Instructions to use featherless-ai-quants/Qwen-Qwen2-1.5B-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 featherless-ai-quants/Qwen-Qwen2-1.5B-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 featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf featherless-ai-quants/Qwen-Qwen2-1.5B-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 featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf featherless-ai-quants/Qwen-Qwen2-1.5B-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 featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf featherless-ai-quants/Qwen-Qwen2-1.5B-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 featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "featherless-ai-quants/Qwen-Qwen2-1.5B-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": "featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M
- Ollama
How to use featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF with Ollama:
ollama run hf.co/featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF with Docker Model Runner:
docker model run hf.co/featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M
- Lemonade
How to use featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen-Qwen2-1.5B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download Qwen-Qwen2-1.5B-Q6_K.gguf from featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.27 GB
-
https://huggingface.co/featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF/resolve/main/Qwen-Qwen2-1.5B-Q6_K.gguf
- Command line
-
hf download hf://featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF/Qwen-Qwen2-1.5B-Q6_K.gguf
-
curl -L -o Qwen-Qwen2-1.5B-Q6_K.gguf https://huggingface.co/featherless-ai-quants/Qwen-Qwen2-1.5B-GGUF/resolve/main/Qwen-Qwen2-1.5B-Q6_K.gguf
1.27 GB
- Xet hash:
- dd6993e13c69c0060b1fe5abf3cd19ddba9d486e3a026f255e6dc54ccfe9b6b0
- Size of remote file:
- 1.27 GB
- SHA256:
- 2897b6d45271616e0253099716730f7593ea3a957c3c6f9a277c1b993b1346f7
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