--- title: "SmolLM2-135M-Instruct" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af" updated: "2026-11-13" --- # SmolLM2-135M-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 135M-parameter instruction-tuned model small enough for on-device use. For developers who need a tiny chat model in Python or JavaScript. SmolLM2-135M-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 SmolLM2-135M-Instruct is the chat-tuned version of the 135M model in the SmolLM2 family. The team built it from the base model with supervised fine-tuning on public and curated datasets, then applied Direct Preference Optimization. It takes messages in a chat format and replies in English. ### Who it suits It suits developers who want a chat model small enough to run in a browser, on a laptop CPU or inside an app, and who accept modest answer quality. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | App and web developers | Add a tiny assistant that runs on the user's device | A help bubble inside a web page | | Education | Teachers and students | Show how a chat model is loaded and prompted | A classroom demo of chat templates | | Research | ML researchers | Use a small instruction model as a fine-tuning base | Adapting it to a narrow task | | Testing | Engineers | Check chat pipelines with a model that loads fast | End-to-end tests of a prompt flow | Skip it if you need dependable facts or reasoning, since the card's score for grade-school maths is only 1.4. ### Where to use it - Python scripts with the transformers library, on CPU or GPU - The TRL command line, for chatting from a terminal - JavaScript with Transformers.js, including in a browser - Windows, macOS or Linux with Python or Node | Use case | Who | What they get back | | --- | --- | --- | | Chat replies | Developers | A short answer to a message you send | | Rewriting and summaries | Content tools | Reworded or condensed text, which the card lists as supported | | Terminal chat | Hobbyists | An interactive session started with one command | ### How to set it up You need these first. - Python with pip, or Node with npm for the JavaScript route - The transformers library, plus accelerate for several GPUs - About 269 MB for the BF16 weights, or about 67 MB at 4-bit by estimate 1. Install the transformers library with pip. 2. Load the model by its Hugging Face name. 3. Build a messages list and apply the chat template. 4. Call generate and decode the output. ```bash pip install transformers ``` ```sh pip install -U huggingface_hub hf download HuggingFaceTB/SmolLM2-135M-Instruct ``` ### How to use it Pass a list of role and content messages through the chat template, then generate. The card sets a low temperature of 0.2 with top-p 0.9 and 50 new tokens. For a quick terminal chat, install TRL and run its chat command. ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-135M-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 gravity?"}] 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/SmolLM2-135M-Instruct --device cpu ``` ```bash npm i @huggingface/transformers ``` ```js import { pipeline } from "@huggingface/transformers"; // Create a text generation pipeline const generator = await pipeline( "text-generation", "HuggingFaceTB/SmolLM2-135M-Instruct", ); // Define the list of messages const messages = [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "What is the capital of France?" }, ]; // Generate a response const output = await generator(messages, { max_new_tokens: 128 }); console.log(output[0].generated_text.at(-1).content); ``` | Tool | What it does | | --- | --- | | apply\_chat\_template | Turns your messages into the prompt format the model expects | | model.generate | Produces the reply tokens | | trl chat | Opens a chat session in the terminal | | Transformers.js pipeline | Runs text generation from JavaScript | ### Example In the JavaScript snippet, a system message says the assistant is helpful and the user asks for the capital of France. The reply names Paris, as the card's own comment shows. The terminal chat works the same way, one message at a time. ### For newcomers and experts If you are new, this is the version of SmolLM2 you can talk to. Start with the TRL chat command on CPU, ask one plain question and see how short the reply is. Do not expect long or deep answers from 135M parameters. If you know the field, the card compares it with SmolLM-135M-Instruct on lighteval zero-shot tests. IFEval rises from 17.2 to 29.9 and MT-Bench from 16.8 to 19.8. The recipe is SFT then DPO on UltraFeedback. The function-calling support the card mentions applies to the 1.7B model, not this one. SmolLM2-135M base model → Supervised fine-tuning on chat data → DPO on preference data → Chat template formats your messages → Model generates the reply ### Good to know - Language: English only - Size: 135M parameters in Safetensors - Family: 360M and 1.7B siblings exist - Community: other people have made 155 quantized and 411 fine-tuned versions Instruction benchmarks, SmolLM2 against the older SmolLM | Label | Value | | --- | --- | | IFEval, SmolLM2-135M-Instruct | 29.9 points | | IFEval, SmolLM-135M-Instruct | 17.2 points | | MT-Bench, SmolLM2-135M-Instruct | 19.8 points | | MT-Bench, SmolLM-135M-Instruct | 16.8 points | - 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 or browser page, and the source names no web or account access - Not for: reliable facts, maths or function calling, since the card ties function calling to the 1.7B size ## README ````markdown # SmolLM2-135M-Instruct Answers chat-style questions and handles rewriting and summaries with a 135M-parameter instruction-tuned model small enough for on-device use. For developers who need a tiny chat model in Python or JavaScript. 