Instructions to use teolm30/fox1.4-high-reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use teolm30/fox1.4-high-reasoning with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="teolm30/fox1.4-high-reasoning", filename="fox1.4-high-reasoning.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use teolm30/fox1.4-high-reasoning with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf teolm30/fox1.4-high-reasoning # Run inference directly in the terminal: llama-cli -hf teolm30/fox1.4-high-reasoning
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf teolm30/fox1.4-high-reasoning # Run inference directly in the terminal: llama-cli -hf teolm30/fox1.4-high-reasoning
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 teolm30/fox1.4-high-reasoning # Run inference directly in the terminal: ./llama-cli -hf teolm30/fox1.4-high-reasoning
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 teolm30/fox1.4-high-reasoning # Run inference directly in the terminal: ./build/bin/llama-cli -hf teolm30/fox1.4-high-reasoning
Use Docker
docker model run hf.co/teolm30/fox1.4-high-reasoning
- LM Studio
- Jan
- Ollama
How to use teolm30/fox1.4-high-reasoning with Ollama:
ollama run hf.co/teolm30/fox1.4-high-reasoning
- Unsloth Studio
How to use teolm30/fox1.4-high-reasoning 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 teolm30/fox1.4-high-reasoning 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 teolm30/fox1.4-high-reasoning to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for teolm30/fox1.4-high-reasoning to start chatting
- Docker Model Runner
How to use teolm30/fox1.4-high-reasoning with Docker Model Runner:
docker model run hf.co/teolm30/fox1.4-high-reasoning
- Lemonade
How to use teolm30/fox1.4-high-reasoning with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull teolm30/fox1.4-high-reasoning
Run and chat with the model
lemonade run user.fox1.4-high-reasoning-{{QUANT_TAG}}List all available models
lemonade list
How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teolm30/fox1.4-high-reasoning# Run inference directly in the terminal:
llama-cli -hf teolm30/fox1.4-high-reasoningUse 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 teolm30/fox1.4-high-reasoning# Run inference directly in the terminal:
./llama-cli -hf teolm30/fox1.4-high-reasoningBuild 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 teolm30/fox1.4-high-reasoning# Run inference directly in the terminal:
./build/bin/llama-cli -hf teolm30/fox1.4-high-reasoningUse Docker
docker model run hf.co/teolm30/fox1.4-high-reasoningQuick Links
Fox 1.4 High Reasoning
A Greek fine-tuned version of Fox 1.3, a Qwen2.5-1B-Instruct based model.
Training
- Method: QLoRA (4-bit quantization)
- Dataset: Custom Greek conversation dataset (20 conversations)
- Epochs: 3
- Loss: 3.26 → 0.79
- Hardware: NVIDIA RTX 3050 (6GB VRAM)
- LoRA rank: 8, alpha: 16
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("teolm30/fox1.4-high-reasoning", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("teolm30/fox1.4-high-reasoning", trust_remote_code=True)
messages = [{"role": "user", "content": "Τι είναι η τεχνητή νοημοσύνη;"}]
input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
output = model.generate(input_ids, max_new_tokens=256)
print(tokenizer.decode(output[0]))
Greek Language Support
Fine-tuned for Greek language — grammar, vocabulary, natural conversation, and technical topics in Greek.
Previous Versions
🤖 Run with Ollama
ollama run hf.co/teolm30/fox1.4-high-reasoning
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Model tree for teolm30/fox1.4-high-reasoning
Base model
teolm30/fox1.3
Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf teolm30/fox1.4-high-reasoning# Run inference directly in the terminal: llama-cli -hf teolm30/fox1.4-high-reasoning