Instructions to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF",
	filename="Qwen2-VL-OCR-2B-Instruct.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M

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 prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M

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 prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-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 prithivMLmods/Qwen2-VL-OCR-2B-Instruct-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 prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF to start chatting

Docker Model Runner
How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Qwen2-VL-OCR-2B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2-VL-OCR-2B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Qwen2-VL-OCR-2B-Instruct-GGUF [ VL / OCR ]

The Qwen2-VL-OCR-2B-Instruct model is a fine-tuned version of Qwen/Qwen2-VL-2B-Instruct, tailored for tasks that involve Optical Character Recognition (OCR), image-to-text conversion, math problem solving with LaTeX formatting and Messy Handwriting OCR. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.

Model Files (Qwen2-VL-OCR-2B-Instruct, GGUF)

File Name	Size	Quantization	Format	Description
`Qwen2-VL-OCR-2B-Instruct.f16.gguf`	3.09 GB	FP16	GGUF	Full precision (float16)
`Qwen2-VL-OCR-2B-Instruct.Q2_K.gguf`	676 MB	Q2_K	GGUF	2-bit quantized
`Qwen2-VL-OCR-2B-Instruct.Q3_K_L.gguf`	880 MB	Q3_K_L	GGUF	3-bit quantized (K L variant)
`Qwen2-VL-OCR-2B-Instruct.Q3_K_M.gguf`	824 MB	Q3_K_M	GGUF	3-bit quantized (K M variant)
`Qwen2-VL-OCR-2B-Instruct.Q3_K_S.gguf`	761 MB	Q3_K_S	GGUF	3-bit quantized (K S variant)
`Qwen2-VL-OCR-2B-Instruct.Q4_K_M.gguf`	986 MB	Q4_K_M	GGUF	4-bit quantized (K M variant)
`Qwen2-VL-OCR-2B-Instruct.Q4_K_S.gguf`	940 MB	Q4_K_S	GGUF	4-bit quantized (K S variant)
`Qwen2-VL-OCR-2B-Instruct.Q5_K_M.gguf`	1.13 GB	Q5_K_M	GGUF	5-bit quantized (K M variant)
`Qwen2-VL-OCR-2B-Instruct.Q5_K_S.gguf`	1.1 GB	Q5_K_S	GGUF	5-bit quantized (K S variant)
`Qwen2-VL-OCR-2B-Instruct.Q6_K.gguf`	1.27 GB	Q6_K	GGUF	6-bit quantized
`Qwen2-VL-OCR-2B-Instruct.Q8_0.gguf`	1.65 GB	Q8_0	GGUF	8-bit quantized

i1 Quantized Variants

File Name	Size	Quantization	Description
`Qwen2-VL-OCR-2B-Instruct.i1-IQ1_M.gguf`	464 MB	i1-IQ1_M	i1 1-bit medium
`Qwen2-VL-OCR-2B-Instruct.i1-IQ1_S.gguf`	437 MB	i1-IQ1_S	i1 1-bit small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ2_M.gguf`	601 MB	i1-IQ2_M	i1 2-bit medium
`Qwen2-VL-OCR-2B-Instruct.i1-IQ2_S.gguf`	564 MB	i1-IQ2_S	i1 2-bit small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ2_XS.gguf`	550 MB	i1-IQ2_XS	i1 2-bit extra small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ2_XXS.gguf`	511 MB	i1-IQ2_XXS	i1 2-bit extra extra small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ3_M.gguf`	777 MB	i1-IQ3_M	i1 3-bit medium
`Qwen2-VL-OCR-2B-Instruct.i1-IQ3_S.gguf`	762 MB	i1-IQ3_S	i1 3-bit small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ3_XS.gguf`	732 MB	i1-IQ3_XS	i1 3-bit extra small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ3_XXS.gguf`	669 MB	i1-IQ3_XXS	i1 3-bit extra extra small
`Qwen2-VL-OCR-2B-Instruct.i1-IQ4_NL.gguf`	936 MB	i1-IQ4_NL	i1 4-bit with no-layernorm quantization
`Qwen2-VL-OCR-2B-Instruct.i1-IQ4_XS.gguf`	896 MB	i1-IQ4_XS	i1 4-bit extra small
`Qwen2-VL-OCR-2B-Instruct.i1-Q4_0.gguf`	938 MB	i1-Q4_0	i1 4-bit traditional quant
`Qwen2-VL-OCR-2B-Instruct.i1-Q4_1.gguf`	1.02 GB	i1-Q4_1	i1 4-bit traditional variant

Metadata

File Name	Size	Description
`.gitattributes`	3.37 kB	Git LFS tracking file
`config.json`	34 B	Config placeholder
`README.md`	672 B	Model readme

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	0.4
GGUF	Q3_K_S	0.5
GGUF	Q3_K_M	0.5	lower quality
GGUF	Q3_K_L	0.5
GGUF	IQ4_XS	0.6
GGUF	Q4_K_S	0.6	fast, recommended
GGUF	Q4_K_M	0.6	fast, recommended
GGUF	Q5_K_S	0.6
GGUF	Q5_K_M	0.7
GGUF	Q6_K	0.7	very good quality
GGUF	Q8_0	0.9	fast, best quality
GGUF	f16	1.6	16 bpw, overkill