Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
text-generation-inference
4-bit precision
awq
Instructions to use solidrust/SeverusWestLake-7B-DPO-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use solidrust/SeverusWestLake-7B-DPO-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="solidrust/SeverusWestLake-7B-DPO-AWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("solidrust/SeverusWestLake-7B-DPO-AWQ") model = AutoModelForMultimodalLM.from_pretrained("solidrust/SeverusWestLake-7B-DPO-AWQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use solidrust/SeverusWestLake-7B-DPO-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "solidrust/SeverusWestLake-7B-DPO-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "solidrust/SeverusWestLake-7B-DPO-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/solidrust/SeverusWestLake-7B-DPO-AWQ
- SGLang
How to use solidrust/SeverusWestLake-7B-DPO-AWQ 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 "solidrust/SeverusWestLake-7B-DPO-AWQ" \ --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": "solidrust/SeverusWestLake-7B-DPO-AWQ", "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 "solidrust/SeverusWestLake-7B-DPO-AWQ" \ --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": "solidrust/SeverusWestLake-7B-DPO-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use solidrust/SeverusWestLake-7B-DPO-AWQ with Docker Model Runner:
docker model run hf.co/solidrust/SeverusWestLake-7B-DPO-AWQ
Adding model card
Browse files- .gitattributes +1 -0
- README.md +27 -0
.gitattributes
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README.md
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---
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license: mit
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---
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---
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base_model:
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- FelixChao/Sectumsempra-7B-DPO
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- cognitivecomputations/WestLake-7B-v2-laser
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license: mit
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tags:
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- mergekit
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- merge
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library_name: transformers
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model_creator: s3nh
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model_name: SeverusWestLake-7B-DPO
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model_type: mistral
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pipeline_tag: text-generation
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prompt_template: '<|im_start|>system
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{system_message}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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'
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quantized_by: Suparious
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---
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# SeverusWestLake-7B-DPO - AWQ
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- Model creator: [s3nh](https://huggingface.co/s3nh)
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- Original model: [SeverusWestLake-7B-DPO](https://huggingface.co/s3nh/SeverusWestLake-7B-DPO)
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