Instructions to use mingdali/ChatTruth-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mingdali/ChatTruth-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mingdali/ChatTruth-7B", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("mingdali/ChatTruth-7B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use mingdali/ChatTruth-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mingdali/ChatTruth-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mingdali/ChatTruth-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mingdali/ChatTruth-7B
- SGLang
How to use mingdali/ChatTruth-7B 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 "mingdali/ChatTruth-7B" \ --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": "mingdali/ChatTruth-7B", "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 "mingdali/ChatTruth-7B" \ --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": "mingdali/ChatTruth-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mingdali/ChatTruth-7B with Docker Model Runner:
docker model run hf.co/mingdali/ChatTruth-7B
ChatTruth-7B
ChatTruth-7B 在Qwen-VL的基础上,使用精心设计的数据进行了优化训练。与Qwen-VL相比,模型在大分辨率上得到了大幅提升。创新性提出Restore Module使大分辨率计算量大幅减少。
安装要求 (Requirements)
transformers 4.32.0
python 3.8 and above
pytorch 1.13 and above
CUDA 11.4 and above
快速开始 (Quickstart)
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
import torch
torch.manual_seed(1234)
model_path = 'ChatTruth-7B' # your downloaded model path.
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
# use cuda device
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="cuda", trust_remote_code=True).eval()
model.generation_config = GenerationConfig.from_pretrained(model_path, trust_remote_code=True)
model.generation_config.top_p = 0.01
query = tokenizer.from_list_format([
{'image': 'demo.jpeg'},
{'text': '图片中的文字是什么'},
])
response, history = model.chat(tokenizer, query=query, history=None)
print(response)
# 昆明太厉害了
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docker model run hf.co/mingdali/ChatTruth-7B