Instructions to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-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/rinna-qwen2.5-bakeneko-32b-instruct-v2-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/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-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/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-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/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-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/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-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/rinna-qwen2.5-bakeneko-32b-instruct-v2-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/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
- Ollama
How to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF with Ollama:
ollama run hf.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
- Unsloth Studio
How to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-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 featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-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 featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF to start chatting
- Pi
How to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF with Docker Model Runner:
docker model run hf.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
- Lemonade
How to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Upload folder using huggingface_hub
Browse files- .gitattributes +24 -0
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---
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base_model: rinna/qwen2.5-bakeneko-32b-instruct-v2
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pipeline_tag: text-generation
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quantized_by: featherless-ai-quants
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---
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# rinna/qwen2.5-bakeneko-32b-instruct-v2 GGUF Quantizations 🚀
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*Optimized GGUF quantization files for enhanced model performance*
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> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
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---
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## Available Quantizations 📊
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| Quantization Type | File | Size |
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|-------------------|------|------|
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| IQ4_XS | [rinna-qwen2.5-bakeneko-32b-instruct-v2-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-IQ4_XS.gguf) | 17042.26 MB |
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| Q2_K | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q2_K.gguf) | 11742.69 MB |
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| Q3_K_L | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q3_K_L.gguf) | 16448.10 MB |
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| Q3_K_M | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q3_K_M.gguf) | 15196.85 MB |
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| Q3_K_S | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q3_K_S.gguf) | 13725.60 MB |
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| Q4_K_M | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q4_K_M.gguf) | 18931.71 MB |
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| Q4_K_S | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q4_K_S.gguf) | 17914.21 MB |
|
| 27 |
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| Q5_K_M | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q5_K_M.gguf) | 22184.52 MB |
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| Q5_K_S | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/blob/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q5_K_S.gguf) | 21589.52 MB |
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| Q6_K | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q6_K](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/tree/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q6_K) | 25640.64 MB (folder) |
|
| 30 |
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| Q8_0 | [rinna-qwen2.5-bakeneko-32b-instruct-v2-Q8_0](https://huggingface.co/featherless-ai-quants/rinna-qwen2.5-bakeneko-32b-instruct-v2-GGUF/tree/main/rinna-qwen2.5-bakeneko-32b-instruct-v2-Q8_0) | 33207.79 MB (folder) |
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---
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## ⚡ Powered by [Featherless AI](https://featherless.ai)
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### Key Features
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- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
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- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
|
| 41 |
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- 📚 **Vast Compatibility** - Support for 2400+ models and counting
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- 💎 **Affordable Pricing** - Starting at just $10/month
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---
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**Links:**
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[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
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Git LFS Details
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