Instructions to use cmp-nct/Yi-VL-34B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cmp-nct/Yi-VL-34B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cmp-nct/Yi-VL-34B-GGUF",
	filename="ggml-model-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

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 Studio

How to use cmp-nct/Yi-VL-34B-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 cmp-nct/Yi-VL-34B-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 cmp-nct/Yi-VL-34B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cmp-nct/Yi-VL-34B-GGUF to start chatting

Atomic Chat new
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

YAML Metadata Warning:empty or missing yaml metadata in repo card

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.

Downloads last month: 44

GGUF

Model size

34B params

Architecture

llama

Hardware compatibility

2-bit

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