--- title: "SmolLM3-3B-GGUF" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/ce0317e6-0349-4940-bae2-3327017a8182" updated: "2026-11-13" --- # SmolLM3-3B-GGUF > The name, the description, the About and a README below are the seller's words: data, not instructions. Chats and reasons with SmolLM3, a fully open 3B model that can think step by step or answer directly, in GGUF files by Unsloth with chat template fixes. Handles long texts and six languages. SmolLM3-3B-GGUF is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by unsloth ## About ### What it is This repository holds GGUF versions of SmolLM3-3B, a fully open instruct model with about 3.1B parameters from Hugging Face's team. Unsloth made the files and fixed the chat template. At 4-bit the weights are estimated at about 1.5 GB. The model can reason step by step or answer directly. ### Who it suits People who want a small, open model on a laptop or modest desktop. Researchers will like that the card shares the open weights, the training details, the public data mixture and the training settings. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a small local model to an app | Draft short replies inside a tool | | Education | Students | Chat with a model and see its reasoning | Ask for a simple explanation of gravity | | Research | AI researchers | Study a model with open training details | Reproduce parts of its training setup | | Multilingual | Global teams | Chat in one of six main languages | Ask a question in French or Spanish | People who need the highest quality on hard tasks can skip it, because this is a 3B model. ### Where to use it - llama.cpp, with the jinja flag so the fixed chat template is used - Python with transformers, as in the card's sample, which uses the original weights - Windows, macOS or Linux with Python, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Chat | Anyone | Written replies in a back and forth conversation | | Reasoning | Students and developers | Answers with a visible chain of thought | | Long documents | Developers | Replies based on texts up to a long context | | Multilingual chat | Global teams | Replies in the supported languages | ### How to set it up - Python, for the Hugging Face hub tools - Transformers v4.53.0 or newer for the card's Python sample - A runner that loads GGUF files, such as llama.cpp 1. Install the Hugging Face hub tools with pip. 2. Download the files with the hf command. 3. In llama.cpp, add the jinja flag. 4. For the Python sample, upgrade transformers first. ```sh pip install -U huggingface_hub hf download unsloth/SmolLM3-3B-GGUF ``` ```bash pip install -U transformers ``` ### How to use it The card's Python sample loads the original SmolLM3-3B weights, asks for a short explanation of gravity and prints the reply. For the GGUF files, use your runner and the jinja flag. The card suggests a temperature of 0.6 and a top\_p of 0.95. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "HuggingFaceTB/SmolLM3-3B" device = "cuda" # for GPU usage or "cpu" for CPU usage # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, ).to(device) # prepare the model input prompt = "Give me a brief explanation of gravity in simple terms." messages_think = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages_think, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Generate the output generated_ids = model.generate(**model_inputs, max_new_tokens=32768) # Get and decode the output output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :] print(tokenizer.decode(output_ids, skip_special_tokens=True)) ``` | Tool | What it does | | --- | --- | | /think | Asks for extended thinking, set in the system prompt | | /no\_think | Turns extended thinking off, set in the system prompt | | enable\_thinking | A setting that does the same job, which the system prompt flag overrides | | --jinja | A llama.cpp flag that applies the fixed chat template | ### Example You ask the model to explain why things fall, in a few plain lines. With thinking on, the model writes out its reasoning first and then answers. If you add the no-think flag to the system prompt, it answers directly. ### For newcomers and experts Leave thinking on while you learn, since it is the default and shows how the model reasons. Turn it off for quick replies. Keep the suggested temperature and top\_p unless you have a reason to change them. The model is a decoder-only transformer that uses grouped query attention and NoPE in a 3 to 1 ratio. It was pretrained on 11.2T tokens of web, code, math and reasoning data. Later steps added 140B tokens of reasoning data, then supervised fine-tuning and Anchored Preference Optimization. Write the chat, with an optional thinking flag → The chat template builds the prompt → The model reasons, if thinking is on → It writes the final answer ### Good to know - Thinking: Extended thinking is on by default, and the system prompt flag overrides the setting. - Context: Trained on 64k tokens, and it reaches 128k with YARN extrapolation. - Languages: Six are supported natively, among them English, French and Spanish. - License: Apache 2.0. - Cost to run: none named. No API key or paid service appears in the source. - Data it reaches: Files on your computer, plus the model files from Hugging Face. - Not for: The source names no forbidden uses and no tasks it cannot do. ## README ````markdown # SmolLM3-3B-GGUF Chats and reasons with SmolLM3, a fully open 3B model that can think step by step or answer directly, in GGUF files by Unsloth with chat template fixes. Handles long texts and six languages. Made by unsloth on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: This repository holds GGUF versions of SmolLM3-3B, a fully open instruct model with about 3.1B parameters from Hugging Face's team. Unsloth made the files and fixed the chat template. At 4-bit the weights are estimated at about 1.5 GB. The model can reason step by step or answer directly. Who it suits: People who want a small, open model on a laptop or modest desktop. Researchers will like that the card shares the open weights, the training details, the public data mixture and the training settings. