Text Generation
Transformers
Safetensors
llama
text-generation-inference
8-bit precision
bitsandbytes
Instructions to use Chat-Error/7B-XXL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Chat-Error/7B-XXL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Chat-Error/7B-XXL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Chat-Error/7B-XXL") model = AutoModelForCausalLM.from_pretrained("Chat-Error/7B-XXL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Chat-Error/7B-XXL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Chat-Error/7B-XXL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Chat-Error/7B-XXL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Chat-Error/7B-XXL
- SGLang
How to use Chat-Error/7B-XXL 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 "Chat-Error/7B-XXL" \ --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": "Chat-Error/7B-XXL", "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 "Chat-Error/7B-XXL" \ --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": "Chat-Error/7B-XXL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Chat-Error/7B-XXL with Docker Model Runner:
docker model run hf.co/Chat-Error/7B-XXL
Download checkpoint-1058/training_args.bin from Chat-Error/7B-XXL: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/Chat-Error/7B-XXL/resolve/main/checkpoint-1058/training_args.bin
- Command line
-
hf download hf://Chat-Error/7B-XXL/checkpoint-1058/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Chat-Error/7B-XXL/resolve/main/checkpoint-1058/training_args.bin
5.24 kB
- Xet hash:
- 9ca228e9efa895316cedd70919e5c6815991e75ea5ab28a66db77f1631e2561f
- Size of remote file:
- 5.24 kB
- SHA256:
- f0aab41d2319cf6e60af3f66d70dcf7009e492957f40bbf492e789e4268fe546
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