Image-to-Text
Transformers
Safetensors
dots_ocr
text-generation
ocr
vision
quantized
awq
4bit
custom_code
compressed-tensors
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("sugam24/dots-ocr-awq-4bit", trust_remote_code=True, dtype="auto")Quick Links
dots.ocr AWQ 4-bit Quantized
This is a 4-bit AWQ quantized version of rednote-hilab/dots.ocr.
Model Details
- Base Model: rednote-hilab/dots.ocr
- Quantization: W4A16 (4-bit weights, 16-bit activations)
- Method: llm-compressor
- Size: ~1.5GB (reduced from ~6GB)
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
model = AutoModelForCausalLM.from_pretrained(
"sugam24/dots-ocr-awq-4bit",
trust_remote_code=True,
device_map="cuda"
)
tokenizer = AutoTokenizer.from_pretrained("sugam24/dots-ocr-awq-4bit", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("sugam24/dots-ocr-awq-4bit", trust_remote_code=True)
License
Same as the base model (Apache 2.0).
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Model tree for sugam24/dots-ocr-awq-4bit
Base model
rednote-hilab/dots.ocr
# 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="sugam24/dots-ocr-awq-4bit", trust_remote_code=True)