Instructions to use smile-forge/unlimited-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use smile-forge/unlimited-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 smile-forge/unlimited-ocr:F16 # Run inference directly in the terminal: llama cli -hf smile-forge/unlimited-ocr:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf smile-forge/unlimited-ocr:F16 # Run inference directly in the terminal: llama cli -hf smile-forge/unlimited-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 smile-forge/unlimited-ocr:F16 # Run inference directly in the terminal: ./llama-cli -hf smile-forge/unlimited-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 smile-forge/unlimited-ocr:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf smile-forge/unlimited-ocr:F16
Use Docker
docker model run hf.co/smile-forge/unlimited-ocr:F16
- Llama new
- LM Studio
- Jan
- vLLM
How to use smile-forge/unlimited-ocr with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "smile-forge/unlimited-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": "smile-forge/unlimited-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/smile-forge/unlimited-ocr:F16
- Ollama
How to use smile-forge/unlimited-ocr with Ollama:
ollama run hf.co/smile-forge/unlimited-ocr:F16
- Unsloth Desktop
- Docker Model Runner
How to use smile-forge/unlimited-ocr with Docker Model Runner:
docker model run hf.co/smile-forge/unlimited-ocr:F16
- Lemonade
How to use smile-forge/unlimited-ocr with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smile-forge/unlimited-ocr:F16
Run and chat with the model
lemonade run user.unlimited-ocr-F16
List all available models
lemonade list
- Atomic Chat
File size: 3,427 Bytes
81bd5e5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | """The sample pages the reference is made from, all from Unlimited-OCR's own paper (MIT, in the makers' repository):
paper-p1, paper-p3, paper-p5 three of its pages at 200 dpi — a title page with a figure, a page of prose and maths,
a page with figures and their captions
small-print its text from pages 3 to 9 set on one page in small print: a page long enough that the
model writes past its 128-token window many times over
two-columns its text from pages 10 to 12 in two columns, scanned: a page read across its columns
no-text shapes and gradients with no writing, for the text finder
make_samples.py <Unlimited-OCR.pdf> <out folder> <a TrueType font>"""
import io
import random
import sys
from pathlib import Path
import pymupdf
from PIL import Image, ImageDraw, ImageFilter, ImageFont
pdf, out, font_path = Path(sys.argv[1]), Path(sys.argv[2]), sys.argv[3]
out.mkdir(parents=True, exist_ok=True)
doc = pymupdf.open(str(pdf))
for number in (1, 3, 5):
doc[number - 1].get_pixmap(matrix=pymupdf.Matrix(200 / 72, 200 / 72)).save(str(out / f"paper-p{number}.png"))
def words(first, last):
return " ".join(" ".join(doc[i - 1].get_text().split()) for i in range(first, last + 1)).split()
def wrap(draw, ws, font, width):
lines, line = [], ""
for word in ws:
trial = f"{line} {word}".strip()
if draw.textlength(trial, font=font) <= width:
line = trial
else:
lines.append(line)
line = word
return lines + ([line] if line else [])
page = Image.new("RGB", (2480, 3508), "white")
draw = ImageDraw.Draw(page)
font = ImageFont.truetype(font_path, 30)
y = 140
for line in wrap(draw, words(3, 9), font, 2200)[:88]:
draw.text((140, y), line, fill="black", font=font)
y += 37
page.save(out / "small-print.png")
random.seed(7)
page = Image.new("RGB", (1700, 2200), "white")
draw = ImageDraw.Draw(page)
font = ImageFont.truetype(font_path, 26)
lines = wrap(draw, words(10, 12), font, 700)
for column, x in ((lines[:62], 100), (lines[62:124], 900)):
y = 120
for line in column:
draw.text((x, y), line, fill="black", font=font)
y += 32
page = page.rotate(0.6, resample=Image.BICUBIC, fillcolor=(250, 250, 248)).filter(ImageFilter.GaussianBlur(0.6))
pixels = page.load()
for _ in range(page.width * page.height // 60):
px, py = random.randrange(page.width), random.randrange(page.height)
v = max(0, min(255, pixels[px, py][0] + random.randint(-30, 30)))
pixels[px, py] = (v, v, v)
buffer = io.BytesIO()
page.convert("L").save(buffer, "JPEG", quality=60)
Image.open(io.BytesIO(buffer.getvalue())).convert("RGB").save(out / "two-columns.png")
page = Image.new("RGB", (1600, 1200))
draw = ImageDraw.Draw(page)
for row in range(1200):
draw.line([(0, row), (1600, row)], fill=(40 + row // 8, 90 + row // 12, 160 - row // 10))
for i in range(14):
cx, cy, r = random.randrange(1600), random.randrange(1200), random.randrange(30, 220)
draw.ellipse([cx - r, cy - r, cx + r, cy + r], fill=(random.randrange(256), random.randrange(256), random.randrange(256)))
page.filter(ImageFilter.GaussianBlur(3)).save(out / "no-text.png")
print(sorted(p.name for p in out.iterdir()))
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