Instructions to use ambrosfitz/mistral-nemo-humanities-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ambrosfitz/mistral-nemo-humanities-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ambrosfitz/mistral-nemo-humanities-gguf", filename="model-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use ambrosfitz/mistral-nemo-humanities-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf ambrosfitz/mistral-nemo-humanities-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 ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ambrosfitz/mistral-nemo-humanities-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 ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
Use Docker
docker model run hf.co/ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ambrosfitz/mistral-nemo-humanities-gguf with Ollama:
ollama run hf.co/ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
- Unsloth Studio new
How to use ambrosfitz/mistral-nemo-humanities-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ambrosfitz/mistral-nemo-humanities-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ambrosfitz/mistral-nemo-humanities-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ambrosfitz/mistral-nemo-humanities-gguf to start chatting
- Pi new
How to use ambrosfitz/mistral-nemo-humanities-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ambrosfitz/mistral-nemo-humanities-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use ambrosfitz/mistral-nemo-humanities-gguf with Docker Model Runner:
docker model run hf.co/ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
- Lemonade
How to use ambrosfitz/mistral-nemo-humanities-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ambrosfitz/mistral-nemo-humanities-gguf:Q4_K_M
Run and chat with the model
lemonade run user.mistral-nemo-humanities-gguf-Q4_K_M
List all available models
lemonade list
Mistral Nemo 12B - Humanities Distilled (GGUF)
This model is a distilled version of Mistral Nemo 12B, fine-tuned on humanities content including:
- MMLU Humanities datasets (history, philosophy, religion, ethics)
- Synthetic data generated from GPT-OSS-120B
Training Details
- Base Model: mistralai/Mistral-Nemo-Instruct-2407
- Teacher Model: openai/gpt-oss-120b
- Training Method: LoRA with 8-bit quantization
- Dataset: MMLU humanities + synthetic data
- Specialization: History, philosophy, and humanities understanding
Available Quantizations
- FP16 (
model-f16.gguf): Full precision, ~24GB - Q8_0 (
model-Q8_0.gguf): 8-bit quantization, ~13GB, high quality - Q4_K_M (
model-Q4_K_M.gguf): 4-bit quantization, ~7GB, recommended for most users
Usage with llama.cpp
# Download model
huggingface-cli download ambrosfitz/mistral-nemo-gguf model-Q4_K_M.gguf --local-dir ./models
# Run inference
./llama.cpp/main -m ./models/model-Q4_K_M.gguf -p "Question: What was the Renaissance?\n\nAnswer:" -n 256
Usage with Ollama
# Create Modelfile
cat > Modelfile <<EOF
FROM ./model-Q4_K_M.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.9
EOF
# Create model
ollama create mistral-humanities -f Modelfile
# Run
ollama run mistral-humanities "What was the Renaissance?"
Example Prompts
Question: What were the main causes of World War I?
Answer:
Question: Explain the philosophical ideas of the Enlightenment.
Answer:
Question: Who wrote 'The Republic' and what is it about?
Answer:
Limitations
This model is specialized for humanities topics and may not perform as well on:
- Technical/scientific questions
- Mathematics
- Coding
- Current events (knowledge cutoff applies)
License
Apache 2.0 (same as base model)
- Downloads last month
- 24
4-bit
16-bit
Model tree for ambrosfitz/mistral-nemo-humanities-gguf
Base model
mistralai/Mistral-Nemo-Base-2407