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--- |
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language: |
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- en |
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license: mit |
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library_name: transformers |
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inference: |
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parameters: |
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max_new_tokens: 64 |
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do_sample: true |
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temperature: 0.1 |
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repetition_penalty: 10 |
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no_repeat_ngram_size: 4 |
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eta_cutoff: 0.0006 |
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renormalize_logits: true |
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widget: |
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- text: My name is El Microondas the Wise, and |
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example_title: El Microondas |
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- text: Kennesaw State University is a public |
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example_title: Kennesaw State University |
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- text: >- |
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Bungie Studios is an American video game developer. They are most famous for |
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developing the award winning Halo series of video games. They also made |
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Destiny. The studio was founded |
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example_title: Bungie |
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- text: The Mona Lisa is a world-renowned painting created by |
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example_title: Mona Lisa |
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- text: >- |
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The Harry Potter series, written by J.K. Rowling, begins with the book |
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titled |
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example_title: Harry Potter Series |
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- text: >- |
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Question: I have cities, but no houses. I have mountains, but no trees. I |
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have water, but no fish. What am I? |
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|
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Answer: |
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example_title: Riddle |
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- text: The process of photosynthesis involves the conversion of |
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example_title: Photosynthesis |
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- text: >- |
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Jane went to the store to buy some groceries. She picked up apples, oranges, |
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and a loaf of bread. When she got home, she realized she forgot |
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example_title: Story Continuation |
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- text: >- |
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Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph, and |
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another train leaves Station B at 10:00 AM and travels at 80 mph, when will |
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they meet if the distance between the stations is 300 miles? |
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|
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To determine |
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example_title: Math Problem |
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- text: In the context of computer programming, an algorithm is |
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example_title: Algorithm Definition |
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pipeline_tag: text-generation |
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model-index: |
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- name: nano-phi-115M-v0.1 |
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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: 24.15 |
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name: normalized accuracy |
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source: |
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url: >- |
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 |
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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: 29.99 |
