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--- |
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library_name: transformers |
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language: |
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- en |
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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: 0.8 |
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repetition_penalty: 1.15 |
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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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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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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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datasets: |
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- JeanKaddour/minipile |
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- pszemraj/simple_wikipedia_LM |
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- mattymchen/refinedweb-3m |
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- Locutusque/TM-DATA |
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- Skylion007/openwebtext |
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--- |
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# Model Card for nano-phi-115M-control-v0.1 |
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Inspired by [Phi2](https://huggingface.co/microsoft/phi-2), and open source small language model attempts like [smol_llama-101M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA). |
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Pre-trained with training 7B token from scratch, with a dataset of 0.6B token. |
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This model acts as a control of [kenhktsui/nano-phi-115M-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-v0.1) which applies quality filter to dataset resulting in small dataset. |
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It just took 2d 4h to train in Colab with a A100 40GB (~USD$ 100). |
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It achieves quite competitive results in evaluation given its training token, and training data size. |
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No alignment has been done yet. |
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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: 115M |
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- training: |
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- global steps: 14,000 |
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## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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| Metric | Value | |
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|-----------------------|---------------------------| |
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| Avg. | 28.75 | |
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| ARC (25-shot) | 21.67 | |
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| HellaSwag (10-shot) | 26.89 | |
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| MMLU (5-shot) | 24.76 | |
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| TruthfulQA (0-shot) | 47.69 | |
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| Winogrande (5-shot) | 51.46 | |
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| GSM8K (5-shot) | 0.0 | |
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Details: |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-ehgq969i:v13,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16 |
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| Task |Version| Metric |Value | |Stderr| |
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|--------|------:|--------|-----:|---|-----:| |
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|arc_easy| 0|acc |0.3973|± |0.0100| |
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| | |acc_norm|0.3531|± |0.0098| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-ehgq969i:v13,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 25, batch_size: 16 |
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| Task |Version| Metric |Value | |Stderr| |
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|-------------|------:|--------|-----:|---|-----:| |
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|arc_challenge| 0|acc |0.1843|± |0.0113| |
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| | |acc_norm|0.2167|± |0.0120| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-ehgq969i:v13,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 10, batch_size: 16 |
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| Task |Version| Metric |Value | |Stderr| |
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|---------|------:|--------|-----:|---|-----:| |
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|hellaswag| 0|acc |0.2682|± |0.0044| |
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| | |acc_norm|0.2689|± |0.0044| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-ehgq969i:v13,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16 |
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| Task |Version|Metric|Value | |Stderr| |
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|-------------|------:|------|-----:|---|-----:| |
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|truthfulqa_mc| 1|mc1 |0.2619|± |0.0154| |
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| | |mc2 |0.4769|± |0.0156| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-ehgq969i:v13,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 16 |
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| Task |Version| Metric |Value | |Stderr| |
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|-------------------------------------------------|------:|--------|-----:|---|-----:| |
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|hendrycksTest-abstract_algebra | 1|acc |0.2200|± |0.0416| |
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| | |acc_norm|0.2200|± |0.0416| |
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|hendrycksTest-anatomy | 1|acc |0.3333|± |0.0407| |
