TinyLlama-1.1B-Chat-v1.0

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.

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.

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.

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.

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:

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.