Instructions to use Envoid/Bacchus-22B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Envoid/Bacchus-22B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Envoid/Bacchus-22B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Envoid/Bacchus-22B") model = AutoModelForCausalLM.from_pretrained("Envoid/Bacchus-22B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Envoid/Bacchus-22B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Envoid/Bacchus-22B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Envoid/Bacchus-22B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Envoid/Bacchus-22B
- SGLang
How to use Envoid/Bacchus-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 "Envoid/Bacchus-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": "Envoid/Bacchus-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 "Envoid/Bacchus-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": "Envoid/Bacchus-22B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Envoid/Bacchus-22B with Docker Model Runner:
docker model run hf.co/Envoid/Bacchus-22B
Create README.md
Browse files
README.md
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# Warning: This model is unpredictable and may produce adult content.
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Bacchus-22B uses the chargoddard llama-22b block diagonal merge script found here:
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https://huggingface.co/chargoddard/llama2-22b
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In this case I used Nous Hermes 13B as the base model:
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https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b
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And Manticore-30b-Chat-Pyg-Lapha-Landmark as the donor model:
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https://huggingface.co/Honkware/Manticore-30b-Chat-Pyg-Alpha-Landmark
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The initial results were a surprisingly coherent and functional model although I went ahead and gave it a fairly deep LoRA on 51 megabytes of raw text.
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It responds well to Alpaca instruct style prompt formatting.
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It can be a little rude at times and doesn't have Dendrite's ego and thirst for philosophical discussion but I feel that it's overall it's a much better general purpose model.
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