Instructions to use csdc-atl/buffer-baichuan2-13B-rag-4bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use csdc-atl/buffer-baichuan2-13B-rag-4bits with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="csdc-atl/buffer-baichuan2-13B-rag-4bits", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("csdc-atl/buffer-baichuan2-13B-rag-4bits", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use csdc-atl/buffer-baichuan2-13B-rag-4bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "csdc-atl/buffer-baichuan2-13B-rag-4bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "csdc-atl/buffer-baichuan2-13B-rag-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/csdc-atl/buffer-baichuan2-13B-rag-4bits

SGLang

How to use csdc-atl/buffer-baichuan2-13B-rag-4bits 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 "csdc-atl/buffer-baichuan2-13B-rag-4bits" \
    --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": "csdc-atl/buffer-baichuan2-13B-rag-4bits",
		"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 "csdc-atl/buffer-baichuan2-13B-rag-4bits" \
        --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": "csdc-atl/buffer-baichuan2-13B-rag-4bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use csdc-atl/buffer-baichuan2-13B-rag-4bits with Docker Model Runner:
```
docker model run hf.co/csdc-atl/buffer-baichuan2-13B-rag-4bits
```

buffer-baichuan2-13B-rag-4bits

File size: 1,099 Bytes

0b8561b

import torch
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from transformers.generation.utils import GenerationConfig

# get dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16

class EndpointHandler:
    def __init__(self, path=""):
        # load the model
        self.model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", torch_dtype=dtype, trust_remote_code=True)
        self.model.generation_config = GenerationConfig.from_pretrained(path)
        self.tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False, trust_remote_code=True)

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        inputs = data.pop("inputs", data)
        # ignoring parameters! Default to configs in generation_config.json.
        messages = [{"role": "user", "content": inputs}]
        response = self.model.chat(self.tokenizer, messages)
        if torch.backends.mps.is_available():
            torch.mps.empty_cache()
        return [{'generated_text': response}]