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name: normalized accuracy |
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source: |
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url: >- |
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 |
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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: 25.46 |
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name: accuracy |
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source: |
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url: >- |
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 |
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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: 44.3 |
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source: |
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url: >- |
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 |
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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: 51.45 |
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name: accuracy |
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source: |
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url: >- |
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 |
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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 |
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name: accuracy |
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source: |
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url: >- |
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https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1 |
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name: Open LLM Leaderboard |
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datasets: |
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- kenhktsui/minipile_quality_score_v1 |
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- kenhktsui/simple_wikipedia_LM_quality_score_v1 |
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- kenhktsui/refinedweb-3m_quality_score_v1 |
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- kenhktsui/TM-DATA_quality_score_v1 |
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- kenhktsui/openwebtext_quality_score_v1 |
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- HuggingFaceTB/cosmopedia |
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--- |
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|
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|
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# Model Card for nano-phi-192M-v0.1 |
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This is a continual effort from [kenhktsui/nano-phi-115M-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-v0.1). |
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The model is not aligned. |
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|
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Major differences: |
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- bigger tokenizer's vocab size |
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- addition of [HuggingFaceTB/cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia) as training dataset |
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- training token: 19B vs 7B |
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|
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|
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## How to use |
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To use the model, you will need transformer version >= 4.37.2 |
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``` |
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pip install transformers>=4.37.2 |
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``` |
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|
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``` |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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|
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pipe = pipeline("text-generation", model="kenhktsui/nano-phi-192M-v0.1") |
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pipe("I am a machine learning researcher. I work on", max_new_tokens=50, repetition_penalty=10.0) |
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``` |
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|
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## Some metrics |
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- model |
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- hidden_size: 768 |
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- num_key_value_heads: 8 (grouped query attention) |
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- num_attention_heads: 24 |
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- num_hidden_layers: 6 |
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- context length: 1024 |
