Text Generation
Transformers
PyTorch
Safetensors
English
llama
text generation
instruct
text-generation-inference
Instructions to use upro/pygmalion-2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use upro/pygmalion-2-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="upro/pygmalion-2-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("upro/pygmalion-2-13b") model = AutoModelForCausalLM.from_pretrained("upro/pygmalion-2-13b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use upro/pygmalion-2-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "upro/pygmalion-2-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "upro/pygmalion-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/upro/pygmalion-2-13b
- SGLang
How to use upro/pygmalion-2-13b 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 "upro/pygmalion-2-13b" \ --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": "upro/pygmalion-2-13b", "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 "upro/pygmalion-2-13b" \ --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": "upro/pygmalion-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use upro/pygmalion-2-13b with Docker Model Runner:
docker model run hf.co/upro/pygmalion-2-13b
Update README.md
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by cherry0328 - opened
README.md
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- Norquinal/claude_multiround_chat_30k
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- jondurbin/airoboros-gpt4-1.4.1
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- databricks/databricks-dolly-15k
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---
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<h1 style="text-align: center">Pygmalion-2 13B</h1>
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<h2 style="text-align: center">An instruction-tuned Llama-2 biased towards fiction writing and conversation.</h2>
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## Acknowledgements
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We would like to thank [SpicyChat](https://spicychat.ai/) for sponsoring the training for this model.
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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- Norquinal/claude_multiround_chat_30k
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- jondurbin/airoboros-gpt4-1.4.1
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- databricks/databricks-dolly-15k
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base_model:
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- meta-llama/Llama-2-13b-hf
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---
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<h1 style="text-align: center">Pygmalion-2 13B</h1>
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<h2 style="text-align: center">An instruction-tuned Llama-2 biased towards fiction writing and conversation.</h2>
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## Acknowledgements
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We would like to thank [SpicyChat](https://spicychat.ai/) for sponsoring the training for this model.
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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