Instructions to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr 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 Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16
# Run inference directly in the terminal:
llama cli -hf Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16
# Run inference directly in the terminal:
llama cli -hf Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

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 Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16
# Run inference directly in the terminal:
./llama-cli -hf Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

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 Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

Use Docker

docker model run hf.co/Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

LM Studio
Jan

vLLM

How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Abhay2310/qwen_vlm_0.8B_finetune_math_ocr"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Abhay2310/qwen_vlm_0.8B_finetune_math_ocr",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

Ollama
How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr with Ollama:
```
ollama run hf.co/Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16
```

Unsloth Studio

How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr 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 Abhay2310/qwen_vlm_0.8B_finetune_math_ocr 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 Abhay2310/qwen_vlm_0.8B_finetune_math_ocr to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Abhay2310/qwen_vlm_0.8B_finetune_math_ocr to start chatting

How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

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": "Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

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 Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr with Docker Model Runner:
```
docker model run hf.co/Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16
```

Lemonade

How to use Abhay2310/qwen_vlm_0.8B_finetune_math_ocr with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Abhay2310/qwen_vlm_0.8B_finetune_math_ocr:F16

Run and chat with the model

lemonade run user.qwen_vlm_0.8B_finetune_math_ocr-F16

List all available models

lemonade list

Qwen3.5-0.8B VLM — Equation Image → LaTeX

A fine-tune of unsloth/Qwen3.5-0.8B that transcribes images of mathematical equations into LaTeX. Quantized to Q4_K_M for CPU inference.

Files

File	Size	Purpose
`Qwen3.5-0.8B.Q4_K_M.gguf`	542 MB	Quantized language model
`Qwen3.5-0.8B.F16-mmproj.gguf`	205 MB	Vision projector — required

Both files are needed. Loading the model without --mmproj gives you a text-only model with no ability to see images.

Usage

With llama.cpp:

llama-mtmd-cli \
  -m Qwen3.5-0.8B.Q4_K_M.gguf \
  --mmproj Qwen3.5-0.8B.F16-mmproj.gguf \
  --image equation.png \
  -p "Convert the equation in the image to LaTeX." \
  --temp 0.1

Low temperature is recommended — transcription is a deterministic task, and sampling introduces errors in brace matching and command names.

Training

Fine-tuned with Unsloth using LoRA, then merged to 16-bit and converted to GGUF.

Limitations

0.8B parameters at 4-bit is a small, heavily compressed model. Expect errors on dense or unusual notation.
Best results on clean screenshots; handwriting and low-resolution photos are harder.
Output may need brace-matching cleanup before it compiles.

Downloads last month: 23

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for Abhay2310/qwen_vlm_0.8B_finetune_math_ocr

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

unsloth/Qwen3.5-0.8B

Quantized

(14)

this model