Instructions to use QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF", filename="Rombos-LLM-V2.5.1-Qwen-3b.Q2_K.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 QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-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 QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-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 QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-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 QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF with Ollama:
ollama run hf.co/QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-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 QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-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 QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF to start chatting
- Pi new
How to use QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-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": "QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-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 QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-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 QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Rombos-LLM-V2.5.1-Qwen-3b-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF
This is quantized version of rombodawg/Rombos-LLM-V2.5.1-Qwen-3b created using llama.cpp
Original Model Card
Rombos-LLM-V2.5.1-Qwen-3b
A little experiment I threw together to take a really high quality LLM I found (arcee-ai/raspberry-3B) and merge it using the last step of my Continuous Finetuning method outlines in the paper linked bellow.
https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing
Mergekit.yaml file is as follows:
models:
- model: Qwen2.5-3B-Instruct
parameters:
weight: 1
density: 1
- model: raspberry-3B
parameters:
weight: 1
density: 1
merge_method: ties
base_model: Qwen2.5-3B
parameters:
weight: 1
density: 1
normalize: true
int8_mask: true
dtype: bfloat16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 13.22 |
| IFEval (0-Shot) | 25.95 |
| BBH (3-Shot) | 14.88 |
| MATH Lvl 5 (4-Shot) | 8.31 |
| GPQA (0-shot) | 3.24 |
| MuSR (0-shot) | 7.82 |
| MMLU-PRO (5-shot) | 19.10 |
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Model tree for QuantFactory/Rombos-LLM-V2.5.1-Qwen-3b-GGUF
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard25.950
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard14.880
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard8.310
- acc_norm on GPQA (0-shot)Open LLM Leaderboard3.240
- acc_norm on MuSR (0-shot)Open LLM Leaderboard7.820
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard19.100
