalLM": "modeling_baichuan.BaichuanForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 11008, | |
| "max_position_embeddings": 4096, | |
| "model_max_length": 4096, | |
| "model_type": "baichuan", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "pad_token_id": 0, | |
| "rms_norm_eps": 1e-06, | |
| "tie_word_embeddings": false, | |
| "tokenizer_class": "BaichuanTokenizer", | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.33.0", | |
| "use_cache": true, | |
| "vocab_size": 125711, | |
| "z_loss_weight": 0 | |
| } | |