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: SmolLM2-135M-Instruct is the chat-tuned version of the 135M model in the SmolLM2 family. The team built it from the base model with supervised fine-tuning on public and curated datasets, then applied Direct Preference Optimization. It takes messages in a chat format and replies in English. Who it suits: It suits developers who want a chat model small enough to run in a browser, on a laptop CPU or inside an app, and who accept modest answer quality. ```table Industry | Who uses it | How they use it | Example Software | App and web developers | Add a tiny assistant that runs on the user's device | A help bubble inside a web page Education | Teachers and students | Show how a chat model is loaded and prompted | A classroom demo of chat templates Research | ML researchers | Use a small instruction model as a fine-tuning base | Adapting it to a narrow task Testing | Engineers | Check chat pipelines with a model that loads fast | End-to-end tests of a prompt flow ``` Skip it if you need dependable facts or reasoning, since the card's score for grade-school maths is only 1.4. Where to use it: - Python scripts with the transformers library, on CPU or GPU - The TRL command line, for chatting from a terminal - JavaScript with Transformers.js, including in a browser - Windows, macOS or Linux with Python or Node ```table Use case | Who | What they get back Chat replies | Developers | A short answer to a message you send Rewriting and summaries | Content tools | Reworded or condensed text, which the card lists as supported Terminal chat | Hobbyists | An interactive session started with one command ``` How to set it up: You need these first. - Python with pip, or Node with npm for the JavaScript route - The transformers library, plus accelerate for several GPUs - About 269 MB for the BF16 weights, or about 67 MB at 4-bit by estimate 1. Install the transformers library with pip. 2. Load the model by its Hugging Face name. 3. Build a messages list and apply the chat template. 4. Call generate and decode the output. ```bash pip install transformers ``` ```sh pip install -U huggingface_hub hf download HuggingFaceTB/SmolLM2-135M-Instruct ``` How to use it: Pass a list of role and content messages through the chat template, then generate. The card sets a low temperature of 0.2 with top-p 0.9 and 50 new tokens. For a quick terminal chat, install TRL and run its chat command. ```python from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "HuggingFaceTB/SmolLM2-135M-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 gravity?"}] 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/SmolLM2-135M-Instruct --device cpu ``` ```bash npm i @huggingface/transformers ``` ```js import { pipeline } from "@huggingface/transformers"; // Create a text generation pipeline const generator = await pipeline( "text-generation", "HuggingFaceTB/SmolLM2-135M-Instruct", ); // Define the list of messages const messages = [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: "What is the capital of France?" }, ]; // Generate a response const output = await generator(messages, { max_new_tokens: 128 }); console.log(output[0].generated_text.at(-1).content); ``` ```table Tool | What it does apply_chat_template | Turns your messages into the prompt format the model expects model.generate | Produces the reply tokens trl chat | Opens a chat session in the terminal Transformers.js pipeline | Runs text generation from JavaScript ``` Example: In the JavaScript snippet, a system message says the assistant is helpful and the user asks for the capital of France. The reply names Paris, as the card's own comment shows. The terminal chat works the same way, one message at a time. For newcomers and experts: If you are new, this is the version of SmolLM2 you can talk to. Start with the TRL chat command on CPU, ask one plain question and see how short the reply is. Do not expect long or deep answers from 135M parameters. If you know the field, the card compares it with SmolLM-135M-Instruct on lighteval zero-shot tests. IFEval rises from 17.2 to 29.9 and MT-Bench from 16.8 to 19.8. The recipe is SFT then DPO on UltraFeedback. The function-calling support the card mentions applies to the 1.7B model, not this one. ```flow SmolLM2-135M base model Supervised fine-tuning on chat data DPO on preference data Chat template formats your messages Model generates the reply ``` Good to know: - Language: English only - Size: 135M parameters in Safetensors - Family: 360M and 1.7B siblings exist - Community: other people have made 155 quantized and 411 fine-tuned versions ```chart Title: Instruction benchmarks, SmolLM2 against the older SmolLM IFEval, SmolLM2-135M-Instruct: 29.9 points IFEval, SmolLM-135M-Instruct: 17.2 points MT-Bench, SmolLM2-135M-Instruct: 19.8 points MT-Bench, SmolLM-135M-Instruct: 16.8 points ``` - 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 or browser page, and the source names no web or account access - Not for: reliable facts, maths or function calling, since the card ties function calling to the 1.7B size ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 135M - Weights: about 269 MB in BF16; about 67 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 841,106 - Likes: 474 - License: apache-2.0 - Languages: en - Based on: HuggingFaceTB/SmolLM2-135M - Quantized versions: 155 - Fine-tunes: 412 - Tags: transformers, tensorboard, onnx, safetensors, llama, text-generation, transformers.js, conversational ## Links - Model: https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct - Files: https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download HuggingFaceTB/SmolLM2-135M-Instruct ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 7 KB, downloaded as a zip. ```text README.md 7 KB ``` ## Source [HuggingFaceTB/SmolLM2-135M-Instruct on Hugging Face](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-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 | | --- | --- | --- | --- | --- | --- | | SmolLM2-135M-Instruct (this page) | 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 | | [SmolLM2-360M-Instruct](https://mindket.com/p/4dafb8ad-484d-45b0-b0a4-e2df2a736c85) | 221,273 | 244 | 362M | apache-2.0 | 2025-09-22 | | [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/SmolLM2-135M-Instruct on Hugging Face](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af) - [Its page on Mindket](https://mindket.com/p/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af)