Text Generation
Transformers
Safetensors
English
mistral
instruct
finetune
chatml
gpt4
conversational
text-generation-inference
Instructions to use FPHam/Autolycus-Mistral_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FPHam/Autolycus-Mistral_7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FPHam/Autolycus-Mistral_7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("FPHam/Autolycus-Mistral_7B") model = AutoModelForMultimodalLM.from_pretrained("FPHam/Autolycus-Mistral_7B") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FPHam/Autolycus-Mistral_7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FPHam/Autolycus-Mistral_7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FPHam/Autolycus-Mistral_7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FPHam/Autolycus-Mistral_7B
- SGLang
How to use FPHam/Autolycus-Mistral_7B 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 "FPHam/Autolycus-Mistral_7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FPHam/Autolycus-Mistral_7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "FPHam/Autolycus-Mistral_7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FPHam/Autolycus-Mistral_7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use FPHam/Autolycus-Mistral_7B with Docker Model Runner:
docker model run hf.co/FPHam/Autolycus-Mistral_7B
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Autolycus is a son of Hermes.
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Autolycus-Mistral is a language
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- Original model: [OpenHermes 2.5 Mistral 7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
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Autolycus is a son of Hermes.
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Autolycus-Mistral is a language/content refinement of Open Hermes 2.5 Mistral, with the aim of taking it's responses from stilted, gpt-4 robotic gibberish into something approaching actual English, but at the expense of a slight increase in lying, fabrication and general BS.
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7-billion models are not known for their complete honesty.
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The most obvious examples of "making it all up," of course, are those times when Autolycus actually cites some reference (usually a book title or name, or date), but which you find to be nothing more than a load of hogwash when you check it out for yourself.
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- Original model: [OpenHermes 2.5 Mistral 7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)
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