Instructions to use mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF with Transformers:

# Use a pipeline as a high-level helper
# Warning: Pipeline type "image-to-text" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# 'pip install "transformers<5.0.0'
from transformers import pipeline

pipe = pipeline("image-to-text", model="mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Gliese-OCR-7B-Post2.0-final-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 mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Gliese-OCR-7B-Post2.0-final-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 mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/Gliese-OCR-7B-Post2.0-final-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 mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Gliese-OCR-7B-Post2.0-final-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 mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M
```

Unsloth Studio

How to use mradermacher/Gliese-OCR-7B-Post2.0-final-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 mradermacher/Gliese-OCR-7B-Post2.0-final-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 mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF to start chatting

Docker Model Runner
How to use mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Gliese-OCR-7B-Post2.0-final-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gliese-OCR-7B-Post2.0-final-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Gliese-OCR-7B-Post2.0-final-GGUF / README.md

mradermacher

auto-patch README.md

10ac7a2 verified 10 months ago

3.85 kB

base_model: prithivMLmods/Gliese-OCR-7B-Post2.0-final
language:
  - en
  - zh
library_name: transformers
license: apache-2.0
mradermacher:
  readme_rev: 1
quantized_by: mradermacher
tags:
  - trl
  - Document
  - VLM
  - KIE
  - OCR
  - VL
  - Camel
  - Openpdf
  - text-generation-inference
  - Extraction
  - Linking
  - Markdown
  - .Md
  - Document Digitization
  - Intelligent Document Processing (IDP)
  - Intelligent Word Recognition (IWR)
  - Optical Mark Recognition (OMR)

About

static quants of https://huggingface.co/prithivMLmods/Gliese-OCR-7B-Post2.0-final

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Gliese-OCR-7B-Post2.0-final-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

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

Link	Type	Size/GB	Notes
GGUF	mmproj-Q8_0	1.0	multi-modal supplement
GGUF	mmproj-f16	1.5	multi-modal supplement
GGUF	Q2_K	3.1
GGUF	Q3_K_S	3.6
GGUF	Q3_K_M	3.9	lower quality
GGUF	Q3_K_L	4.2
GGUF	Q4_K_S	4.6	fast, recommended
GGUF	Q6_K	6.4	very good quality
GGUF	Q8_0	8.2	fast, best quality
GGUF	f16	15.3	16 bpw, overkill

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

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.