Instructions to use Envoid/MindFlay-22B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Envoid/MindFlay-22B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Envoid/MindFlay-22B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Envoid/MindFlay-22B") model = AutoModelForCausalLM.from_pretrained("Envoid/MindFlay-22B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Envoid/MindFlay-22B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Envoid/MindFlay-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/MindFlay-22B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Envoid/MindFlay-22B
- SGLang
How to use Envoid/MindFlay-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/MindFlay-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/MindFlay-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/MindFlay-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/MindFlay-22B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Envoid/MindFlay-22B with Docker Model Runner:
docker model run hf.co/Envoid/MindFlay-22B
Update README.md
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README.md
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This is the original FP16 result of the model created using chargoddard's frankenllama script so that others interested in further experimentation with the results may do so.
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This is the original FP16 result of the model created using chargoddard's frankenllama script so that others interested in further experimentation with the results may do so.
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# WARNING: this model is very unpredictable.
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This model is an experiment using the frankenstein script from
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https://huggingface.co/chargoddard/llama2-22b
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Except I decided to use it with two models that have already been extensively finetuned.
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With https://huggingface.co/TheBloke/Llama-2-13B-Chat-fp16 as the base model
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and
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https://huggingface.co/Aeala/Enterredaas-33b as the donor model.
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The resulting model is surprisingly coherent and still responds well to the llama2chat prompt format
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```[INST]<<SYS>><</SYS>>[/INST]``` and still has most of llama2chat's bubbly/giddy personality but more gritty and visceral.
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It makes occasional "typos" along with some other quirks so it was not completely unscathed by the frankensteining process.
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I plan to massage it over with a LoRA in the near future to bring it into more harmony but in the meantime it is available now for your enjoyment.
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Use cases: Chat/RP not much else.
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