Instructions to use Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix 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 Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: llama cli -hf Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix: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 Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix: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 Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M
Use Docker
docker model run hf.co/Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix with Ollama:
ollama run hf.co/Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M
- Unsloth Studio
How to use Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix 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 Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix 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 Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix to start chatting
- Docker Model Runner
How to use Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix with Docker Model Runner:
docker model run hf.co/Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M
- Lemonade
How to use Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Lewdiculous/L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix:Q4_K_M
Run and chat with the model
lemonade run user.L3-8B-Stheno-v3.3-32K-GGUF-IQ-Imatrix-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Feels like a step back from 3.2
Going straight from 3.2 to 3.3 I've noticed a significant loss in quality. I'm running q6 at 16k context with the exact settings I use for 3.2 with the most recent kcpp. I've found that Failure to follow format has increased, incredibly short or incredibly long replies are more common, and sometimes it will just ignore the prompt and either parrot from context, or make up a new prompt to follow.
Can confirm degradation too. I'm running Q8 with 16k context and model response is awful.
Can confirm degradation too. I'm running Q8 with 16k context and model response is awful.
Same.
I think I'm one of the few who doesn't completely agree with that. I would say it's like taking one steps forward and two step back. This is the first small model that was able to interpret two characters in the same card coherently, which is a step forward. The step back is the repetition and the inability to follow the prompt 100%, but I still see room for improvement. Overall, it's the first step in the right direction.