Instructions to use QuantFactory/L3-8B-Stheno-v3.2-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 QuantFactory/L3-8B-Stheno-v3.2-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/L3-8B-Stheno-v3.2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/L3-8B-Stheno-v3.2-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/L3-8B-Stheno-v3.2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/L3-8B-Stheno-v3.2-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/L3-8B-Stheno-v3.2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/L3-8B-Stheno-v3.2-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/L3-8B-Stheno-v3.2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/L3-8B-Stheno-v3.2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/L3-8B-Stheno-v3.2-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/L3-8B-Stheno-v3.2-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/L3-8B-Stheno-v3.2-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/L3-8B-Stheno-v3.2-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/L3-8B-Stheno-v3.2-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/L3-8B-Stheno-v3.2-GGUF with Ollama:
ollama run hf.co/QuantFactory/L3-8B-Stheno-v3.2-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/L3-8B-Stheno-v3.2-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/L3-8B-Stheno-v3.2-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/L3-8B-Stheno-v3.2-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/L3-8B-Stheno-v3.2-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/L3-8B-Stheno-v3.2-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/L3-8B-Stheno-v3.2-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/L3-8B-Stheno-v3.2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/L3-8B-Stheno-v3.2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.L3-8B-Stheno-v3.2-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
QuantFactory/L3-8B-Stheno-v3.2-GGUF
This is quantized version of Sao10K/L3-8B-Stheno-v3.2 created using llama.cpp
Model Description
Just message me on discord if you want to host this privately for a service or something. We can talk.
Train used 1x H100 SXM for like a total of 24 Hours over multiple runs.
Art by navy_(navy.blue) - Danbooru
Stheno-v3.2-Zeta
I have done a test run with multiple variations of the models, merged back to its base at various weights, different training runs too, and this Sixth iteration is the one I like most.
Changes compared to v3.1
- Included a mix of SFW and NSFW Storywriting Data, thanks to Gryphe
- Included More Instruct / Assistant-Style Data
- Further cleaned up Roleplaying Samples from c2 Logs -> A few terrible, really bad samples escaped heavy filtering. Manual pass fixed it.
- Hyperparameter tinkering for training, resulting in lower loss levels.
Testing Notes - Compared to v3.1
- Handles SFW / NSFW seperately better. Not as overly excessive with NSFW now. Kinda balanced.
- Better at Storywriting / Narration.
- Better at Assistant-type Tasks.
- Better Multi-Turn Coherency -> Reduced Issues?
- Slightly less creative? A worthy tradeoff. Still creative.
- Better prompt / instruction adherence.
Recommended Samplers:
Temperature - 1.12-1.22
Min-P - 0.075
Top-K - 50
Repetition Penalty - 1.1
Stopping Strings:
\n\n{{User}} # Or Equivalent, depending on Frontend
<|eot_id|>
<|end_of_text|>
Prompting Template - Llama-3-Instruct
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{output}<|eot_id|>
Basic Roleplay System Prompt
You are an expert actor that can fully immerse yourself into any role given. You do not break character for any reason, even if someone tries addressing you as an AI or language model.
Currently your role is {{char}}, which is described in detail below. As {{char}}, continue the exchange with {{user}}.
- Downloads last month
- 1,315
2-bit
3-bit
4-bit
5-bit
6-bit
8-bit
Model tree for QuantFactory/L3-8B-Stheno-v3.2-GGUF
Base model
Sao10K/L3-8B-Stheno-v3.2