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Add iMatrix GGUF quantizations for ReaderLM-v2

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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ ReaderLM-v2-IQ2_M.gguf filter=lfs diff=lfs merge=lfs -text
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+ ReaderLM-v2-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
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+ ReaderLM-v2-IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text
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+ ReaderLM-v2-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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+ ReaderLM-v2-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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+ ReaderLM-v2-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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+ ReaderLM-v2-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+ ---
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+ license: other
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+ base_model: jinaai/ReaderLM-v2
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+ pipeline_tag: text-generation
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+ tags:
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+ - gguf
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+ - local-llm
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+ - llama.cpp
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+ - lm-studio
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+ - quantized
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+ - imatrix
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+ - sub-4-bit
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+ - qwen2
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+ ---
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+
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+ # ReaderLM-v2 — iMatrix GGUF
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+
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+ GGUF quantizations of [jinaai/ReaderLM-v2](https://huggingface.co/jinaai/ReaderLM-v2), published by [Liodon AI](https://huggingface.co/liodon-ai).
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+
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+ ## Quick Start
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+
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+ **llama.cpp**
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+ ```bash
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+ llama-cli -hf liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
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+ ```
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+
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+ **Ollama**
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+ ```bash
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+ ollama run hf.co/liodon-ai/ReaderLM-v2-imatrix-GGUF:Q4_K_M
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+ ```
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+
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+ **LM Studio / Jan** — search `liodon-ai/ReaderLM-v2-imatrix-GGUF` and pick your quant.
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+
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+ ## Quants
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+
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+ | Quant | Size | VRAM est. | Notes |
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+ |-------|------|-----------|-------|
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+ | `IQ2_M` | 0.60 GB | ~1 GB | 2-bit, iMatrix — smallest usable |
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+ | `IQ3_M` | 0.78 GB | ~1 GB | 3-bit, iMatrix — great quality/size tradeoff |
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+ | `IQ4_XS` | 0.90 GB | ~1 GB | 4-bit extra-small, iMatrix |
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+ | `Q4_K_M` | 0.99 GB | ~1 GB | 4-bit, iMatrix-calibrated (recommended) |
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+ | `Q5_K_M` | 1.13 GB | ~1 GB | 5-bit, iMatrix-calibrated |
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+ | `Q6_K` | 1.27 GB | ~1 GB | 6-bit, iMatrix-calibrated, near-lossless |
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+ | `Q8_0` | 1.65 GB | ~2 GB | 8-bit, essentially lossless |
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+
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+
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+ ## What is iMatrix?
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+
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+ Standard quantization treats all weights equally. iMatrix runs 128 calibration chunks through
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+ the full-precision model to find which weights matter most, then allocates more precision where
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+ it counts. At Q2/Q3/Q4 this means noticeably better coherence and instruction-following —
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+ **same file size, better output**.
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+
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+ Calibration: 2M tokens of [WikiText-103](https://huggingface.co/datasets/wikitext).
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+
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+ > Also see plain (non-iMatrix) quants: `liodon-ai/ReaderLM-v2-GGUF`
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+
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+ ## Source
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+
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+ - **Model**: [jinaai/ReaderLM-v2](https://huggingface.co/jinaai/ReaderLM-v2)
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+ - **License**: other
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+
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+ ---
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+ *Quantized by [Liodon AI](https://huggingface.co/liodon-ai)*
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