Instructions to use Envoid/Yousei-22B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Envoid/Yousei-22B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Envoid/Yousei-22B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Envoid/Yousei-22B") model = AutoModelForCausalLM.from_pretrained("Envoid/Yousei-22B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Envoid/Yousei-22B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Envoid/Yousei-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/Yousei-22B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Envoid/Yousei-22B
- SGLang
How to use Envoid/Yousei-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/Yousei-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/Yousei-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/Yousei-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/Yousei-22B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Envoid/Yousei-22B with Docker Model Runner:
docker model run hf.co/Envoid/Yousei-22B
Update README.md
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README.md
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@@ -6,7 +6,7 @@ This model started as a block-diagonal [frankenllama merge](https://huggingface.
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However due to some anomaly likely caused by the novel methods used by MythoMax I was unable to initiate the LoRA training needed to bring the resulting model back to order.
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Being a [Chronorctypus-Limarobormes](https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b) enjoyer I decided to look further into the TIES-merging that it utilizes- as cited in
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I used [llama2-22b](https://huggingface.co/chargoddard/llama2-22b) as the base model upon which I merged the MythoMax/Enterredaas frankenmerge, [Dendrite-II](https://huggingface.co/Envoid/Dendrite-II-22B) and [Bacchus](https://huggingface.co/Envoid/Bacchus-22B)
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However due to some anomaly likely caused by the novel methods used by MythoMax I was unable to initiate the LoRA training needed to bring the resulting model back to order.
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Being a [Chronorctypus-Limarobormes](https://huggingface.co/chargoddard/Chronorctypus-Limarobormes-13b) enjoyer I decided to look further into the TIES-merging that it utilizes- as cited in the arXiv paper: [Resolving Interference When Merging Models](https://huggingface.co/papers/2306.01708
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I used [llama2-22b](https://huggingface.co/chargoddard/llama2-22b) as the base model upon which I merged the MythoMax/Enterredaas frankenmerge, [Dendrite-II](https://huggingface.co/Envoid/Dendrite-II-22B) and [Bacchus](https://huggingface.co/Envoid/Bacchus-22B)
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