Instructions to use mobilint/Qwen2-VL-2B-Instruct-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 mobilint/Qwen2-VL-2B-Instruct-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 mobilint/Qwen2-VL-2B-Instruct-GGUF # Run inference directly in the terminal: llama cli -hf mobilint/Qwen2-VL-2B-Instruct-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mobilint/Qwen2-VL-2B-Instruct-GGUF # Run inference directly in the terminal: llama cli -hf mobilint/Qwen2-VL-2B-Instruct-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 mobilint/Qwen2-VL-2B-Instruct-GGUF # Run inference directly in the terminal: ./llama-cli -hf mobilint/Qwen2-VL-2B-Instruct-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 mobilint/Qwen2-VL-2B-Instruct-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf mobilint/Qwen2-VL-2B-Instruct-GGUF
Use Docker
docker model run hf.co/mobilint/Qwen2-VL-2B-Instruct-GGUF
- LM Studio
- Jan
- Ollama
How to use mobilint/Qwen2-VL-2B-Instruct-GGUF with Ollama:
ollama run hf.co/mobilint/Qwen2-VL-2B-Instruct-GGUF
- Unsloth Desktop
- Docker Model Runner
How to use mobilint/Qwen2-VL-2B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/mobilint/Qwen2-VL-2B-Instruct-GGUF
- Lemonade
How to use mobilint/Qwen2-VL-2B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mobilint/Qwen2-VL-2B-Instruct-GGUF
Run and chat with the model
lemonade run user.Qwen2-VL-2B-Instruct-GGUF-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Upload config.json with huggingface_hub
Browse files- config.json +45 -0
config.json
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{
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"architectures": ["MobilintQwen2VLForConditionalGeneration"],
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"model_type": "mobilint-qwen2_vl",
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_size": 1536,
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"intermediate_size": 8960,
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"max_position_embeddings": 4096,
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rope_theta": 1000000.0,
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"vocab_size": 151936,
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"tie_word_embeddings": false,
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"mxq_path": "Qwen2-VL-2B-Instruct_text_model.mxq",
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"gguf_path": "qwen2-vl-2b-instruct-vocab.gguf",
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"embd_path": "target_emb.bin",
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"core_mode": "single",
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"dev_no": 0,
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"target_cores": ["0:0"],
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"vision_start_token_id": 151652,
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"vision_end_token_id": 151653,
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"vision_token_id": 151654,
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"image_token_id": 151655,
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"video_token_id": 151656,
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"vision_config": {
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"depth": 32,
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"embed_dim": 1280,
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"mlp_ratio": 4,
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"mxq_path": "Qwen2-VL-2B-Instruct_vision_transformer.mxq",
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"num_heads": 16,
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"in_chans": 3,
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"hidden_size": 1536,
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"patch_size": 14,
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"spatial_merge_size": 2,
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"spatial_patch_size": 14,
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"temporal_patch_size": 2,
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"core_mode": "multi",
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"target_clusters": [1]
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},
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"rope_scaling": {
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"type": "mrope",
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"mrope_section": [16, 24, 24]
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}
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}
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