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")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Envoid/Bacchus-22B") model = AutoModelForCausalLM.from_pretrained("Envoid/Bacchus-22B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
Update README.md
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README.md
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@@ -11,4 +11,5 @@ The initial results were a surprisingly coherent and functional model although I
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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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It does occasionally output grammatical errors during RP so might need a few more epochs to better fit the training data.
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If you are role playing using the SillyTavern+SimpleProxy stack it does have a tendency to run away with a scene when using the verbose.mjs prompt format. The singleline.mjs format sometimes remedies this issue however it also causes some characters to give very short, dull replies. So achieving a balance might require a complete new custom prompt format.
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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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It does occasionally output grammatical errors during RP so might need a few more epochs to better fit the training data.
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If you are role playing using the SillyTavern+SimpleProxy stack it does have a tendency to run away with a scene when using the verbose.mjs prompt format. The singleline.mjs format sometimes remedies this issue however it also causes some characters to give very short, dull replies. So achieving a balance might require a complete new custom prompt format.
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## use_cache was originally set to false when uploaded this has now been remedied. recommended edit or redownload config.
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