Recursive Flint vs Baidu Unlimited-OCR-2

Recursive Flint

A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.

Baidu Unlimited-OCR-2

A small MIT document-parsing cousin that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. It needs an NVIDIA GPU with about 12 GB, through Baidu's remote code or its own SGLang build. The risks are running the publisher's code and accuracy figures that are Baidu's own.

Recursive Flint

1. Quickest start is ollama run flint (Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp
2. With llama.cpp use release b11803 or later: llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0, and keep --jinja on
3. Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out
4. Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
5. Strip <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> can add a call
6. Message content must be a string, flatten content-part arrays first
7. Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key

Baidu Unlimited-OCR-2

1. Quickest local run, pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13, then AutoModel.from_pretrained("baidu/Unlimited-OCR-2", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda() and model.infer_multi(tokenizer, image_files=pages, output_path="out")
2. To serve it, download the repository, install wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl with pymupdf and fastokens in a Python 3.10 venv, and start python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --chat-template unlimited-ocr --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --mem-fraction-static 0.7 --max-prefill-tokens 2048 with the SGLANG_UOCR_ variables from the card
3. Send all pages of a document in one request, images first, then the text <PAGE><image> repeated per page and Multi page parsing., with temperature 0, skip_special_tokens false, max_tokens of about 8,000 per page and the DeepseekOCRNoRepeatNGramLogitProcessor (ngram_size 35, window_size 1024 for several pages)
4. The output is layout blocks, not Markdown; run utils/process_unlimitedOCR2.py from github.com/baidu/Unlimited-OCR, or convert(raw_output) in Python, to get Markdown with HTML tables
5. Render PDFs to images first (the card uses PyMuPDF at 300 DPI), keep save_results off for untrusted documents, and treat extracted text as untrusted before passing it to another model