Instructions to use prapaa/eastrus-vl-qwen3-8b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prapaa/eastrus-vl-qwen3-8b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prapaa/eastrus-vl-qwen3-8b-gguf",
	filename="qwen3-vl-8b-instruct.BF16-mmproj.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 prapaa/eastrus-vl-qwen3-8b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prapaa/eastrus-vl-qwen3-8b-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf prapaa/eastrus-vl-qwen3-8b-gguf:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prapaa/eastrus-vl-qwen3-8b-gguf:BF16
# Run inference directly in the terminal:
llama-cli -hf prapaa/eastrus-vl-qwen3-8b-gguf:BF16

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 prapaa/eastrus-vl-qwen3-8b-gguf:BF16
# Run inference directly in the terminal:
./llama-cli -hf prapaa/eastrus-vl-qwen3-8b-gguf:BF16

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 prapaa/eastrus-vl-qwen3-8b-gguf:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prapaa/eastrus-vl-qwen3-8b-gguf:BF16

Use Docker

docker model run hf.co/prapaa/eastrus-vl-qwen3-8b-gguf:BF16

LM Studio
Jan
Ollama
How to use prapaa/eastrus-vl-qwen3-8b-gguf with Ollama:
```
ollama run hf.co/prapaa/eastrus-vl-qwen3-8b-gguf:BF16
```

Unsloth Studio

How to use prapaa/eastrus-vl-qwen3-8b-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 prapaa/eastrus-vl-qwen3-8b-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 prapaa/eastrus-vl-qwen3-8b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prapaa/eastrus-vl-qwen3-8b-gguf to start chatting

How to use prapaa/eastrus-vl-qwen3-8b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prapaa/eastrus-vl-qwen3-8b-gguf:BF16

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": "prapaa/eastrus-vl-qwen3-8b-gguf:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prapaa/eastrus-vl-qwen3-8b-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prapaa/eastrus-vl-qwen3-8b-gguf:BF16

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 prapaa/eastrus-vl-qwen3-8b-gguf:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use prapaa/eastrus-vl-qwen3-8b-gguf with Docker Model Runner:
```
docker model run hf.co/prapaa/eastrus-vl-qwen3-8b-gguf:BF16
```

Lemonade

How to use prapaa/eastrus-vl-qwen3-8b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prapaa/eastrus-vl-qwen3-8b-gguf:BF16

Run and chat with the model

lemonade run user.eastrus-vl-qwen3-8b-gguf-BF16

List all available models

lemonade list

eastrus-vl-qwen3-8b-gguf / README.md

somu9

Set model card

67b5d0b verified 3 months ago

preview code

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	---
	tags:
	- gguf
	- llama.cpp
	- unsloth
	- vision-language-model
	- qwen3-vl
	library_name: llama.cpp
	pipeline_tag: visual-question-answering
	license: apache-2.0
	datasets:
	- prapaa/eastrus-vl
	language:
	- en
	base_model:
	- unsloth/Qwen3-VL-8B-Instruct-unsloth-bnb-4bit
	---

	# eastrus-vl-qwen3-8b-gguf

	A GGUF-exported multimodal (vision-language) model based on Qwen3-VL-8B-Instruct, fine-tuned for cattle estrus–related vulval image assessment. The model is intended to produce symptom-by-symptom observations and a single confidence-style score, rather than a hard binary decision.

	This repo contains:
	- A quantized GGUF model for inference (`Q4_K_M`)
	- A matching multimodal projection file (`mmproj`) required by `llama.cpp` for vision inputs

	## Model details
	- Base model: Qwen3-VL-8B-Instruct (VLM)
	- Fine-tuning: Unsloth (LoRA / efficient finetuning workflow)
	- Export: `llama.cpp` GGUF conversion via Unsloth
	- Quantization: `Q4_K_M` (balanced quality/speed)

	## Intended use
	- Primary: Assistive analysis of cattle vulval imagery for estrus-related visual signs (educational/research workflow support).
	- Not a medical device: Outputs should not be used as the sole basis for veterinary diagnosis, treatment, or critical farm management decisions.

	## Output format (what you should expect)
	The model is trained to generate:
	- A structured, human-readable symptom list (mucus color, swelling severity, redness severity, moisture level, mucus viscosity, tissue turgidity)
	- A single confidence-style score (0–100%)
	- A JSON structured summary of all observed symptoms

	## How to run (llama.cpp)

	### 1) Text-only prompt (sanity check)
	```bash
	llama-cli -hf prapaa/eastrus-vl-qwen3-8b-gguf --jinja -p "Is there eastrus?"
	```