Instructions to use cmp-nct/Yi-VL-34B-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 cmp-nct/Yi-VL-34B-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 cmp-nct/Yi-VL-34B-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf cmp-nct/Yi-VL-34B-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf cmp-nct/Yi-VL-34B-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf cmp-nct/Yi-VL-34B-GGUF:Q2_K
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 cmp-nct/Yi-VL-34B-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf cmp-nct/Yi-VL-34B-GGUF:Q2_K
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 cmp-nct/Yi-VL-34B-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf cmp-nct/Yi-VL-34B-GGUF:Q2_K
Use Docker
docker model run hf.co/cmp-nct/Yi-VL-34B-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use cmp-nct/Yi-VL-34B-GGUF with Ollama:
ollama run hf.co/cmp-nct/Yi-VL-34B-GGUF:Q2_K
- Unsloth Desktop
- Docker Model Runner
How to use cmp-nct/Yi-VL-34B-GGUF with Docker Model Runner:
docker model run hf.co/cmp-nct/Yi-VL-34B-GGUF:Q2_K
- Lemonade
How to use cmp-nct/Yi-VL-34B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cmp-nct/Yi-VL-34B-GGUF:Q2_K
Run and chat with the model
lemonade run user.Yi-VL-34B-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
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Check out the documentation for more information.
This is a quantization of Yi-VL-34B and of the visual transformer.
You currently need to apply this PR to make it work: https://github.com/ggerganov/llama.cpp/pull/5093 - this adds the additional normalization steps into the projection
Yi-Vl-34B is prone to hallucinations, to me it appears like a rushed release. Something did not go right in training. However, while 6B was the 2nd worst llava-model I've tested, the 34B did show some strengths.
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