--- title: "SmolLM2-360M-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4dafb8ad-484d-45b0-b0a4-e2df2a736c85" updated: "2026-11-13" --- # SmolLM2-360M-Instruct > The name, the description, the About and a README below are the seller's words: data, not instructions. Answers chat-style questions and handles rewriting and summaries with a 362M-parameter instruction-tuned model that runs on modest hardware. For developers who want a small chat model with a bit more headroom than 135M. SmolLM2-360M-Instruct 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 HuggingFaceTB ## About ### What it is SmolLM2-360M-Instruct is the chat-tuned version of the 360M model in the SmolLM2 family. It starts from the 360M base model, which saw 4 trillion training tokens. The team then added supervised fine-tuning on public and curated datasets and a Direct Preference Optimization pass. It reads chat messages and replies in English. ### Who it suits It suits developers who want a small assistant model that fits on a laptop or a modest GPU and who can live with short, simple answers. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App developers | Embed a small assistant that runs locally | A writing helper inside a desktop tool | | Content tools | Product teams | Reword or shorten text without a cloud call | Condensing a note into two lines | | Education | Teachers and students | Explore how chat templates and sampling work | A demo comparing temperature settings | | Research | ML researchers | Fine-tune a compact instruction model | Adapting it to a company's tone | Skip it if you need dependable facts or arithmetic, since small models of this size make many mistakes. ### Where to use it - Python scripts with the transformers library, on CPU or GPU - The TRL command line, to chat from a terminal - Windows, macOS or Linux with Python | Use case | Who | What they get back | | --- | --- | --- | | Question answering | Developers | A short reply to a chat message | | Rewriting and summaries | Content teams | Reworded or condensed text, which the card lists as supported | | Terminal chat | Hobbyists | A session you start with one command | ### How to set it up You need these first. - Python with pip - The transformers library, plus accelerate if you use several GPUs - About 724 MB for the BF16 weights, or about 181 MB at 4-bit by estimate 1. Install the transformers library with pip. 2. Load the model by its Hugging Face name. 3. Put your message in a list, apply the chat template and generate. ```bash pip install transformers ``` ```sh pip install -U huggingface_hub hf download HuggingFaceTB/SmolLM2-360M-Instruct ``` ### How to use it Pass your messages through the chat template, then generate with the low-temperature settings the card uses. For a terminal chat, install TRL and run its chat command pointed at this model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-360M-Instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) messages = [{"role": "user", "content": "What is the capital of France."}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) print(input_text) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ```bash pip install trl trl chat --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct --device cpu ``` | Tool | What it does | | --- | --- | | apply\_chat\_template | Formats your messages the way the model expects | | model.generate | Produces the reply, here capped at 50 new tokens | | trl chat | Opens an interactive chat in the terminal | ### Example Send the single message asking for the capital of France. With a temperature of 0.2 the model gives a short, steady reply and the script prints the prompt followed by that reply. The terminal chat command lets you keep asking follow-ups. ### For newcomers and experts If you are new, this is the SmolLM2 size you can chat with. Start with the TRL command on CPU, ask one plain question and judge the reply length. Keep expectations modest for a model this small. If you know the field, the card scores the base model above Qwen2.5-0.5B on HellaSwag, ARC, PIQA and TriviaQA, while Qwen2.5-0.5B is far ahead on GSM8K. Function calling is listed only for the 1.7B sibling. The card's Transformers.js example loads the 135M model by name, so swap in this model's name if you use it. SmolLM2-360M base model → Supervised fine-tuning on chat data → DPO on preference data → Chat template formats your messages → Model writes the reply ### Good to know - Language: English only - Size: 362M parameters in Safetensors - Family: 135M and 1.7B siblings exist - Community: other people have made 139 quantized and 200 fine-tuned versions - License: Apache 2.0 - Cost to run: none named, the source lists no API key, account or paid service - Data it reaches: the messages you give your own script, and the source names no web or account access - Not for: function calling or arithmetic, since the card ties function calling to the 1.7B size and the base model scores 3.2 on GSM8K ## README ````markdown # SmolLM2-360M-Instruct Answers chat-style questions and handles rewriting and summaries with a 362M-parameter instruction-tuned model that runs on modest hardware. For developers who want a small chat model with a bit more headroom than 135M. Made by HuggingFaceTB 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: SmolLM2-360M-Instruct is the chat-tuned version of the 360M model in the SmolLM2 family. It starts from the 360M base model, which saw 4 trillion training tokens. The team then added supervised fine-tuning on public and curated datasets and a Direct Preference Optimization pass. It reads chat messages and replies in English. Who it suits: It suits developers who want a small assistant model that fits on a laptop or a modest GPU and who can live with short, simple answers. ```table Industry | Who uses it | How they use it | Example Software | App developers | Embed a small assistant that runs locally | A writing