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- total params: 192M |
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- training: |
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- global steps: 36,000 |
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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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|
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| Metric |kenhktsui/nano-phi-191M-v0.1 |[kenhktsui/nano-phi-115M-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-v0.1)|[microsoft/phi-2](https://huggingface.co/microsoft/phi-2) (Reproduced)| |
|
|-----------------------|---------------------------|---------------------------|---------------------------| |
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| Avg. |29.24 | 28.68 |61.53 | |
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| ARC (25-shot) |24.15 | 21.93 |61.52 | |
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| HellaSwag (10-shot) | 29.99 | 27.87 |75.13 | |
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| MMLU (5-shot) |25.46 | 25.30 |58.23 | |
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| TruthfulQA (0-shot) |44.30 | 46.01 |44.46 | |
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| Winogrande (5-shot) |51.54 | 50.99 |74.51 | |
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| GSM8K (5-shot) |0.0 | 0.0 |55.34 | |
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|
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Details: |
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|
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8 |
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| Task |Version| Metric |Value | |Stderr| |
|
|--------|------:|--------|-----:|---|-----:| |
|
|arc_easy| 0|acc |0.4596|± |0.0102| |
|
| | |acc_norm|0.4070|± |0.0101| |
|
|
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 25, batch_size: 8 |
|
| Task |Version| Metric |Value | |Stderr| |
|
|-------------|------:|--------|-----:|---|-----:| |
|
|arc_challenge| 0|acc |0.1911|± |0.0115| |
|
| | |acc_norm|0.2415|± |0.0125| |
|
|
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 10, batch_size: 8 |
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| Task |Version| Metric |Value | |Stderr| |
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|---------|------:|--------|-----:|---|-----:| |
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|hellaswag| 0|acc |0.2833|± |0.0045| |
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| | |acc_norm|0.2999|± |0.0046| |
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|
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 8 |
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| Task |Version|Metric|Value | |Stderr| |
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|-------------|------:|------|-----:|---|-----:| |
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|truthfulqa_mc| 1|mc1 |0.2583|± |0.0153| |
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| | |mc2 |0.4430|± |0.0152| |
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|
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 8 |
|
| Task |Version| Metric |Value | |Stderr| |
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|-------------------------------------------------|------:|--------|-----:|---|-----:| |
|
|hendrycksTest-abstract_algebra | 1|acc |0.2200|± |0.0416| |
|
| | |acc_norm|0.2200|± |0.0416| |
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|hendrycksTest-anatomy | 1|acc |0.2593|± |0.0379| |
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| | |acc_norm|0.2593|± |0.0379| |
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|hendrycksTest-astronomy | 1|acc |0.1711|± |0.0306| |
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| | |acc_norm|0.1711|± |0.0306| |
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|hendrycksTest-business_ethics | 1|acc |0.2400|± |0.0429| |
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| | |acc_norm|0.2400|± |0.0429| |
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|hendrycksTest-clinical_knowledge | 1|acc |0.2566|± |0.0269| |
|
| | |acc_norm|0.2566|± |0.0269| |
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|hendrycksTest-college_biology | 1|acc |0.2639|± |0.0369| |
|
| | |acc_norm|0.2639|± |0.0369| |
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|hendrycksTest-college_chemistry | 1|acc |0.1800|± |0.0386| |
|
| | |acc_norm|0.1800|± |0.0386| |
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|hendrycksTest-college_computer_science | 1|acc |0.3300|± |0.0473| |
|
| | |acc_norm|0.3300|± |0.0473| |
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|hendrycksTest-college_mathematics | 1|acc |0.3000|± |0.0461| |
|
| | |acc_norm|0.3000|± |0.0461| |
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|hendrycksTest-college_medicine | 1|acc |0.2023|± |0.0306| |
|
| | |acc_norm|0.2023|± |0.0306| |
|
|hendrycksTest-college_physics | 1|acc |0.2843|± |0.0449| |
|
| | |acc_norm|0.2843|± |0.0449| |
