Instructions to use cloudraker/rakedoc-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cloudraker/rakedoc-nano with Transformers:

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

pipe = pipeline("image-text-to-text", model="cloudraker/rakedoc-nano")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("cloudraker/rakedoc-nano")
model = AutoModelForMultimodalLM.from_pretrained("cloudraker/rakedoc-nano", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cloudraker/rakedoc-nano with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cloudraker/rakedoc-nano"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloudraker/rakedoc-nano",
		"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/cloudraker/rakedoc-nano

SGLang

How to use cloudraker/rakedoc-nano 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 "cloudraker/rakedoc-nano" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloudraker/rakedoc-nano",
		"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 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 "cloudraker/rakedoc-nano" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloudraker/rakedoc-nano",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use cloudraker/rakedoc-nano with Docker Model Runner:
```
docker model run hf.co/cloudraker/rakedoc-nano
```

rakedoc-nano

A 1.2B-parameter document parser (Qwen2-VL architecture) fine-tuned for table structure. It is a LoRA fine-tune of florin-inc/florin-parser-nano, itself a fine-tune of KDLAI/KDL-Frontier-Parser-nano. Weights are merged; no adapter loading is needed.

This model powers the document parsing pipeline of the CloudRaker Paperwork API.

Lineage and license

Link	Owner	License
Qwen2-VL	Alibaba	Apache-2.0
KDL-Frontier-Parser-nano	KoreaDeep	AGPL-3.0
florin-parser-nano	florin-inc	AGPL-3.0
rakedoc-nano	CloudRaker	AGPL-3.0

AGPL-3.0 is inherited from the KDL weights and applies to this model. See LICENSE and NOTICE.

ParseBench

Official result — mean of 3 full runs on ParseBench main (plus the florin_parser_nano layout-adapter registration fix submitted with the leaderboard PR), single H100 SXM, vllm/vllm-openai:0.28.0, all defaults, LLM normalization off:

Model	Overall	Tables	Charts	Content Faithfulness	Semantic Formatting	Visual Grounding
rakedoc-nano (mean of 3, submitted)	77.23	86.44	64.89	88.84	71.68	74.28

Per-run Overall: 77.24 / 77.23 / 77.21 (run-to-run sigma about 0.015).

Earlier measurement (commit `facdaf02`)

Scores at commit facdaf02, single run, LLM normalization off unless stated.

Model	Overall	Tables	Content Faithfulness	Semantic Formatting	Visual Grounding	Charts
rakedoc-nano	76.04	86.49	87.27	67.89	74.08	64.45
rakedoc-nano, LLM normalization on (Claude Haiku 4.5 judge)	76.04	86.40	87.26	67.87	74.20	64.48
rakedoc-nano, fixed-harness normalization (non-official, see below)	76.06	86.38	87.23	67.85	74.10	64.76

At commit facdaf02 the judge's label normalizer receives no table headers (llm_normalization/postprocess.py:209 passes table_headers=[]), so normalization on and off score the same; both are reported for transparency.

"Fixed-harness" rows use a two-line patch that threads the predicted markdown into the judge and extracts header cells from Markdown and HTML tables (_table_headers_from_content). It is not the official harness and is not comparable to the public leaderboard; it is shown so the effect of the judge bug can be seen (about +0.3 on Charts, nothing elsewhere).

Run-to-run noise measured on this harness: about ±0.1 Overall, ±0.05 Tables, ±0.3 Charts.

Three-pillar mean (Tables, Content Faithfulness, Semantic Formatting), the metric used in Cohere's Parse announcement: rakedoc-nano 80.55 versus Cohere Parse 79.2.

What moved: merged-cell structure. Perfect table record match rose from 0.584 to 0.606 and hard-table GriTS from 0.792 to 0.794. Text, layout, and chart behaviour are unchanged within noise.

Training

LoRA r=16, alpha 32, on language attention and MLP projections; vision tower and projector frozen. 1 epoch, LR 2e-5, cosine schedule, bf16, one H100, about 12 minutes.
Data: 3,000 synthetic table region crops rendered from HTML with exact OTSL targets, calibrated to the ParseBench ground-truth distribution (median 4 columns, hierarchical headers, row and column spans, multi-line cells). No text-stage data; no benchmark data.
The table stage prompt is "\nTable Recognition:\n" and the output is OTSL (<fcel>, <ecel>, <lcel>, <ucel>, <xcel>, <nl>), identical to the base model.

Serving

Drop-in replacement for the base model in the KDL pipeline:

vllm serve CloudRaker/rakedoc-nano --served-model-name kdl-frontier-parser-nano \
  --max-model-len 8192 --gpu-memory-utilization 0.85 --max-num-seqs 24 \
  --trust-remote-code --limit-mm-per-prompt '{"image":1}'

Stage prompts, sampling parameters, and post-processing are those of the ParseBench kdl_frontier_nano pipeline.

Limitations

Charts (64) and layout detection are unchanged from the base model; they were out of scope.
Underline, highlight, and code-block marks are never emitted by the pipeline.
Evaluated on English-heavy financial and insurance documents plus the ParseBench mix; other domains are untested.

Downloads last month: 167

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for cloudraker/rakedoc-nano

Base model

KDLAI/KDL-Frontier-Parser-nano

Finetuned

florin-inc/florin-parser-nano