Instructions to use prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF with Transformers:

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

pipe = pipeline("image-text-to-text", model="prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF",
	filename="Camel-Doc-OCR-062825.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/Camel-Doc-OCR-062825-mmp-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 prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-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/Camel-Doc-OCR-062825-mmp-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Camel-Doc-OCR-062825-mmp-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Camel-Doc-OCR-062825-mmp-GGUF-Q4_K_M

List all available models

lemonade list

Camel-Doc-OCR-062825-mmp-GGUF

File size: 1,949 Bytes

---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Camel-Doc-OCR-062825
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- doc
- ocr
---

# **Camel-Doc-OCR-062825-mmp-GGUF**

> The Camel-Doc-OCR-062825 model is a fine-tuned version of Qwen2.5-VL-7B-Instruct, optimized for Document Retrieval, Content Extraction, and Analysis Recognition. Built on top of the Qwen2.5-VL architecture, this model enhances document comprehension capabilities with focused training on the Opendoc2-Analysis-Recognition dataset for superior document analysis and information extraction tasks.

## Model Files

| File Name | Size | Type | Description |
|-----------|------|------|-------------|
| Camel-Doc-OCR-062825.Q2_K.gguf | 3.02 GB | Model | Q2_K quantized model |
| Camel-Doc-OCR-062825.Q3_K_M.gguf | 3.81 GB | Model | Q3_K_M quantized model |
| Camel-Doc-OCR-062825.Q4_K_M.gguf | 4.68 GB | Model | Q4_K_M quantized model |
| Camel-Doc-OCR-062825.Q5_K_M.gguf | 5.44 GB | Model | Q5_K_M quantized model |
| Camel-Doc-OCR-062825.Q6_K.gguf | 6.25 GB | Model | Q6_K quantized model |
| Camel-Doc-OCR-062825.Q8_0.gguf | 8.1 GB | Model | Q8_0 quantized model |
| Camel-Doc-OCR-062825.f16.gguf | 15.2 GB | Model | Full precision f16 model |
| Camel-Doc-OCR-062825.mmproj-Q8_0.gguf | 853 MB | Projection | Q8_0 multimodal projection |
| Camel-Doc-OCR-062825.mmproj-f16.gguf | 1.35 GB | Projection | f16 multimodal projection |
| .gitattributes | 2.14 kB | Config | Git LFS configuration |
| config.json | 36 Bytes | Config | Model configuration |
| README.md | 633 Bytes | Documentation | Repository documentation |

## Quants Usage 

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

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)