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|hendrycksTest-computer_security | 1|acc |0.2200|± |0.0416| |
|
| | |acc_norm|0.2200|± |0.0416| |
|
|hendrycksTest-conceptual_physics | 1|acc |0.2511|± |0.0283| |
|
| | |acc_norm|0.2511|± |0.0283| |
|
|hendrycksTest-econometrics | 1|acc |0.2807|± |0.0423| |
|
| | |acc_norm|0.2807|± |0.0423| |
|
|hendrycksTest-electrical_engineering | 1|acc |0.2897|± |0.0378| |
|
| | |acc_norm|0.2897|± |0.0378| |
|
|hendrycksTest-elementary_mathematics | 1|acc |0.2804|± |0.0231| |
|
| | |acc_norm|0.2804|± |0.0231| |
|
|hendrycksTest-formal_logic | 1|acc |0.2143|± |0.0367| |
|
| | |acc_norm|0.2143|± |0.0367| |
|
|hendrycksTest-global_facts | 1|acc |0.1700|± |0.0378| |
|
| | |acc_norm|0.1700|± |0.0378| |
|
|hendrycksTest-high_school_biology | 1|acc |0.3226|± |0.0266| |
|
| | |acc_norm|0.3226|± |0.0266| |
|
|hendrycksTest-high_school_chemistry | 1|acc |0.2759|± |0.0314| |
|
| | |acc_norm|0.2759|± |0.0314| |
|
|hendrycksTest-high_school_computer_science | 1|acc |0.2700|± |0.0446| |
|
| | |acc_norm|0.2700|± |0.0446| |
|
|hendrycksTest-high_school_european_history | 1|acc |0.2606|± |0.0343| |
|
| | |acc_norm|0.2606|± |0.0343| |
|
|hendrycksTest-high_school_geography | 1|acc |0.3081|± |0.0329| |
|
| | |acc_norm|0.3081|± |0.0329| |
|
|hendrycksTest-high_school_government_and_politics| 1|acc |0.3627|± |0.0347| |
|
| | |acc_norm|0.3627|± |0.0347| |
|
|hendrycksTest-high_school_macroeconomics | 1|acc |0.2641|± |0.0224| |
|
| | |acc_norm|0.2641|± |0.0224| |
|
|hendrycksTest-high_school_mathematics | 1|acc |0.2630|± |0.0268| |
|
| | |acc_norm|0.2630|± |0.0268| |
|
|hendrycksTest-high_school_microeconomics | 1|acc |0.3403|± |0.0308| |
|
| | |acc_norm|0.3403|± |0.0308| |
|
|hendrycksTest-high_school_physics | 1|acc |0.3113|± |0.0378| |
|
| | |acc_norm|0.3113|± |0.0378| |
|
|hendrycksTest-high_school_psychology | 1|acc |0.2716|± |0.0191| |
|
| | |acc_norm|0.2716|± |0.0191| |
|
|hendrycksTest-high_school_statistics | 1|acc |0.4491|± |0.0339| |
|
| | |acc_norm|0.4491|± |0.0339| |
|
|hendrycksTest-high_school_us_history | 1|acc |0.2402|± |0.0300| |
|
| | |acc_norm|0.2402|± |0.0300| |
|
|hendrycksTest-high_school_world_history | 1|acc |0.2363|± |0.0277| |
|
| | |acc_norm|0.2363|± |0.0277| |
|
|hendrycksTest-human_aging | 1|acc |0.2197|± |0.0278| |
|
| | |acc_norm|0.2197|± |0.0278| |
|
|hendrycksTest-human_sexuality | 1|acc |0.2824|± |0.0395| |
|
| | |acc_norm|0.2824|± |0.0395| |
|
|hendrycksTest-international_law | 1|acc |0.2479|± |0.0394| |
|
| | |acc_norm|0.2479|± |0.0394| |
|
|hendrycksTest-jurisprudence | 1|acc |0.2037|± |0.0389| |
|
| | |acc_norm|0.2037|± |0.0389| |
|
|hendrycksTest-logical_fallacies | 1|acc |0.2393|± |0.0335| |
|
| | |acc_norm|0.2393|± |0.0335| |
|
|hendrycksTest-machine_learning | 1|acc |0.1875|± |0.0370| |
|
| | |acc_norm|0.1875|± |0.0370| |
|
|hendrycksTest-management | 1|acc |0.2039|± |0.0399| |
|
| | |acc_norm|0.2039|± |0.0399| |
|
|hendrycksTest-marketing | 1|acc |0.1795|± |0.0251| |
|
| | |acc_norm|0.1795|± |0.0251| |
|
|hendrycksTest-medical_genetics | 1|acc |0.3000|± |0.0461| |
|
| | |acc_norm|0.3000|± |0.0461| |
|
|hendrycksTest-miscellaneous | 1|acc |0.2644|± |0.0158| |
|
| | |acc_norm|0.2644|± |0.0158| |
|
|hendrycksTest-moral_disputes | 1|acc |0.2225|± |0.0224| |
|
| | |acc_norm|0.2225|± |0.0224| |
|
|hendrycksTest-moral_scenarios | 1|acc |0.2726|± |0.0149| |
|
| | |acc_norm|0.2726|± |0.0149| |
|
|hendrycksTest-nutrition | 1|acc |0.2353|± |0.0243| |
|
| | |acc_norm|0.2353|± |0.0243| |
|
|hendrycksTest-philosophy | 1|acc |0.2283|± |0.0238| |
|
| | |acc_norm|0.2283|± |0.0238| |
|
|hendrycksTest-prehistory | 1|acc |0.2099|± |0.0227| |
|
| | |acc_norm|0.2099|± |0.0227| |
|
|hendrycksTest-professional_accounting | 1|acc |0.2411|± |0.0255| |
|
| | |acc_norm|0.2411|± |0.0255| |
|
|hendrycksTest-professional_law | 1|acc |0.2458|± |0.0110| |
|
| | |acc_norm|0.2458|± |0.0110| |
|
|hendrycksTest-professional_medicine | 1|acc |0.3897|± |0.0296| |
|
| | |acc_norm|0.3897|± |0.0296| |
|
|hendrycksTest-professional_psychology | 1|acc |0.2141|± |0.0166| |
|
| | |acc_norm|0.2141|± |0.0166| |
|
|hendrycksTest-public_relations | 1|acc |0.1818|± |0.0369| |
|
| | |acc_norm|0.1818|± |0.0369| |
|
|hendrycksTest-security_studies | 1|acc |0.2490|± |0.0277| |
|
| | |acc_norm|0.2490|± |0.0277| |
|
|hendrycksTest-sociology | 1|acc |0.2537|± |0.0308| |
|
| | |acc_norm|0.2537|± |0.0308| |
|
|hendrycksTest-us_foreign_policy | 1|acc |0.2900|± |0.0456| |
|
| | |acc_norm|0.2900|± |0.0456| |
|
|hendrycksTest-virology | 1|acc |0.1807|± |0.0300| |
|
| | |acc_norm|0.1807|± |0.0300| |
|
|hendrycksTest-world_religions | 1|acc |0.1813|± |0.0295| |
|
| | |acc_norm|0.1813|± |0.0295| |
|
|
|
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 8 |
|
| Task |Version|Metric|Value | |Stderr| |
|
|----------|------:|------|-----:|---|-----:| |
|
|winogrande| 0|acc |0.5154|± | 0.014| |
|
|
|
hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/model-9gh18vfl:v25,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 8 |
|
|Task |Version|Metric|Value| |Stderr| |
|
|-----|------:|------|----:|---|-----:| |
|
|gsm8k| 0|acc | 0|± | 0| |
|
|
