Instructions to use RenderedFatality/Qwen3-8B-DSLR-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 RenderedFatality/Qwen3-8B-DSLR-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 RenderedFatality/Qwen3-8B-DSLR-GGUF
# Run inference directly in the terminal:
llama cli -hf RenderedFatality/Qwen3-8B-DSLR-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RenderedFatality/Qwen3-8B-DSLR-GGUF
# Run inference directly in the terminal:
llama cli -hf RenderedFatality/Qwen3-8B-DSLR-GGUF

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 RenderedFatality/Qwen3-8B-DSLR-GGUF
# Run inference directly in the terminal:
./llama-cli -hf RenderedFatality/Qwen3-8B-DSLR-GGUF

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 RenderedFatality/Qwen3-8B-DSLR-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RenderedFatality/Qwen3-8B-DSLR-GGUF

Use Docker

docker model run hf.co/RenderedFatality/Qwen3-8B-DSLR-GGUF

LM Studio
Jan
Ollama
How to use RenderedFatality/Qwen3-8B-DSLR-GGUF with Ollama:
```
ollama run hf.co/RenderedFatality/Qwen3-8B-DSLR-GGUF
```

Unsloth Studio

How to use RenderedFatality/Qwen3-8B-DSLR-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 RenderedFatality/Qwen3-8B-DSLR-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 RenderedFatality/Qwen3-8B-DSLR-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RenderedFatality/Qwen3-8B-DSLR-GGUF to start chatting

Docker Model Runner
How to use RenderedFatality/Qwen3-8B-DSLR-GGUF with Docker Model Runner:
```
docker model run hf.co/RenderedFatality/Qwen3-8B-DSLR-GGUF
```

Lemonade

How to use RenderedFatality/Qwen3-8B-DSLR-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RenderedFatality/Qwen3-8B-DSLR-GGUF

Run and chat with the model

lemonade run user.Qwen3-8B-DSLR-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

Domain specific finetune Trained on SLR Translator API calls between Qwen3 32B and the application. Small dataset of 405~ examples.

This is definitely NOT SOTA. more like it works better at this size with this particular interaction than i couldve expected.

LORA training params Rank 256 Alpha 256

Downloads last month: 4

GGUF

Model size

8B params

Architecture

qwen3

Hardware compatibility

Inference Providers NEW

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

Model tree for RenderedFatality/Qwen3-8B-DSLR-GGUF

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Quantized

(374)

this model

RenderedFatality
/

Qwen3-8B-DSLR-GGUF

Model tree for RenderedFatality/Qwen3-8B-DSLR-GGUF

Dataset used to train RenderedFatality/Qwen3-8B-DSLR-GGUF