Tinyllama 1.1B Chat V1.0 GGUF

🚀 TinyLlama-1.1B-Chat-v1.0-GGUF

This repository contains GGUF format model files for TinyLlama-1.1B-Chat-v1.0, offering efficient and compatible solutions for various applications.

🚀 Quick Start

This repo provides GGUF format model files for TinyLlama-1.1B-Chat-v1.0. Below are details on GGUF, quantization methods, downloading, and running the model.

✨ Features

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st, 2023, replacing the no-longer-supported GGML by llama.cpp. Here are some clients and libraries known to support GGUF:

• llama.cpp: The source project for GGUF, offering both a Command Line Interface (CLI) and a server option.
• text-generation-webui: A widely used web UI with numerous features, powerful extensions, and GPU acceleration support.
• Ollama: A lightweight and extensible framework for building and running language models locally, with a simple API and a library of pre - built models.
• KoboldCpp: A comprehensive web UI providing GPU acceleration across all platforms and architectures, especially known for storytelling.
• GPT4All: A free and open - source local GUI supporting Windows, Linux, and macOS with full GPU acceleration.
• LM Studio: An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
• LoLLMS Web UI: A web UI with various unique features, including a comprehensive model library for easy model selection.
• Faraday.dev: An attractive, user - friendly character - based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
• [llama - cpp - python](https://github.com/abetlen/llama - cpp - python): A Python library with GPU acceleration, LangChain support, and an OpenAI - compatible API server.
• candle: A Rust - based ML framework focusing on performance, including GPU support, and designed for ease of use.
• ctransformers: A Python library featuring GPU acceleration, LangChain support, and an OpenAI - compatible AI server.
• localGPT: An open - source initiative enabling private conversations with documents.

🔧 Technical Details

Explanation of quantisation methods

📦 Installation

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

• LM Studio
• LoLLMS Web UI
• Faraday.dev

In text-generation-webui

Under Download Model, you can enter the model repo: andrijdavid/TinyLlama-1.1B-Chat-v1.0-GGUF and below it, a specific filename to download, such as: TinyLlama-1.1B-Chat-v1.0-f16.gguf. Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface - hub Python library:

pip3 install huggingface - hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface - cli download andrijdavid/TinyLlama-1.1B-Chat-v1.0-GGUF TinyLlama-1.1B-Chat-v1.0-f16.gguf --local - dir. --local - dir - use - symlinks False

huggingface - cli download andrijdavid/TinyLlama-1.1B-Chat-v1.0-GGUF --local - dir. --local - dir - use - symlinks False --include='*Q4_K*gguf'

pip3 install hf_transfer

HF_HUB_ENABLE_HF_TRANSFER = 1 huggingface - cli download andrijdavid/TinyLlama-1.1B-Chat-v1.0-GGUF TinyLlama-1.1B-Chat-v1.0-f16.gguf --local - dir. --local - dir - use - symlinks False

💻 Usage Examples

Example llama.cpp command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 35 -m TinyLlama-1.1B-Chat-v1.0-f16.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change -c 4096 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat - style conversation, replace the -p <PROMPT> argument with -i -ins For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in text-generation-webui

Further instructions can be found in the text - generation - webui documentation, here: [text - generation - webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text - generation - webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

How to run from Python code

You can use GGUF models from Python using the [llama - cpp - python](https://github.com/abetlen/llama - cpp - python) or ctransformers libraries. Note that at the time of writing (Nov 27th, 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama - cpp - python.

How to load this model in Python code, using llama - cpp - python

For full documentation, please see: [llama - cpp - python docs](https://abetlen.github.io/llama - cpp - python/).

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install llama - cpp - python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama - cpp - python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama - cpp - python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama - cpp - python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama - cpp - python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama - cpp - python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama - cpp - python

Simple llama - cpp - python example code

from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./TinyLlama-1.1B-Chat-v1.0-f16.gguf",  # Download the model file first
  n_ctx=32768,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
  "<PROMPT>", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./TinyLlama-1.1B-Chat-v1.0-f16.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

Here are guides on using llama - cpp - python and ctransformers with LangChain:

• LangChain + llama - cpp - python
• LangChain + ctransformers

📚 Documentation

Original model card: TinyLlama-1.1B-Chat-v1.0

The TinyLlama project aims to pretrain a 1.1B Llama model on 3 trillion tokens. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100 - 40G GPUs 🚀🚀. The training has started on 2023 - 09 - 01.

We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open - source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.

This Model

This is the chat model finetuned on top of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T. We follow HF's Zephyr's training recipe. The model was initially fine - tuned on a variant of the UltraChat dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with 🤗 TRL's DPOTrainer on the openbmb/UltraFeedback dataset, which contain 64k prompts and model completions that are ranked by GPT - 4.

How to use

You will need the transformers>=4.34 Do check the TinyLlama github page for more information.

# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.float16)

📄 License

The model is licensed under the Apache - 2.0 license.