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Model: occ-ai/OCC-RAG-1.7B-GGUF
Source: Original Platform

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README.md

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+---
+license: mit
+language:
+- en
+- ru
+library_name: gguf
+pipeline_tag: text-generation
+base_model:
+- occ-ai/OCC-RAG-1.7B
+tags:
+- rag
+- faithful-qa
+- occ
+- gguf
+- llama.cpp
+- quantized
+---
+
+# OCC-RAG-1.7B-GGUF
+
+<p align="center">
+  <img src="figures/occ.png" alt="OCC-RAG" width="320"/>
+</p>
+
+<p align="center">
+  <a href="https://github.com/optimal-cognitive-core/OCC-RAG"><img src="figures/github-mark.png" alt=""/>&nbsp;<b>GitHub</b></a> &nbsp;|&nbsp;
+  <a href="https://arxiv.org/abs/2606.00683"><b>Technical Report</b></a> &nbsp;|&nbsp;
+  <a href="https://cloud.ru/products/evolution-ml-inference"><b>Cloud</b></a> &nbsp;|&nbsp;
+  <a href="https://huggingface.co/occ-ai/OCC-RAG-1.7B"><b>Base model</b></a>
+</p>
+
+**GGUF quantizations of [`occ-ai/OCC-RAG-1.7B`](https://huggingface.co/occ-ai/OCC-RAG-1.7B)**
+for native inference with [llama.cpp](https://github.com/ggml-org/llama.cpp),
+[Ollama](https://ollama.com/), [LM Studio](https://lmstudio.ai/), and other
+GGUF-compatible runtimes.
+
+OCC-RAG-1.7B is a 1.7B-parameter small language model specialized for **faithful,
+context-grounded question answering**: given a question and a set of sources, it produces
+a structured reasoning trace with explicit source citations, decides whether the context
+supports an answer, and either answers from the context or abstains. It attains the **best
+faithfulness across all evaluated scales (up to 32B)**. See the
+[base model card](https://huggingface.co/occ-ai/OCC-RAG-1.7B) for training details and
+benchmarks.
+
+## Files
+
+The underlying architecture is Qwen3 (1.7B). The chat template is embedded in the GGUF,
+so `llama.cpp`/Ollama apply it automatically.
+
+| File | Quant | Size | Notes |
+|---|---|---|---|
+| `OCC-RAG-1.7B-Q4_0.gguf` | Q4_0 | 1.05 GB | 4-bit, legacy — smallest |
+| `OCC-RAG-1.7B-Q4_K_M.gguf` | Q4_K_M | 1.11 GB | 4-bit K-quant — **recommended balance** |
+| `OCC-RAG-1.7B-Q5_K_M.gguf` | Q5_K_M | 1.26 GB | 5-bit K-quant |
+| `OCC-RAG-1.7B-Q6_K.gguf` | Q6_K | 1.42 GB | 6-bit K-quant |
+| `OCC-RAG-1.7B-Q8_0.gguf` | Q8_0 | 1.83 GB | 8-bit — near-lossless |
+| `OCC-RAG-1.7B-F16.gguf` | F16 | 3.45 GB | 16-bit float (full precision) |
+| `OCC-RAG-1.7B-BF16.gguf` | BF16 | 3.45 GB | 16-bit bfloat (lossless base) |
+
+For most uses pick **Q4_K_M** (smallest good quality) or **Q8_0** (highest quality at
+under 2 GB). All quants are derived from the BF16 base. For an even smaller footprint or
+in-browser use, see [`occ-ai/OCC-RAG-0.6B-GGUF`](https://huggingface.co/occ-ai/OCC-RAG-0.6B-GGUF).
+
+## Usage — llama.cpp
+
+```bash
+# Run directly from the Hub (downloads the chosen quant)
+llama-cli -hf occ-ai/OCC-RAG-1.7B-GGUF:Q4_K_M -p "Hello" -no-cnv
+
+# Or download a file and run it
+llama-cli -m OCC-RAG-1.7B-Q4_K_M.gguf -p "Hello" -no-cnv
+# (newer llama.cpp: use `llama-completion` for non-interactive runs)
+```
+
+## Usage — Ollama
+
+```bash
+ollama run hf.co/occ-ai/OCC-RAG-1.7B-GGUF:Q4_K_M
+```
+
+## Input / output format
+
+OCC-RAG uses a **structured RAG prompt with special tokens**: the question is wrapped in
+`<|query_start|> … <|query_end|>` and each source in
+`<|source_start|><|source_id|>N … <|source_end|>`. The response has five sections —
+**query analysis → source analysis → reasoning → status (`ANSWERABLE` / `UNANSWERABLE`)
+→ answer** — and the final answer is in `<|answer_start|> … <|answer_end|>`.
+
+The embedded chat template (apply with llama.cpp's `--jinja`) builds the query/source
+tokens for you when sources are supplied as documents; alternatively assemble the tokens
+manually. See the [base model card](https://huggingface.co/occ-ai/OCC-RAG-1.7B) for the
+full format and a runnable example.
+
+> We recommend **greedy decoding** (`--temp 0`), the training/evaluation default.
+
+## Limitations
+
+- **Context-grounded only.** Trained to answer from the supplied sources and to ignore
+  parametric knowledge — not a general-purpose chat or knowledge model.
+- **Reasoning depth.** Training/evaluation are capped at three-hop reasoning; longer
+  chains are out of distribution.
+- **Quantization.** Lower-bit quants (Q4) trade some quality for size; prefer Q6_K/Q8_0
+  when accuracy matters most.
+
+## License
+
+Released under the [MIT License](https://huggingface.co/occ-ai/OCC-RAG-1.7B), inherited
+from the base model.
+
+## Citation
+
+```bibtex
+@misc{savkin2026occragoptimalcognitivecore,
+  title         = {OCC-RAG: Optimal Cognitive Core for Faithful Question Answering},
+  author        = {Maksim Savkin and Mikhail Goncharov and Alexander Gambashidze and Alla Chepurova and Dmitrii Tarasov and Nikita Andriianov and Daria Pugacheva and Vasily Konovalov and Andrey Galichin and Ivan Oseledets},
+  year          = {2026},
+  eprint        = {2606.00683},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CL},
+  url           = {https://arxiv.org/abs/2606.00683}
+}
+```