Instructions to use alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-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 alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-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 alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-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 alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-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 alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M
Use Docker
docker model run hf.co/alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf with Ollama:
ollama run hf.co/alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf with Docker Model Runner:
docker model run hf.co/alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M
- Lemonade
How to use alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3-0.6B-Base-D2lang-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download unsloth.Q4_K_M.gguf from alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf: direct link, hf CLI and curl.
- Browser
- Download file 397 MB
-
https://huggingface.co/alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf/resolve/main/unsloth.Q4_K_M.gguf
- Command line
-
hf download hf://alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf/unsloth.Q4_K_M.gguf
-
curl -L -o unsloth.Q4_K_M.gguf https://huggingface.co/alfonsogarciacaro/Qwen3-0.6B-Base-D2lang-gguf/resolve/main/unsloth.Q4_K_M.gguf
397 MB
- Xet hash:
- 35a8f242fef058e0ca063826c292cfee9037fbbd2cfdd30b73a0f9c3220b8bd3
- Size of remote file:
- 397 MB
- SHA256:
- efa1f123be9bb55c640658fcfdce2ec535d4ba490169d502c172e59da5e6b8f8
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