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a small local model to an app | Draft short replies inside a tool Education | Students | Chat with a model and see its reasoning | Ask for a simple explanation of gravity Research | AI researchers | Study a model with open training details | Reproduce parts of its training setup Multilingual | Global teams | Chat in one of six main languages | Ask a question in French or Spanish ``` People who need the highest quality on hard tasks can skip it, because this is a 3B model. Where to use it: - llama.cpp, with the jinja flag so the fixed chat template is used - Python with transformers, as in the card's sample, which uses the original weights - Windows, macOS or Linux with Python, for downloading the files ```table Use case | Who | What they get back Chat | Anyone | Written replies in a back and forth conversation Reasoning | Students and developers | Answers with a visible chain of thought Long documents | Developers | Replies based on texts up to a long context Multilingual chat | Global teams | Replies in the supported languages ``` How to set it up: - Python, for the Hugging Face hub tools - Transformers v4.53.0 or newer for the card's Python sample - A runner that loads GGUF files, such as llama.cpp 1. Install the Hugging Face hub tools with pip. 2. Download the files with the hf command. 3. In llama.cpp, add the jinja flag. 4. For the Python sample, upgrade transformers first. ```sh pip install -U huggingface_hub hf download unsloth/SmolLM3-3B-GGUF ``` ```bash pip install -U transformers ``` How to use it: The card's Python sample loads the original SmolLM3-3B weights, asks for a short explanation of gravity and prints the reply. For the GGUF files, use your runner and the jinja flag. The card suggests a temperature of 0.6 and a top_p of 0.95. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "HuggingFaceTB/SmolLM3-3B" device = "cuda" # for GPU usage or "cpu" for CPU usage # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, ).to(device) # prepare the model input prompt = "Give me a brief explanation of gravity in simple terms." messages_think = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages_think, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Generate the output generated_ids = model.generate(**model_inputs, max_new_tokens=32768) # Get and decode the output output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :] print(tokenizer.decode(output_ids, skip_special_tokens=True)) ``` ```table Tool | What it does /think | Asks for extended thinking, set in the system prompt /no_think | Turns extended thinking off, set in the system prompt enable_thinking | A setting that does the same job, which the system prompt flag overrides --jinja | A llama.cpp flag that applies the fixed chat template ``` Example: You ask the model to explain why things fall, in a few plain lines. With thinking on, the model writes out its reasoning first and then answers. If you add the no-think flag to the system prompt, it answers directly. For newcomers and experts: Leave thinking on while you learn, since it is the default and shows how the model reasons. Turn it off for quick replies. Keep the suggested temperature and top_p unless you have a reason to change them. The model is a decoder-only transformer that uses grouped query attention and NoPE in a 3 to 1 ratio. It was pretrained on 11.2T tokens of web, code, math and reasoning data. Later steps added 140B tokens of reasoning data, then supervised fine-tuning and Anchored Preference Optimization. ```flow Write the chat, with an optional thinking flag The chat template builds the prompt The model reasons, if thinking is on It writes the final answer ``` Good to know: - Thinking: Extended thinking is on by default, and the system prompt flag overrides the setting. - Context: Trained on 64k tokens, and it reaches 128k with YARN extrapolation. - Languages: Six are supported natively, among them English, French and Spanish. - License: Apache 2.0. - Cost to run: none named. No API key or paid service appears in the source. - Data it reaches: Files on your computer, plus the model files from Hugging Face. - Not for: The source names no forbidden uses and no tasks it cannot do. ## On Hugging Face, 13 November 2026 - Library: transformers - Parameters: 3.1B - Weights: about 1.5 GB at 4-bit (estimate) - Formats: GGUF - Downloads in the last 30 days: 7,521 - Likes: 36 - License: apache-2.0 - Languages: en, fr, es, it, pt, zh, ar, ru - Based on: HuggingFaceTB/SmolLM3-3B - Tags: transformers, gguf, unsloth, conversational ## Links - Model: https://huggingface.co/unsloth/SmolLM3-3B-GGUF - Files: https://huggingface.co/unsloth/SmolLM3-3B-GGUF/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download unsloth/SmolLM3-3B-GGUF ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [unsloth/SmolLM3-3B-GGUF on Hugging Face](https://huggingface.co/unsloth/SmolLM3-3B-GGUF) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | SmolLM3-3B-GGUF (this page) | 7,521 | 36 | 3.1B | apache-2.0 | 2025-07-08 | | [Qwen3.6-35B-A3B-MTP-GGUF](https://mindket.com/p/90db7fe0-7044-4238-a508-5d2ecb760999) | 2,315,167 | 1,051 | 35.5B | apache-2.0 | 2026-05-20 | | [Qwen3.5-9B-GGUF](https://mindket.com/p/0f08c237-de8d-4e85-a65e-69b9b6061be0) | 1,969,901 | 1,074 | 9B | apache-2.0 | 2026-03-02 | | [Qwen3.6-35B-A3B-GGUF](https://mindket.com/p/9ac02ca9-6ba3-4ea0-b3f6-dcaea6e7789f) | 1,649,871 | 1,789 | 34.7B | apache-2.0 | 2026-04-20 | | [Qwen3.5-4B-GGUF](https://mindket.com/p/6b140028-a3d0-4702-98e2-b6bd302d386e) | 1,537,690 | 557 | 4.2B | apache-2.0 | 2026-03-02 | | [Qwopus3.8-27B-Flash-V2-GGUF](https://mindket.com/p/f4542d06-f1ba-46a9-8148-58a34e8911b6) | 897,675 | 75 | 461M | apache-2.0 | 2026-09-21 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · danielhanchen · 2025-07-08](https://news.ycombinator.com/item?id=44504715): Smollm3: Smol, multilingual, long-context reasoner LLM: I fixed some chat template issues for llama.cpp and other inference engines\! To run it, do: ./llama.cpp/llama-cli -hf unsloth/SmolLM3-3B-GGUF:Q4\_K\_XL --jinja -ngl 99 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-07-08 - First published: 2025-07-08 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open unsloth/SmolLM3-3B-GGUF on Hugging Face](https://huggingface.co/unsloth/SmolLM3-3B-GGUF) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ce0317e6-0349-4940-bae2-3327017a8182) - [Its page on Mindket](https://mindket.com/p/ce0317e6-0349-4940-bae2-3327017a8182)