63 lines
2.0 KiB
Markdown
63 lines
2.0 KiB
Markdown
---
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license: Apache License 2.0
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language:
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- en
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tasks:
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- text-generation
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---
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<!-- markdownlint-disable first-line-h1 -->
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<!-- markdownlint-disable html -->
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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 the chat model finetuned on [PY007/TinyLlama-1.1B-intermediate-step-240k-503b](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-240k-503b). The dataset used is [openassistant-guananco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco).
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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 = "PY007/TinyLlama-1.1B-Chat-v0.1"
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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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prompt = "What are the values in open source projects?"
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formatted_prompt = (
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f"### Human: {prompt} ### Assistant:"
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)
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sequences = pipeline(
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formatted_prompt,
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do_sample=True,
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top_k=50,
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top_p = 0.7,
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num_return_sequences=1,
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repetition_penalty=1.1,
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max_new_tokens=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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``` |