Instructions to use mlabonne/Beyonder-4x7B-v3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mlabonne/Beyonder-4x7B-v3-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 mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mlabonne/Beyonder-4x7B-v3-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 mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mlabonne/Beyonder-4x7B-v3-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 mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mlabonne/Beyonder-4x7B-v3-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 mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mlabonne/Beyonder-4x7B-v3-GGUF with Ollama:
ollama run hf.co/mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mlabonne/Beyonder-4x7B-v3-GGUF with Docker Model Runner:
docker model run hf.co/mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M
- Lemonade
How to use mlabonne/Beyonder-4x7B-v3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mlabonne/Beyonder-4x7B-v3-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Beyonder-4x7B-v3-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
🔮 Beyonder-4x7B-v3 GGUF
Beyonder-4x7B-v3 is an improvement over the popular Beyonder-4x7B-v2. It's a Mixture of Experts (MoE) made with the following models using LazyMergekit:
- mlabonne/AlphaMonarch-7B
- beowolx/CodeNinja-1.0-OpenChat-7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- mlabonne/NeuralDaredevil-7B
Special thanks to beowolx for making the best Mistral-based code model and to SanjiWatsuki for creating one of the very best RP models.
🔍 Applications
This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).
If you use SillyTavern, you might want to tweak the inference parameters. Here's what LM Studio uses as a reference: temp 0.8, top_k 40, top_p 0.95, min_p 0.05, repeat_penalty 1.1.
Thanks to its four experts, it's a well-rounded model, capable of achieving most tasks. As two experts are always used to generate an answer, every task benefits from other capabilities, like chat with RP, or math with code.
⚡ Quantized models
Thanks bartowski for quantizing this model.
- GGUF: https://huggingface.co/mlabonne/Beyonder-4x7B-v3-GGUF
- More GGUF: https://huggingface.co/bartowski/Beyonder-4x7B-v3-GGUF
- ExLlamaV2: https://huggingface.co/bartowski/Beyonder-4x7B-v3-exl2
🏆 Evaluation
This model is not designed to excel in traditional benchmarks, as the code and role-playing models generally do not apply to those contexts. Nonetheless, it performs remarkably well thanks to strong general-purpose experts.
Nous
Beyonder-4x7B-v3 is one of the best models on Nous' benchmark suite (evaluation performed using LLM AutoEval) and significantly outperforms the v2. See the entire leaderboard here.
| Model | Average | AGIEval | GPT4All | TruthfulQA | Bigbench |
|---|---|---|---|---|---|
| mlabonne/AlphaMonarch-7B 📄 | 62.74 | 45.37 | 77.01 | 78.39 | 50.2 |
| mlabonne/Beyonder-4x7B-v3 📄 | 61.91 | 45.85 | 76.67 | 74.98 | 50.12 |
| mlabonne/NeuralDaredevil-7B 📄 | 59.39 | 45.23 | 76.2 | 67.61 | 48.52 |
| SanjiWatsuki/Kunoichi-DPO-v2-7B 📄 | 58.29 | 44.79 | 75.05 | 65.68 | 47.65 |
| mlabonne/Beyonder-4x7B-v2 📄 | 57.13 | 45.29 | 75.95 | 60.86 | 46.4 |
| beowolx/CodeNinja-1.0-OpenChat-7B 📄 | 50.35 | 39.98 | 71.77 | 48.73 | 40.92 |
EQ-Bench
Beyonder-4x7B-v3 is the best 4x7B model on the EQ-Bench leaderboard, outperforming older versions of ChatGPT and Llama-2-70b-chat. It is very close to Mixtral-8x7B-Instruct-v0.1 and Gemini Pro. Thanks Sam Paech for running the eval.
Open LLM Leaderboard
It's also a strong performer on the Open LLM Leaderboard, significantly outperforming the v2 model.
🧩 Configuration
base_model: mlabonne/AlphaMonarch-7B
experts:
- source_model: mlabonne/AlphaMonarch-7B
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- "I want"
- source_model: beowolx/CodeNinja-1.0-OpenChat-7B
positive_prompts:
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
- source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
positive_prompts:
- "storywriting"
- "write"
- "scene"
- "story"
- "character"
- source_model: mlabonne/NeuralDaredevil-7B
positive_prompts:
- "reason"
- "math"
- "mathematics"
- "solve"
- "count"
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