Instructions to use llmware/minicpm-2.6-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 llmware/minicpm-2.6-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 llmware/minicpm-2.6-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf llmware/minicpm-2.6-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 llmware/minicpm-2.6-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf llmware/minicpm-2.6-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 llmware/minicpm-2.6-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf llmware/minicpm-2.6-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 llmware/minicpm-2.6-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf llmware/minicpm-2.6-gguf:Q4_K_M
Use Docker
docker model run hf.co/llmware/minicpm-2.6-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use llmware/minicpm-2.6-gguf with Ollama:
ollama run hf.co/llmware/minicpm-2.6-gguf:Q4_K_M
- Unsloth Studio
How to use llmware/minicpm-2.6-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 llmware/minicpm-2.6-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 llmware/minicpm-2.6-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for llmware/minicpm-2.6-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use llmware/minicpm-2.6-gguf with Docker Model Runner:
docker model run hf.co/llmware/minicpm-2.6-gguf:Q4_K_M
- Lemonade
How to use llmware/minicpm-2.6-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull llmware/minicpm-2.6-gguf:Q4_K_M
Run and chat with the model
lemonade run user.minicpm-2.6-gguf-Q4_K_M
List all available models
lemonade list
Upload 2 files
Browse files- README.md +33 -3
- hash_record_sha256.json +5 -0
README.md
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---
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license: apache-2.0
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---
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license: apache-2.0
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inference: false
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base_model: openbmb/MiniCPM-V-2_6
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base_model_relation: quantized
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tags: [green, llmware-chat, p7, gguf]
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---
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# minicpm-2.6-gguf
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<!-- Provide a quick summary of what the model is/does. -->
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**minicpm-2.6-gguf** is a GGUF Q4_K_M quantized version of [MiniCPM-2.6](https://www.huggingface.co/openbmb/MiniCPM-V-2_6), providing a fast, small inference implementation, optimized for AI PCs.
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### Model Description
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- **Developed by:** openbmb
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- **Quantized by:** bartowksi
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- **Model type:** minicpmv
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- **Parameters:** 7 billion
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- **Model Parent:** openbmb/MiniCPM-V-2_6
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Uses:** Chat, general-purpose LLM
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- **Quantization:** int4
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## Model Card Contact
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[llmware on hf](https://www.huggingface.co/llmware)
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[llmware website](https://www.llmware.ai)
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{
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"mmproj-model-f16-2.gguf": "99c55b4f5fb463098d936e17039387b54ba52f06849248f4d38e69da8fd1d307",
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"MiniCPM-V-2_6-Q4_K_M.gguf": "f0f3af06f4204a4587bf203d2afcd49333242a593d36b9eccf07b0351c77c504",
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"time_stamp": "2025-12-28_113143"
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}
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