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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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- timdettmers/openassistant-guanaco |
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language: |
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- en |
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tags: |
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- tinyllama |
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- gguf |
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--- |
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<div align="center"> |
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# GGUF Quantized version of TinyLlama at the 250-500k checkpoint |
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Original model card below from [this repo](https://huggingface.co./PY007/TinyLlama-1.1B-Chat-v0.1). |
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Video covering inference: [Youtube](https://youtu.be/T5l228844NI) |
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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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```python |
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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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``` |