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| | |acc_norm|0.3333|± |0.0407| |
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|hendrycksTest-astronomy | 1|acc |0.2895|± |0.0369| |
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| | |acc_norm|0.2895|± |0.0369| |
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|hendrycksTest-business_ethics | 1|acc |0.2000|± |0.0402| |
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| | |acc_norm|0.2000|± |0.0402| |
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|hendrycksTest-clinical_knowledge | 1|acc |0.2189|± |0.0254| |
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| | |acc_norm|0.2189|± |0.0254| |
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|hendrycksTest-college_biology | 1|acc |0.2222|± |0.0348| |
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| | |acc_norm|0.2222|± |0.0348| |
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|hendrycksTest-college_chemistry | 1|acc |0.1700|± |0.0378| |
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| | |acc_norm|0.1700|± |0.0378| |
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|hendrycksTest-college_computer_science | 1|acc |0.3000|± |0.0461| |
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| | |acc_norm|0.3000|± |0.0461| |
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|hendrycksTest-college_mathematics | 1|acc |0.2500|± |0.0435| |
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| | |acc_norm|0.2500|± |0.0435| |
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|hendrycksTest-college_medicine | 1|acc |0.1965|± |0.0303| |
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| | |acc_norm|0.1965|± |0.0303| |
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|hendrycksTest-college_physics | 1|acc |0.2353|± |0.0422| |
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| | |acc_norm|0.2353|± |0.0422| |
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|hendrycksTest-computer_security | 1|acc |0.2000|± |0.0402| |
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| | |acc_norm|0.2000|± |0.0402| |
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|hendrycksTest-conceptual_physics | 1|acc |0.2043|± |0.0264| |
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| | |acc_norm|0.2043|± |0.0264| |
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|hendrycksTest-econometrics | 1|acc |0.2456|± |0.0405| |
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| | |acc_norm|0.2456|± |0.0405| |
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|hendrycksTest-electrical_engineering | 1|acc |0.2621|± |0.0366| |
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| | |acc_norm|0.2621|± |0.0366| |
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|hendrycksTest-elementary_mathematics | 1|acc |0.2566|± |0.0225| |
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| | |acc_norm|0.2566|± |0.0225| |
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|hendrycksTest-formal_logic | 1|acc |0.1587|± |0.0327| |
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| | |acc_norm|0.1587|± |0.0327| |
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|hendrycksTest-global_facts | 1|acc |0.1600|± |0.0368| |
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| | |acc_norm|0.1600|± |0.0368| |
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|hendrycksTest-high_school_biology | 1|acc |0.3226|± |0.0266| |
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| | |acc_norm|0.3226|± |0.0266| |
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|hendrycksTest-high_school_chemistry | 1|acc |0.2956|± |0.0321| |
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| | |acc_norm|0.2956|± |0.0321| |
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|hendrycksTest-high_school_computer_science | 1|acc |0.2800|± |0.0451| |
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| | |acc_norm|0.2800|± |0.0451| |
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|hendrycksTest-high_school_european_history | 1|acc |0.2606|± |0.0343| |
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| | |acc_norm|0.2606|± |0.0343| |
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|hendrycksTest-high_school_geography | 1|acc |0.2626|± |0.0314| |
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| | |acc_norm|0.2626|± |0.0314| |
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|hendrycksTest-high_school_government_and_politics| 1|acc |0.2176|± |0.0298| |
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| | |acc_norm|0.2176|± |0.0298| |
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|hendrycksTest-high_school_macroeconomics | 1|acc |0.2128|± |0.0208| |
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| | |acc_norm|0.2128|± |0.0208| |
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|hendrycksTest-high_school_mathematics | 1|acc |0.2630|± |0.0268| |
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| | |acc_norm|0.2630|± |0.0268| |
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|hendrycksTest-high_school_microeconomics | 1|acc |0.2227|± |0.0270| |
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| | |acc_norm|0.2227|± |0.0270| |
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|hendrycksTest-high_school_physics | 1|acc |0.3046|± |0.0376| |
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| | |acc_norm|0.3046|± |0.0376| |
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|hendrycksTest-high_school_psychology | 1|acc |0.2055|± |0.0173| |
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| | |acc_norm|0.2055|± |0.0173| |
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|hendrycksTest-high_school_statistics | 1|acc |0.4815|± |0.0341| |
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| | |acc_norm|0.4815|± |0.0341| |
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|hendrycksTest-high_school_us_history | 1|acc |0.2059|± |0.0284| |
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| | |acc_norm|0.2059|± |0.0284| |
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|hendrycksTest-high_school_world_history | 1|acc |0.2574|± |0.0285| |
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| | |acc_norm|0.2574|± |0.0285| |
