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Model: jc-builds/SmolLM3-3B-Instruct-GGUF Source: Original Platform
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README.md
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---
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license: apache-2.0
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license_link: https://www.apache.org/licenses/LICENSE-2.0
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base_model: HuggingFaceTB/SmolLM3-3B
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library_name: gguf
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pipeline_tag: text-generation
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language:
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- en
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- fr
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- es
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- de
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- it
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- pt
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tags:
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- gguf
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- llama.cpp
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- quantized
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- on-device
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- mobile
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- apple-silicon
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- haplo
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- smollm3
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inference: false
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---
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# SmolLM3-3B — GGUF (iPhone-optimized)
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GGUF quantizations of [`HuggingFaceTB/SmolLM3-3B`](https://huggingface.co/HuggingFaceTB/SmolLM3-3B), built and optimized for on-device inference on iPhone, iPad, and Apple Silicon Macs via [llama.cpp](https://github.com/ggerganov/llama.cpp) or apps that wrap it (e.g. [Haplo](https://haploapp.com)).
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> Built and quantized by **jc-builds** for the [Haplo](https://haploapp.com) ecosystem. Original weights © Hugging Face, redistributed under Apache 2.0 per the upstream license.
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## TL;DR
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A 3B-parameter decoder-only transformer with **hybrid reasoning** (toggle "thinking mode" via `/think` or `/no_think` system prompts), **128k context** (with YaRN), and 6 native languages. SmolLM3 is the rare model where **everything is open** — weights, training data mixture, and training configs. At the 3B scale it outperforms Llama-3.2-3B and Qwen2.5-3B across most benchmarks and stays competitive with many 4B-class models.
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## Available quantizations
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| File | Size | Bits/weight | Recommended use |
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|------|------|-------------|-----------------|
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| `SmolLM3-3B-Q4_K_M.gguf` | 1.8 GB | 4.8 | **Default — best size/quality tradeoff for phone & laptop** |
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| `SmolLM3-3B-Q5_K_M.gguf` | 2.1 GB | 5.7 | Slightly better quality, ~17% bigger; good for iPad / Mac |
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| `SmolLM3-3B-Q8_0.gguf` | 3.0 GB | 8.5 | Near-FP16 quality; only worth it on Apple Silicon Mac |
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**Pick `Q4_K_M`** unless you have a reason not to — it's the sweet spot for on-device on Apple Silicon. Q5_K_M is ~5-10% smarter on hard reasoning prompts but ~20% bigger; Q8_0 is essentially indistinguishable from FP16 but 2× the size of Q4_K_M.
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## Performance on Apple Silicon
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Approximate decode throughput at single-batch greedy decode, 2048-token context. Measured with `llama-cli`.
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| Device | RAM | Q4_K_M tok/s | Notes |
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|--------|-----|--------------|-------|
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| iPhone 15 Pro | 8 GB | ~22 tok/s | Smooth chat experience |
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| iPhone 14 Pro | 6 GB | ~18 tok/s | Comfortable |
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| iPad Pro M2 | 8 GB | ~45 tok/s | Snappy |
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| MacBook Pro M3 | 16 GB | ~80 tok/s | Effectively instant |
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> Reference numbers — your throughput will vary with prompt length, KV cache, and what else is running. Q5_K_M and Q8_0 are roughly 15% / 40% slower than Q4_K_M respectively.
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## How to use
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### 1. Haplo (iPhone / iPad / Mac)
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The model appears automatically in Haplo's model browser on Kuzco-1.1.0+ builds. The download URL for Q4_K_M is:
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```
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https://huggingface.co/jc-builds/SmolLM3-3B-Instruct-GGUF/resolve/main/SmolLM3-3B-Q4_K_M.gguf
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```
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### 2. llama.cpp (CLI)
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```bash
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huggingface-cli download jc-builds/SmolLM3-3B-Instruct-GGUF SmolLM3-3B-Q4_K_M.gguf --local-dir .
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./llama-cli \
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-m SmolLM3-3B-Q4_K_M.gguf \
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-p "Explain gravity in two sentences." \
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-n 256 \
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--temp 0.6 \
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--top-p 0.95
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```
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### 3. Ollama
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```bash
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cat <<'EOF' > Modelfile
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FROM ./SmolLM3-3B-Q4_K_M.gguf
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PARAMETER temperature 0.6
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PARAMETER top_p 0.95
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EOF
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ollama create smollm3 -f Modelfile
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ollama run smollm3
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```
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## Reasoning modes (think / no_think)
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SmolLM3 ships with hybrid reasoning. You toggle it via system prompt:
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| System prompt | Behavior |
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|---|---|
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| `/think` (default) | Emits a `<think>…</think>` reasoning block, then the answer. Better on math / code / multi-step problems. |
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| `/no_think` | Skips the reasoning block. Use for fast chat / simple Q&A. |
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Example:
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```
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<|im_start|>system
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/no_think<|im_end|>
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<|im_start|>user
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Capital of Australia?<|im_end|>
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<|im_start|>assistant
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```
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## Sampling defaults
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The upstream team recommends `temperature=0.6` and `top_p=0.95`. The GGUF metadata stores these as the defaults — most clients (llama.cpp, Haplo, Ollama) will use them automatically.
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## Chat template
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The HuggingFaceTB chat template is preserved in the GGUF metadata (so llama.cpp's `--chat-template` flag is *not* required). It uses ChatML-style turns:
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```
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<|im_start|>system
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{system}<|im_end|>
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<|im_start|>user
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{user}<|im_end|>
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<|im_start|>assistant
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{assistant}<|im_end|>
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```
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## Quantization recipe
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Built with [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit `e43431b` (May 7, 2026).
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1. Downloaded `HuggingFaceTB/SmolLM3-3B` safetensors checkpoint via `huggingface-cli`.
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2. Converted to GGUF FP16 via `convert_hf_to_gguf.py --outtype f16`.
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3. Quantized to each target type via `llama-quantize`:
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```bash
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llama-quantize SmolLM3-3B-F16.gguf SmolLM3-3B-Q4_K_M.gguf Q4_K_M
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llama-quantize SmolLM3-3B-F16.gguf SmolLM3-3B-Q5_K_M.gguf Q5_K_M
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llama-quantize SmolLM3-3B-F16.gguf SmolLM3-3B-Q8_0.gguf Q8_0
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```
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No imatrix calibration was used — the weights come from the upstream FP16 directly.
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## Original model card
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See the upstream model card for full architecture, training, and benchmark details: [HuggingFaceTB/SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B).
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## License
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Apache 2.0, inherited from the original model. Commercial use, modification, and redistribution are permitted. See [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for the full terms.
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> SmolLM3 by Hugging Face. Licensed under Apache 2.0.
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## Acknowledgements
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- The [Hugging Face SmolLM team](https://huggingface.co/HuggingFaceTB) for the original weights and an unusually generous open-everything release (training data, recipe, configs).
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- The [llama.cpp](https://github.com/ggerganov/llama.cpp) team for the GGUF format and quantization tooling.
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- The [Haplo](https://haploapp.com) ecosystem this drop is built for.
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SmolLM3-3B-Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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size 1915305888
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3
SmolLM3-3B-Q5_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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oid sha256:4a6e39157ee99fec3048717a3d19ebf2dceac7eabab33702528aa9f9e8c90f07
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size 2213756832
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version https://git-lfs.github.com/spec/v1
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oid sha256:4e6378d678a215e410b7233fe818d72585710aec78d6b7808be3f46eb96887eb
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size 3275575200
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