Text Generation
Transformers
Safetensors
mistral
alignment-handbook
Generated from Trainer
text-generation-inference
Instructions to use fblgit/juanako-7b-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fblgit/juanako-7b-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fblgit/juanako-7b-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fblgit/juanako-7b-v1") model = AutoModelForCausalLM.from_pretrained("fblgit/juanako-7b-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fblgit/juanako-7b-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fblgit/juanako-7b-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fblgit/juanako-7b-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fblgit/juanako-7b-v1
- SGLang
How to use fblgit/juanako-7b-v1 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 "fblgit/juanako-7b-v1" \ --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": "fblgit/juanako-7b-v1", "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 "fblgit/juanako-7b-v1" \ --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": "fblgit/juanako-7b-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fblgit/juanako-7b-v1 with Docker Model Runner:
docker model run hf.co/fblgit/juanako-7b-v1
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README.md
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# juanako-7b-v1 (UNA: Uniform Neural Alignment)
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This model uses uniform neural alignment (UNA) for the DPO training phases and is a fine-tuned version of [fblgit/zephyr-lora-dpo-b1](https://huggingface.co/fblgit/zephyr-lora-dpo-b1) on the HuggingFaceH4/ultrafeedback_binarized dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4594
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- Rewards/chosen: -1.1095
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Followed [alignment-handbook](https://github.com/huggingface/alignment-handbook) to perform DPO (Phase 2) over Zephyr-SFT model.
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**Please feel free to run more tests and commit the results. Also if you are interested to participate in [UNA's paper research or GPU sponsorship](mailto:
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Special thanks to [TheBloke](https://huggingface.co/TheBloke) for converting the model into multiple formats and overall his enormous contribution to the community.
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Here are the models:
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# juanako-7b-v1 (UNA: Uniform Neural Alignment)
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This model uses uniform neural alignment (UNA) for the DPO training phases and is a fine-tuned version of [fblgit/zephyr-lora-dpo-b1](https://huggingface.co/fblgit/zephyr-lora-dpo-b1) on the HuggingFaceH4/ultrafeedback_binarized dataset.
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**It is recommended to use the latest [Juanako Version](https://huggingface.co/fblgit/juanako-7b-UNA) which highly outperforms the v1**
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It achieves the following results on the evaluation set:
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- Loss: 0.4594
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- Rewards/chosen: -1.1095
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Followed [alignment-handbook](https://github.com/huggingface/alignment-handbook) to perform DPO (Phase 2) over Zephyr-SFT model.
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**Please feel free to run more tests and commit the results. Also if you are interested to participate in [UNA's paper research or GPU sponsorship](mailto:xavi@juanako.ai) to support UNA research, feel free to contact.**
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Special thanks to [TheBloke](https://huggingface.co/TheBloke) for converting the model into multiple formats and overall his enormous contribution to the community.
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Here are the models:
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