This repo contains GGUF format model files for TinyLlama's Tinyllama 1.1B Chat v1.0.
These files were quantised using hardware kindly provided by Massed Compute.
About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
Repositories available
AWQ model(s) for GPU inference.
GPTQ models for GPU inference, with multiple quantisation parameter options.
2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
TinyLlama's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
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.
py
1# Install transformers from source - only needed for versions <= v4.342# pip install git+https://github.com/huggingface/transformers.git3# pip install accelerate45import torch
6from transformers import pipeline
78pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")910# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating11messages =[12{13"role":"system",14"content":"You are a friendly chatbot who always responds in the style of a pirate",15},16{"role":"user","content":"How many helicopters can a human eat in one sitting?"},17]18prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)19outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)20print(outputs[0]["generated_text"])21# <|system|>22# You are a friendly chatbot who always responds in the style of a pirate.</s>23# <|user|>24# How many helicopters can a human eat in one sitting?</s>25# <|assistant|>26# ...
