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--- |
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language: |
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- en |
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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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model-index: |
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- name: TinyLlama-1.1B-step-50K-105b |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: AI2 Reasoning Challenge (25-Shot) |
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type: ai2_arc |
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config: ARC-Challenge |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 25.85 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-step-50K-105b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (10-Shot) |
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type: hellaswag |
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split: validation |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 44.1 |
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name: normalized accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-step-50K-105b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: MMLU (5-Shot) |
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type: cais/mmlu |
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config: all |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 26.78 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-step-50K-105b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (0-shot) |
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type: truthful_qa |
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config: multiple_choice |
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split: validation |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 39.51 |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-step-50K-105b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Winogrande (5-shot) |
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type: winogrande |
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config: winogrande_xl |
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split: validation |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 54.38 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-step-50K-105b |
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name: Open LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: GSM8k (5-shot) |
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type: gsm8k |
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config: main |
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split: test |
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args: |
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num_few_shot: 5 |
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metrics: |
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- type: acc |
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value: 0.53 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=PY007/TinyLlama-1.1B-step-50K-105b |
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name: Open LLM Leaderboard |
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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 ππ. The training has started on 2023-09-01. |
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|
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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 50K steps and 105B 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 = "PY007/TinyLlama-1.1B-step-50K-105b" |
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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 ππ. 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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``` |
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_PY007__TinyLlama-1.1B-step-50K-105b) |
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|
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| Metric |Value| |
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|---------------------------------|----:| |
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|Avg. |31.86| |
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|AI2 Reasoning Challenge (25-Shot)|25.85| |
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|HellaSwag (10-Shot) |44.10| |
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|MMLU (5-Shot) |26.78| |
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|TruthfulQA (0-shot) |39.51| |
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|Winogrande (5-shot) |54.38| |
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|GSM8k (5-shot) | 0.53| |
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