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wasm-TinyLlama-1.1B-Chat-q4f342_1

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  1. .gitattributes +35 -0
  2. README.md +57 -0
  3. TinyLlama-1.1B-Chat-v1.0-q4f32_1-webgpu.wasm +3 -0
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1
+ --
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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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+ ---
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+ <div align="center">
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+ # TinyLlama-1.1B compiled for WebGPU - q4f32_1
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+
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+ - Original model: [TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
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+ - compiled by: Caleb Fahlgren: [👋 on Twitter](https://twitter.com/calebfahlgren)
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+
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+
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+ https://github.com/jzhang38/TinyLlama
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+
19
+ 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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+
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+
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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.
23
+
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+ #### This Model
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+ This is the chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). **We follow [HF's Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/edit/main/README.md)'s training recipe.** The model was " initially fine-tuned on a variant of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
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+ We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, which contain 64k prompts and model completions that are ranked by GPT-4."
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+
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+
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+ #### How to use
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+ You will need the transformers>=4.34
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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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+ ```python
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+ # Install transformers from source - only needed for versions <= v4.34
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+ # pip install git+https://github.com/huggingface/transformers.git
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+ # pip install accelerate
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+ import torch
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+ from transformers import pipeline
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+ pipe = pipeline("text-generation", model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", torch_dtype=torch.bfloat16, device_map="auto")
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+ # We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
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+ messages = [
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+ {
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+ "role": "system",
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+ "content": "You are a friendly chatbot who always responds in the style of a pirate",
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+ },
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+ {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
47
+ ]
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+ prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
49
+ outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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+ print(outputs[0]["generated_text"])
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+ # <|system|>
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+ # You are a friendly chatbot who always responds in the style of a pirate.</s>
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+ # <|user|>
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+ # How many helicopters can a human eat in one sitting?</s>
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+ # <|assistant|>
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+ # ...
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