Instructions to use QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-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 QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-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 QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-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/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-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/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-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/SOLAR-10.7B-Instruct-v1.0-uncensored-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/SOLAR-10.7B-Instruct-v1.0-uncensored-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/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF

This is quantized version of w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored created using llama.cpp

Original Model Card

SOLAR-10.7B-Instruct-v1.0-uncensored

SOLAR-10.7B-Instruct-v1.0 finetuned to be less censored. Refer to upstage/SOLAR-10.7B-Instruct-v1.0 for model info and usage instructions.

Training details

This model was trained using Lora and DPOTrainer on unalignment/toxic-dpo-v0.1

How to Cite

@misc{solarUncensoredDPO,
      title={solar-10.7b-instruct-V1.0-uncensored},
      url={https://huggingface.co/w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored},
      author={Stepan Zuev},
      year={2023},
      month={Dec}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	20.56
IFEval (0-Shot)	38.84
BBH (3-Shot)	33.86
MATH Lvl 5 (4-Shot)	0.23
GPQA (0-shot)	5.93
MuSR (0-shot)	18.49
MMLU-PRO (5-shot)	26.04

Downloads last month: 58

GGUF

Model size

11B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

38.840
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

33.860
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

0.230
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

5.930
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

18.490
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

26.040