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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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- OpenAssistant/oasst_top1_2023-08-25 |
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language: |
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- en |
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tags: |
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- coreml |
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--- |
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# TinyLlama-1.1B-Chat-v0.3-CoreML |
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- Model creator: [Zhang Peiyuan](https://huggingface.co/PY007) |
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- Original model: [TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) |
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## Description |
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This repository contains CoreML model files for [Zhang Peiyuan's TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3). |
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### About CoreML |
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CoreML is the Apple-exclusive model format that is highly optimized for their Apple Silicon chips and for use with their mobile devices. |
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## Prompt template: ChatML |
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``` |
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<|im_start|>system |
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{system_prompt}<|im_end|> |
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<|im_start|>user |
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{prompt}<|im_end|> |
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<|im_start|>assistant |
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``` |
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## Licensing |
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The creator of the source model has listed its license as `apache-2.0`, and this model has therefore used that same license. |
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As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms. |
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## Usage |
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- [Swift Transformers](https://github.com/huggingface/swift-transformers) |
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# Original Model Card: TinyLlama-1.1B |
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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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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 top of [PY007/TinyLlama-1.1B-intermediate-step-480k-1T](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-480k-1T). |
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The dataset used is [OpenAssistant/oasst_top1_2023-08-25](https://huggingface.co/datasets/OpenAssistant/oasst_top1_2023-08-25) following the [chatml](https://github.com/openai/openai-python/blob/main/chatml.md) format. |
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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-v0.3" |
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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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CHAT_EOS_TOKEN_ID = 32002 |
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prompt = "How to get in a good university?" |
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formatted_prompt = ( |
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f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n" |
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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 = 0.9, |
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num_return_sequences=1, |
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repetition_penalty=1.1, |
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max_new_tokens=1024, |
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eos_token_id=CHAT_EOS_TOKEN_ID, |
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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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``` |
