nanonets/Nanonets-OCR-s
LoRA-style vs Qwen/Qwen2.5-VL-3B-Instruct. Looks like a merged LoRA (or a fine-tune with frozen norms): only the weight matrices of down_proj, gate_proj, k_proj, o_proj, proj, q_proj, qkv, up_proj, v_proj changed, in 36 of 36 layers; norms, biases and embeddings are untouched.
Tensors changed: Embeddings 0/2, Norms 0/138, MLP 204/304, Attention 208/380
9 derivatives checked
- unsloth/Nanonets-OCR-s-GGUF: Same weights, converted (fewer layers)
- prithivMLmods/Nanonets-OCR-s-AIO-GGUF: Same weights, converted (fewer layers)
- mradermacher/Nanonets-OCR-s-i1-GGUF: Quantization (fewer layers)
- mradermacher/Nanonets-OCR-s-GGUF: Same weights, converted (fewer layers)
- unsloth/Nanonets-OCR-s: Identical copy
- DevQuasar/nanonets.Nanonets-OCR-s-GGUF: Quantization (fewer layers)
- benhaotang/Nanonets-OCR-s-GGUF: Same weights, converted (fewer layers)
- ReallyFloppyPenguin/Nanonets-OCR-s-GGUF: Same weights, converted (fewer layers)
- omarabb315/OCR__merged_nanonets_3b: Full fine-tune
4 other repos hold byte-for-byte identical weight files.