Instructions to use grimpep/Huginnstruct-22B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grimpep/Huginnstruct-22B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="grimpep/Huginnstruct-22B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("grimpep/Huginnstruct-22B") model = AutoModelForCausalLM.from_pretrained("grimpep/Huginnstruct-22B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use grimpep/Huginnstruct-22B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "grimpep/Huginnstruct-22B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "grimpep/Huginnstruct-22B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/grimpep/Huginnstruct-22B
- SGLang
How to use grimpep/Huginnstruct-22B 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 "grimpep/Huginnstruct-22B" \ --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": "grimpep/Huginnstruct-22B", "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 "grimpep/Huginnstruct-22B" \ --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": "grimpep/Huginnstruct-22B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use grimpep/Huginnstruct-22B with Docker Model Runner:
docker model run hf.co/grimpep/Huginnstruct-22B
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Download README.md from grimpep/Huginnstruct-22B: direct link, hf CLI and curl.
- Browser
- Download file 593 Bytes
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https://huggingface.co/grimpep/Huginnstruct-22B/resolve/main/README.md
- Command line
-
hf download hf://grimpep/Huginnstruct-22B/README.md
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curl -L -o README.md https://huggingface.co/grimpep/Huginnstruct-22B/resolve/main/README.md
593 Bytes
| license: other | |
| tags: | |
| - llama | |
| - llama-2 | |
| [Experimental model] | |
| This model is an experiment using the frankenstein script from | |
| https://huggingface.co/chargoddard/llama2-22b | |
| BLOCK_DIAGONAL = False | |
| Using: | |
| https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16 | |
| + | |
| Then used https://huggingface.co/upstage/llama-30b-instruct-2048 | |
| as donor model. | |
| It used 160GB of system ram to merge these models, they merge fast without swap. | |
| For prompt template and model information see [huginnV1](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16). |