Instructions to use trillionmonster/Baichuan-13B-Chat-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trillionmonster/Baichuan-13B-Chat-8bit with Transformers:

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

pipe = pipeline("text-generation", model="trillionmonster/Baichuan-13B-Chat-8bit", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use trillionmonster/Baichuan-13B-Chat-8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trillionmonster/Baichuan-13B-Chat-8bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trillionmonster/Baichuan-13B-Chat-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/trillionmonster/Baichuan-13B-Chat-8bit

SGLang

How to use trillionmonster/Baichuan-13B-Chat-8bit 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 "trillionmonster/Baichuan-13B-Chat-8bit" \
    --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": "trillionmonster/Baichuan-13B-Chat-8bit",
		"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 "trillionmonster/Baichuan-13B-Chat-8bit" \
        --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": "trillionmonster/Baichuan-13B-Chat-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use trillionmonster/Baichuan-13B-Chat-8bit with Docker Model Runner:
```
docker model run hf.co/trillionmonster/Baichuan-13B-Chat-8bit
```

原项目见 [https://huggingface.co/baichuan-inc/Baichuan-13B-Chat]

改动点：将原模型量化为8bit 保存为2GB大小的切片。

使用方式(int8)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
tokenizer = AutoTokenizer.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit",  device_map="auto", trust_remote_code=True)
model.generation_config = GenerationConfig.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit")
messages = []
messages.append({"role": "user", "content": "世界上第二高的山峰是哪座"})
response = model.chat(tokenizer, messages)
print(response)

如需使用 int4 量化 (Similarly, to use int4 quantization):

model = AutoModelForCausalLM.from_pretrained("trillionmonster/Baichuan-13B-Chat-8bit",  device_map="auto",load_in_4bit=True,trust_remote_code=True)

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