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Model: worthdoing/SmolLM2-1.7B-Instruct-GGUF
Source: Original Platform

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README.md

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+---
+language:
+- en
+- fr
+- multilingual
+license: apache-2.0
+tags:
+- gguf
+- quantized
+- mac
+- apple-silicon
+- local-inference
+- worthdoing
+base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
+quantized_by: worthdoing
+pipeline_tag: text-generation
+---
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/Worth-Doing/brand-assets/main/png/variants/04-horizontal.png" alt="worthdoing" width="400"/>
+</p>
+<p align="center"><strong>Author: Simon-Pierre Boucher</strong></p>
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Format-GGUF-blue?style=for-the-badge" alt="GGUF"/>
+  <img src="https://img.shields.io/badge/Params-1.7B-orange?style=for-the-badge" alt="Parameters"/>
+  <img src="https://img.shields.io/badge/Platform-Apple_Silicon-black?style=for-the-badge&logo=apple" alt="Apple Silicon"/>
+  <img src="https://img.shields.io/badge/License-Apache_2.0-green?style=for-the-badge" alt="License"/>
+  <img src="https://img.shields.io/badge/Quantized_by-worthdoing-purple?style=for-the-badge" alt="worthdoing"/>
+</p>
+<p align="center">
+  <img src="https://img.shields.io/badge/Q4__K__M-0.9_GB-brightgreen?style=flat-square" alt="Q4_K_M"/>
+  <img src="https://img.shields.io/badge/Q5__K__M-1.1_GB-yellow?style=flat-square" alt="Q5_K_M"/>
+  <img src="https://img.shields.io/badge/Q8__0-1.6_GB-red?style=flat-square" alt="Q8_0"/>
+</p>
+
+# SmolLM2-1.7B-Instruct - GGUF Quantized by worthdoing
+
+> Quantized for local Mac inference (Apple Silicon / Metal) by **worthdoing**
+
+## About
+
+This is a GGUF quantized version of [SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct), optimized for running locally on Apple Silicon Macs with `llama.cpp`, `Ollama`, or `LM Studio`.
+
+- **Original model:** [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct)
+- **Parameters:** 1.7B
+- **Quantized by:** worthdoing
+- **Pipeline:** corelm-model v1.0
+
+## Description
+
+Smallest viable general model. Perfect for edge/embedded use.
+
+## Available Quantizations
+
+| File | Quant | BPW | Size | Use Case |
+|------|-------|-----|------|----------|
+| `smollm2-1.7b-instruct-Q4_K_M-worthdoing.gguf` | Q4_K_M | 4.58 | ~0.9 GB | **Recommended** - Best quality/size ratio |
+| `smollm2-1.7b-instruct-Q5_K_M-worthdoing.gguf` | Q5_K_M | 5.33 | ~1.1 GB | Higher quality, still fast |
+| `smollm2-1.7b-instruct-Q8_0-worthdoing.gguf` | Q8_0 | 7.96 | ~1.6 GB | Near-original quality |
+
+## How to Use
+
+### With Ollama
+```bash
+# Create a Modelfile
+cat > Modelfile <<'MODELEOF'
+FROM ./smollm2-1.7b-instruct-Q4_K_M-worthdoing.gguf
+MODELEOF
+
+ollama create smollm2-1.7b-instruct -f Modelfile
+ollama run smollm2-1.7b-instruct
+```
+
+### With llama.cpp
+```bash
+llama-cli -m smollm2-1.7b-instruct-Q4_K_M-worthdoing.gguf -p "Your prompt here" -ngl 99
+```
+
+### With LM Studio
+1. Download the GGUF file
+2. Open LM Studio -> My Models -> Import
+3. Select the GGUF file and start chatting
+
+## Quantization Method
+
+Our quantization pipeline (**corelm-model v1.0**) follows a rigorous multi-step process to ensure maximum quality and compatibility:
+
+### Step 1 — Download & Validation
+- Model weights are downloaded from HuggingFace Hub in **SafeTensors** format (`.safetensors`)
+- Legacy formats (`.bin`, `.pt`) are excluded to ensure clean, verified weights
+- Tokenizer, configuration, and all metadata are preserved
+
+### Step 2 — Conversion to GGUF F16 Baseline
+- The original model is converted to **GGUF format at FP16 precision** using `convert_hf_to_gguf.py` from [llama.cpp](https://github.com/ggml-org/llama.cpp)
+- This lossless baseline preserves the full original model quality
+- Architecture-specific tensors (attention, FFN, embeddings, MoE routing) are mapped to their GGUF equivalents
+
+### Step 3 — K-Quant Quantization
+- The F16 baseline is quantized using `llama-quantize` with **k-quant methods**
+- K-quants use a mixed-precision approach: more important layers (attention, output) retain higher precision, while less sensitive layers (FFN) are compressed more aggressively
+- Each quantization level offers a different quality/size tradeoff:
+
+| Method | Bits per Weight | Strategy |
+|--------|----------------|----------|
+| **Q4_K_M** | ~4.58 bpw | Mixed 4/5-bit. Attention & output layers use Q5_K, FFN layers use Q4_K. Best balance of quality and size. |
+| **Q5_K_M** | ~5.33 bpw | Mixed 5/6-bit. Attention & output layers use Q6_K, FFN layers use Q5_K. Higher quality with moderate size increase. |
+| **Q8_0** | ~7.96 bpw | Uniform 8-bit. All layers quantized to 8-bit. Near-lossless quality, largest file size. |
+
+### Step 4 — Metadata Injection
+- Custom metadata is embedded directly in each GGUF file:
+  - `general.quantized_by`: worthdoing
+  - `general.quantization_version`: corelm-1.0
+- This ensures full traceability and provenance of every quantized file
+
+### Tools & Environment
+- **llama.cpp**: Used for both conversion and quantization — the industry-standard open-source LLM inference engine
+- **Target platform**: Apple Silicon Macs (M1/M2/M3/M4) with Metal GPU acceleration
+- **Inference runtimes**: Compatible with `llama.cpp`, `Ollama`, `LM Studio`, `koboldcpp`, and any GGUF-compatible runtime
+
+## Recommended Hardware
+
+| Quant | Min RAM | Recommended |
+|-------|---------|-------------|
+| Q4_K_M | 4 GB | Mac with 8 GB+ RAM |
+| Q5_K_M | 4 GB | Mac with 8 GB+ RAM |
+| Q8_0 | 4 GB | Mac with 8 GB+ RAM |
+
+## Tags
+
+`general`, `ultra-lightweight`, `edge`
+
+---
+
+*Quantized with corelm-model pipeline by **worthdoing** on 2026-04-17*

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