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--- |
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license: apache-2.0 |
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datasets: |
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- cerebras/SlimPajama-627B |
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- bigcode/starcoderdata |
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language: |
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- en |
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--- |
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<div align="center"> |
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# TinyLlama-1.1B |
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</div> |
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https://github.com/jzhang38/TinyLlama |
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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. |
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<div align="center"> |
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<img src="./TinyLlama_logo.png" width="300"/> |
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</div> |
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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. |
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#### This Model |
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This is an intermediate checkpoint with 995K steps and 2003B tokens. |
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#### Releases Schedule |
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We will be rolling out intermediate checkpoints following the below schedule. We also include some baseline models for comparison. |
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| Date | HF Checkpoint | Tokens | Step | HellaSwag Acc_norm | |
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|------------|-------------------------------------------------|--------|------|---------------------| |
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| Baseline | [StableLM-Alpha-3B](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)| 800B | -- | 38.31 | |
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| Baseline | [Pythia-1B-intermediate-step-50k-105b](https://huggingface.co/EleutherAI/pythia-1b/tree/step50000) | 105B | 50k | 42.04 | |
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| Baseline | [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) | 300B | 143k | 47.16 | |
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| 2023-09-04 | [TinyLlama-1.1B-intermediate-step-50k-105b](https://huggingface.co/PY007/TinyLlama-1.1B-step-50K-105b) | 105B | 50k | 43.50 | |
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| 2023-09-16 | -- | 500B | -- | -- | |
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| 2023-10-01 | -- | 1T | -- | -- | |
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| 2023-10-16 | -- | 1.5T | -- | -- | |
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| 2023-10-31 | -- | 2T | -- | -- | |
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| 2023-11-15 | -- | 2.5T | -- | -- | |
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| 2023-12-01 | -- | 3T | -- | -- | |
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#### How to use |
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You will need the transformers>=4.31 |
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Do check the [TinyLlama](https://github.com/jzhang38/TinyLlama) github page for more information. |
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``` |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "TinyLlama/TinyLlama-1.1B-intermediate-step-955k-token-2T" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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) |
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sequences = pipeline( |
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'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.', |
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do_sample=True, |
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top_k=10, |
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num_return_sequences=1, |
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repetition_penalty=1.5, |
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eos_token_id=tokenizer.eos_token_id, |
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max_length=500, |
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) |
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for seq in sequences: |
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print(f"Result: {seq['generated_text']}") |
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``` |
