Instructions to use Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF") model = AutoModelForCausalLM.from_pretrained("Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
- SGLang
How to use Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF 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 "Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF" \ --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": "Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF", "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 "Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF" \ --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": "Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF with Ollama:
ollama run hf.co/Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF with Docker Model Runner:
docker model run hf.co/Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
- Lemonade
How to use Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Goekdeniz-Guelmez/Hyperion-2.1-Mistral-7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Hyperion-2.1-Mistral-7B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Description
Further fine-tuned Locutusque/Hyperion-2.0-Mistral-7B at a higher learning rate. This was done to see if performance increased. Read Locutusque/Hyperion-2.0-Mistral-7B's model card for more information. Slight performance gain was observed. More checkpoints will be released in the future.
Disclaimer
This model is very compliant. It will respond to any request without refusal. If you intend to deploy this model at an enterprise level, I would recommend aligning this model using DPO.
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