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Model: bond005/meno-lite-0.1-gguf Source: Original Platform
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bond005/meno-lite-0.1-gguf/meno-lite-0.1-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
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meno-lite-0.1-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
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meno-lite-0.1-fp16.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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language:
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- en
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- ru
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base_model:
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- bond005/meno-lite-0.1
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tags:
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- rag
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- ner
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- information-extraction
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- summarization
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- question-answering
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- document-qa
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- long-context
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pipeline_tag: text-generation
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---
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# Meno-Lite-0.1 GGUF
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This repository contains quantized GGUF versions of [Meno-Lite-0.1](https://huggingface.co/bond005/meno-lite-0.1).
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All variants were produced using an **importance matrix** computed on the `train` split of the [`ru_llm_calibration`](https://huggingface.co/datasets/bond005/ru_llm_calibration) dataset, and are intended to be run with [`llama.cpp`](https://github.com/ggerganov/llama.cpp).
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## Available Formats
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| Quantization type | File size | Quality | Recommendation |
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| :--- | :--- | :--- | :--- |
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| **Q8_0** | ~8.05 GB | Virtually identical to FP16 | **Best quality**. Ideal for CPU inference when memory is not a constraint. |
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| **Q5_K_M** | ~5.41 GB | Minimal degradation | **Recommended balance**. Excellent speed and quality, fits most consumer GPUs. |
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| **Q4_K_M** | ~4.65 GB | Moderate degradation | **"Golden standard"**. Best trade-off between size and quality. |
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| **IQ3_M** | ~3.54 GB | Noticeable degradation | **Maximum memory savings**. Quality drops visibly; suited for highly constrained devices. |
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## Quality Evaluation
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Quality was measured on the `test` split of the [**Ru LLM Calibration**](https://huggingface.co/datasets/bond005/ru_llm_calibration) dataset using the `llama-perplexity` utility. The original FP16 model served as the reference.
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| Metric | Q8_0 | Q5_K_M | Q4_K_M | IQ3_M |
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| :--- | :--- | :--- | :--- | :--- |
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| **Mean PPL (Q) ↓** | 9.047 | 9.075 | 9.135 | 9.689 |
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| **PPL correlation ↑** | 99.97% | 99.87% | 99.69% | 98.64% |
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| **Mean KLD ↓** | 0.0020 | 0.0077 | 0.0174 | 0.0804 |
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| **Same top p ↑** | 96.71% | 94.36% | 92.16% | 84.58% |
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> ↑ – higher is better; ↓ – lower is better
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**How to interpret these metrics:**
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- **Mean PPL (Q)**: Lower is better. Shows the average perplexity of the quantized model.
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- **PPL correlation**: Closer to 100% indicates the quantized model behaves almost identically to FP16. Values above 99.5% are considered excellent.
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- **Mean KLD**: Measures the divergence between the output probability distributions. Lower is better; 0 means identical distributions.
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- **Same top p**: The percentage of tokens where the quantized model's top prediction matches the FP16 model. Higher is better – it reflects how often the model's first-choice token remains unchanged.
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## Usage
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### 1. Install `llama.cpp`
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Follow the [official build instructions](https://github.com/ggerganov/llama.cpp#build).
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### 2. Run the model
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```bash
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# CLI
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./llama-cli -hf bond005/meno-lite-0.1-gguf -m meno-lite-0.1-Q4_K_M.gguf -p "Привет, как дела?"
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# Server with WebUI (default http://127.0.0.1:8080)
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./llama-server -hf bond005/meno-lite-0.1-gguf -m meno-lite-0.1-Q4_K_M.gguf --host 0.0.0.0 --port 8080
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```
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For more details on available parameters, see the [`llama.cpp` documentation](https://github.com/ggerganov/llama.cpp/tree/master/examples).
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## About Meno-Lite-0.1
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Meno-Lite-0.1 is a 7B model based on Qwen2.5, fine-tuned for **RAG, document QA, information extraction, and knowledge graph construction**. Read more about its capabilities, training procedure, and limitations in the [main model card](https://huggingface.co/bond005/meno-lite-0.1).
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## License
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All quantized variants inherit the license of the original model (Apache 2.0).
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meno-lite-0.1-IQ3_M.gguf
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version https://git-lfs.github.com/spec/v1
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size 3544151936
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meno-lite-0.1-Q4_K_M.gguf
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size 4649923456
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meno-lite-0.1-Q5_K_M.gguf
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size 5408584576
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meno-lite-0.1-Q8_0.gguf
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meno-lite-0.1-Q8_0.gguf
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meno-lite-0.1-fp16.gguf
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version https://git-lfs.github.com/spec/v1
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