--- title: "TinyLlama-1.1B-Chat-v1.0" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e73c1610-865e-48f4-a11d-4f6b640f3ae3" updated: "2026-11-12" --- # TinyLlama-1.1B-Chat-v1.0 > The name, the description, the About and a README below are the seller's words: data, not instructions. Chats in plain text as a small 1.1B language model that runs with little memory. For developers and researchers who want a compact chat model built on a Llama-style base. TinyLlama-1.1B-Chat-v1.0 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by TinyLlama ## About ### What it is TinyLlama-1.1B-Chat-v1.0 is a small chat model with 1.1 billion parameters. It is the chat-tuned version of a base model from the TinyLlama project, which aims to pretrain a Llama-style model on 3 trillion tokens. It reuses the Llama 2 architecture and tokenizer, so it fits many tools built for Llama. ### Who it suits Developers and researchers who want a compact chat model that needs little memory. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers | Add a small chat model to an app with limited memory | Run a chatbot on a modest computer | | Research | Researchers | Study a compact Llama-style chat model | Test prompts on a 1.1B model | | Education | Students | Learn how chat models work at a small scale | Try the pipeline code and change the system message | If you need the depth of a large model, you can skip it, since this one has only 1.1B parameters. ### Where to use it - Your own computer with Python and the transformers library, on Windows, macOS or Linux - Open-source tools built for Llama models, because it shares their architecture and tokenizer | Use case | Who | What they get back | | --- | --- | --- | | Chat replies | App developers | A generated answer to each message | | Role play prompts | Hobbyists | Replies in a style set by the system message | | Small experiments | Researchers | A light model for testing ideas | ### How to set it up - Python with pip - The transformers library, version 4.34 or newer - About 2.2 GB of disk space for the BF16 weights, or about 550 MB at 4-bit (an estimate) - The accelerate library, which the card's snippet relies on for device placement 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load them with the transformers pipeline, as shown below. ```sh pip install -U huggingface_hub hf download TinyLlama/TinyLlama-1.1B-Chat-v1.0 ``` ### How to use it Build a list of messages with a system message and a user message. Turn it into a prompt with the model's chat template, then pass the prompt to the pipeline. ```python import torch from transformers import pipeline pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto") messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` | Tool | What it does | | --- | --- | | pipeline | Loads the model for text generation | | apply\_chat\_template | Formats the messages the way the model expects | | max\_new\_tokens | Caps how long the reply can be | ### Example You tell the model to answer as a pirate, then ask a silly question about how many helicopters a person could eat. The printed text holds the formatted prompt followed by the model's reply. The card does not show the reply itself. ### For newcomers and experts If you are new, copy the card's code, run it once, and change the system message to see how the replies shift. The BF16 files are about 2.2 GB, so allow time for the first download. For experts, the base is a 1.1B Llama-style model from the TinyLlama project. This chat version follows the Zephyr training recipe from Hugging Face. It was fine-tuned first on a variant of the UltraChat dataset of synthetic dialogues, then aligned with DPO on UltraFeedback, a set of 64k prompts ranked by GPT-4. You write a system and a user message → The chat template builds the prompt → The 1.1B model generates a reply → You read the generated text ### Good to know The card gives no benchmark scores, so test it on your own task. Others have published 168 quantized and 586 fine-tuned versions of it on Hugging Face. - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. A modest computer can load it. - Data it reaches: downloading reaches Hugging Face. The pipeline only reads the messages you pass in. - Not for: anything the card does not claim. It names no benchmark results, so check answers yourself before relying on them. ## README ````markdown # TinyLlama-1.1B-Chat-v1.0 Chats in plain text as a small 1.1B language model that runs with little memory. For developers and researchers who want a compact chat model built on a Llama-style base. Made by TinyLlama 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: TinyLlama-1.1B-Chat-v1.0 is a small chat model with 1.1 billion parameters. It is the chat-tuned version of a base model from the TinyLlama project, which aims to pretrain a Llama-style model on 3 trillion tokens. It reuses the Llama 2 architecture and tokenizer, so it fits many tools built for Llama. Who it suits: Developers and researchers who want a compact chat model that needs little memory. ```table Industry | Who uses it | How they use it | Example Software | Developers | Add a small chat model to an app with limited memory | Run a chatbot on a modest computer Research | Researchers | Study a compact Llama-style chat model | Test prompts on a 1.1B model Education | Students | Learn how chat models work at a small scale | Try the pipeline code and change the system message ``` If you need the depth of a large model, you can skip it, since this one has only 1.1B parameters. Where to use it: - Your own computer with Python and the transformers library, on Windows, macOS or Linux - Open-source tools built for Llama models, because it shares their architecture and tokenizer ```table Use case | Who | What they get back Chat replies | App developers | A generated answer to each message Role play prompts | Hobbyists | Replies in a style set by the system message Small experiments | Researchers | A light model for testing ideas ``` How to set it up: - Python with pip - The transformers library, version 4.34 or newer - About 2.2 GB of disk space for the BF16 weights, or about 550 MB at 4-bit (an estimate) - The accelerate library, which the card's snippet relies on for device placement 1. Install the Hugging Face hub tool. 2. Download the model files. 