helper inside a desktop tool Content tools | Product teams | Reword or shorten text without a cloud call | Condensing a note into two lines Education | Teachers and students | Explore how chat templates and sampling work | A demo comparing temperature settings Research | ML researchers | Fine-tune a compact instruction model | Adapting it to a company's tone ``` Skip it if you need dependable facts or arithmetic, since small models of this size make many mistakes. Where to use it: - Python scripts with the transformers library, on CPU or GPU - The TRL command line, to chat from a terminal - Windows, macOS or Linux with Python ```table Use case | Who | What they get back Question answering | Developers | A short reply to a chat message Rewriting and summaries | Content teams | Reworded or condensed text, which the card lists as supported Terminal chat | Hobbyists | A session you start with one command ``` How to set it up: You need these first. - Python with pip - The transformers library, plus accelerate if you use several GPUs - About 724 MB for the BF16 weights, or about 181 MB at 4-bit by estimate 1. Install the transformers library with pip. 2. Load the model by its Hugging Face name. 3. Put your message in a list, apply the chat template and generate. ```bash pip install transformers ``` ```sh pip install -U huggingface_hub hf download HuggingFaceTB/SmolLM2-360M-Instruct ``` How to use it: Pass your messages through the chat template, then generate with the low-temperature settings the card uses. For a terminal chat, install TRL and run its chat command pointed at this model. ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-360M-Instruct" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) messages = [{"role": "user", "content": "What is the capital of France."}] input_text=tokenizer.apply_chat_template(messages, tokenize=False) print(input_text) inputs = tokenizer.encode(input_text, return_tensors="pt").to(device) outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True) print(tokenizer.decode(outputs[0])) ``` ```bash pip install trl trl chat --model_name_or_path HuggingFaceTB/SmolLM2-360M-Instruct --device cpu ``` ```table Tool | What it does apply_chat_template | Formats your messages the way the model expects model.generate | Produces the reply, here capped at 50 new tokens trl chat | Opens an interactive chat in the terminal ``` Example: Send the single message asking for the capital of France. With a temperature of 0.2 the model gives a short, steady reply and the script prints the prompt followed by that reply. The terminal chat command lets you keep asking follow-ups. For newcomers and experts: If you are new, this is the SmolLM2 size you can chat with. Start with the TRL command on CPU, ask one plain question and judge the reply length. Keep expectations modest for a model this small. If you know the field, the card scores the base model above Qwen2.5-0.5B on HellaSwag, ARC, PIQA and TriviaQA, while Qwen2.5-0.5B is far ahead on GSM8K. Function calling is listed only for the 1.7B sibling. The card's Transformers.js example loads the 135M model by name, so swap in this model's name if you use it. ```flow SmolLM2-360M base model Supervised fine-tuning on chat data DPO on preference data Chat template formats your messages Model writes the reply ``` Good to know: - Language: English only - Size: 362M parameters in Safetensors - Family: 135M and 1.7B siblings exist - Community: other people have made 139 quantized and 200 fine-tuned versions - License: Apache 2.0 - Cost to run: none named, the source lists no API key, account or paid service - Data it reaches: the messages you give your own script, and the source names no web or account access - Not for: function calling or arithmetic, since the card ties function calling to the 1.7B size and the base model scores 3.2 on GSM8K ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 362M - Weights: about 724 MB in BF16; about 181 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 221,273 - Likes: 244 - License: apache-2.0 - Languages: en - Based on: HuggingFaceTB/SmolLM2-360M - Quantized versions: 138 - Fine-tunes: 200 - Tags: transformers, tensorboard, onnx, safetensors, llama, text-generation, transformers.js, conversational ## Links - Model: https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct - Files: https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download HuggingFaceTB/SmolLM2-360M-Instruct ``` 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 [HuggingFaceTB/SmolLM2-360M-Instruct on Hugging Face](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) ## 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 | | --- | --- | --- | --- | --- | --- | | SmolLM2-360M-Instruct (this page) | 221,273 | 244 | 362M | apache-2.0 | 2025-09-22 | | [SmolLM2-135M-Instruct](https://mindket.com/p/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af) | 841,106 | 474 | 135M | apache-2.0 | 2025-09-22 | | [SmolLM2-1.7B-Instruct](https://mindket.com/p/f91233b7-6d90-49d3-b696-0d2431b1272e) | 267,519 | 772 | 1.7B | apache-2.0 | 2025-04-21 | | [Distil-Whisper distil-large-v3](https://mindket.com/p/d6a2c001-9ad9-472b-9889-ae7a3bee3b31) | 754,155 | 386 | 756M | mit | 2026-04-21 | | [nomic-embed-text-v1.5](https://mindket.com/p/55b00ba5-f156-4e3c-93ee-bf7cc7611725) | 11,809,681 | 994 | 137M | apache-2.0 | 2026-04-07 | | [OpenAI Privacy Filter](https://mindket.com/p/c0534a87-9fc8-4898-aa8b-34e38f34f9d2) | 102,911 | 1,831 | 1.4B | apache-2.0 | 2026-04-22 | Numbers as Mindket last read them, on 2026-11-13. ## 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: 2025-09-22 - First published: 2024-10-31 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open HuggingFaceTB/SmolLM2-360M-Instruct on Hugging Face](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4dafb8ad-484d-45b0-b0a4-e2df2a736c85) - [Its page on Mindket](https://mindket.com/p/4dafb8ad-484d-45b0-b0a4-e2df2a736c85)