3. Load them with the transformers pipeline, as shown below. ```sh pip install -U huggingface_hub hf download TinyLlama/TinyLlama-1.1B-Chat-v1.0 ``` How to use it: Build a list of messages with a system message and a user message. Turn it into a prompt with the model's chat template, then pass the prompt to the pipeline. ```python import torch from transformers import pipeline pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto") messages = [ { "role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate", }, {"role": "user", "content": "How many helicopters can a human eat in one sitting?"}, ] prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ``` ```table Tool | What it does pipeline | Loads the model for text generation apply_chat_template | Formats the messages the way the model expects max_new_tokens | Caps how long the reply can be ``` Example: You tell the model to answer as a pirate, then ask a silly question about how many helicopters a person could eat. The printed text holds the formatted prompt followed by the model's reply. The card does not show the reply itself. For newcomers and experts: If you are new, copy the card's code, run it once, and change the system message to see how the replies shift. The BF16 files are about 2.2 GB, so allow time for the first download. For experts, the base is a 1.1B Llama-style model from the TinyLlama project. This chat version follows the Zephyr training recipe from Hugging Face. It was fine-tuned first on a variant of the UltraChat dataset of synthetic dialogues, then aligned with DPO on UltraFeedback, a set of 64k prompts ranked by GPT-4. ```flow You write a system and a user message The chat template builds the prompt The 1.1B model generates a reply You read the generated text ``` Good to know: The card gives no benchmark scores, so test it on your own task. Others have published 168 quantized and 586 fine-tuned versions of it on Hugging Face. - License: Apache 2.0, as the Hugging Face record lists it. - Cost to run: no API key or account is named. A modest computer can load it. - Data it reaches: downloading reaches Hugging Face. The pipeline only reads the messages you pass in. - Not for: anything the card does not claim. It names no benchmark results, so check answers yourself before relying on them. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1.1B - Weights: about 2.2 GB in BF16; about 550 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 963,057 - Likes: 1,910 - License: apache-2.0 - Languages: en - Quantized versions: 168 - Fine-tunes: 586 - Tags: transformers, safetensors, llama, text-generation, conversational ## Links - Model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 - Files: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download TinyLlama/TinyLlama-1.1B-Chat-v1.0 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [TinyLlama/TinyLlama-1.1B-Chat-v1.0 on Hugging Face](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) ## 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 | | --- | --- | --- | --- | --- | --- | | TinyLlama-1.1B-Chat-v1.0 (this page) | 963,057 | 1,910 | 1.1B | apache-2.0 | 2024-03-17 | | [MiniCPM5-2B](https://mindket.com/p/4d384af8-8281-4982-baed-7ea9aab0a3e1) | 1,039,518 | 1,850 | 2.5B | apache-2.0 | 2026-11-09 | | [SmolLM2-135M-Instruct](https://mindket.com/p/64e0fae8-46ea-40d4-9f5e-7d3ea5c053af) | 841,106 | 474 | 135M | apache-2.0 | 2025-09-22 | | [Meta-Llama-3.1-8B-Instruct-AWQ-INT4](https://mindket.com/p/ccfe63e6-9cb6-47ca-98b8-084a038b9851) | 319,739 | 96 | 8B | llama3.1 | 2024-08-07 | | [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 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-14. ## 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 · dmezzetti · 2023-12-31](https://news.ycombinator.com/item?id=38822777): TinyLlama a 1.1B Llama model trained on 3T tokens reaches 1.0 release - [Hacker News · senkora · 2024-09-23](https://news.ycombinator.com/item?id=41628495): Forget ChatGPT: why researchers now run small AIs on their laptops: I got better performance of 20.18 tokens per second using tinyllama-1.1b-chat-v1.0.Q8\_0.llamafile from https://huggingface.co/Bojun-Feng/TinyLlama-1.1B-Chat-v1.0-l... If anyone is reading this and had trouble with a larger model, that might be the one to try next. - [Hacker News · ultrasaurus · 2024-05-22](https://news.ycombinator.com/item?id=40444005): Ask HN: Which LLMs can run locally on most consumer computers: +100 to this, I don't think many people reading this thread realize how easy they've made it to run a LLM locally. It's a great start if you want to kick multiple tires (be careful to clean up\! the gigs add up). \> wget https://huggingface.co/jartine/TinyLlama-1.1B-Chat-v1.0-GGUF... \> chmod +x… - [Hacker News · jart · 2024-05-18](https://news.ycombinator.com/item?id=40397054): NetBSD bans use of Copilot-generated code: Here's an example of how easy it is to generate code on NetBSD using an open LLM: Last login: Fri May 17 23:58:08 2024 from 10.10.10.129 NetBSD 9.2 (GENERIC) \#0: Wed May 12 13:15:55 UTC 2021 Welcome to NetBSD\! We recommend that you create a non-root account and use su(1) for root access. $ ./curl… - [Hacker News · coder543 · 2024-01-08](https://news.ycombinator.com/item?id=38908113): I made an app that runs Mistral 7B 0.2 LLM locally on iPhone Pros: EDIT: Attempting to converse with any Q4\_K\_M 7B parameter model on a 15 Pro Max... the phone just melts down. It feels like it is producing about one token per minute. MLC-Chat can handle 7B parameter models just fine even on a 14 Pro Max, which has less RAM, so I think there is an issue here.… - [Hacker News · dmezzetti · 2024-01-05](https://news.ycombinator.com/item?id=38885683): TinyLlama: An Open-Source Small Language Model: Link to model on HF Hub: https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0 - [Hacker News · dmezzetti · 2023-12-31](https://news.ycombinator.com/item?id=38822957): TinyLlama a 1.1B Llama model trained on 3T tokens reaches 1.0 release: This is a small model that will easily run on most modern machines. There are many ways to run this either using Hugging Face Transformers directly or a library built on Transformers that makes it easier. For example, you can run LLM inference with txtai (https://github.com/neuml/txtai) in Python… ## 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: 2024-03-17 - First published: 2023-12-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open TinyLlama/TinyLlama-1.1B-Chat-v1.0 on Hugging Face](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e73c1610-865e-48f4-a11d-4f6b640f3ae3) - [Its page on Mindket](https://mindket.com/p/e73c1610-865e-48f4-a11d-4f6b640f3ae3)