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|hendrycksTest-human_aging | 1|acc |0.2063|± |0.0272| |
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| | |acc_norm|0.2063|± |0.0272| |
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|hendrycksTest-human_sexuality | 1|acc |0.2443|± |0.0377| |
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| | |acc_norm|0.2443|± |0.0377| |
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|hendrycksTest-international_law | 1|acc |0.2727|± |0.0407| |
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| | |acc_norm|0.2727|± |0.0407| |
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|hendrycksTest-jurisprudence | 1|acc |0.2130|± |0.0396| |
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| | |acc_norm|0.2130|± |0.0396| |
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|hendrycksTest-logical_fallacies | 1|acc |0.2515|± |0.0341| |
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| | |acc_norm|0.2515|± |0.0341| |
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|hendrycksTest-machine_learning | 1|acc |0.2321|± |0.0401| |
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| | |acc_norm|0.2321|± |0.0401| |
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|hendrycksTest-management | 1|acc |0.2039|± |0.0399| |
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| | |acc_norm|0.2039|± |0.0399| |
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|hendrycksTest-marketing | 1|acc |0.1966|± |0.0260| |
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| | |acc_norm|0.1966|± |0.0260| |
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|hendrycksTest-medical_genetics | 1|acc |0.3000|± |0.0461| |
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| | |acc_norm|0.3000|± |0.0461| |
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|hendrycksTest-miscellaneous | 1|acc |0.2631|± |0.0157| |
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| | |acc_norm|0.2631|± |0.0157| |
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|hendrycksTest-moral_disputes | 1|acc |0.2457|± |0.0232| |
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| | |acc_norm|0.2457|± |0.0232| |
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|hendrycksTest-moral_scenarios | 1|acc |0.2682|± |0.0148| |
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| | |acc_norm|0.2682|± |0.0148| |
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|hendrycksTest-nutrition | 1|acc |0.2451|± |0.0246| |
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| | |acc_norm|0.2451|± |0.0246| |
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|hendrycksTest-philosophy | 1|acc |0.2605|± |0.0249| |
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| | |acc_norm|0.2605|± |0.0249| |
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|hendrycksTest-prehistory | 1|acc |0.2932|± |0.0253| |
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| | |acc_norm|0.2932|± |0.0253| |
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|hendrycksTest-professional_accounting | 1|acc |0.2340|± |0.0253| |
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| | |acc_norm|0.2340|± |0.0253| |
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|hendrycksTest-professional_law | 1|acc |0.2432|± |0.0110| |
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| | |acc_norm|0.2432|± |0.0110| |
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|hendrycksTest-professional_medicine | 1|acc |0.4301|± |0.0301| |
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| | |acc_norm|0.4301|± |0.0301| |
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|hendrycksTest-professional_psychology | 1|acc |0.2369|± |0.0172| |
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| | |acc_norm|0.2369|± |0.0172| |
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|hendrycksTest-public_relations | 1|acc |0.2091|± |0.0390| |
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| | |acc_norm|0.2091|± |0.0390| |
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|hendrycksTest-security_studies | 1|acc |0.2408|± |0.0274| |
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| | |acc_norm|0.2408|± |0.0274| |
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|hendrycksTest-sociology | 1|acc |0.2388|± |0.0301| |
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| | |acc_norm|0.2388|± |0.0301| |
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|hendrycksTest-us_foreign_policy | 1|acc |0.2600|± |0.0441| |
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| | |acc_norm|0.2600|± |0.0441| |
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|hendrycksTest-virology | 1|acc |0.2048|± |0.0314| |
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| | |acc_norm|0.2048|± |0.0314| |
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|hendrycksTest-world_religions | 1|acc |0.2047|± |0.0309| |
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| | |acc_norm|0.2047|± |0.0309| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-ehgq969i:v13,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 16 |
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| Task |Version|Metric|Value | |Stderr| |
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|----------|------:|------|-----:|---|-----:| |
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|winogrande| 0|acc |0.5146|± | 0.014| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-ehgq969i:v13,use_accelerate=false,trust_remote_code=True), limit: None, provide_description: False, num_fewshot: 5, batch_size: 16 |
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|Task |Version|Metric|Value| |Stderr| |
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|-----|------:|------|----:|---|-----:| |
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|gsm8k| 0|acc | 0|± | 0| |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [More Information Needed] |
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## Uses |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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## Training Details |
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### Training Data |
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#### Training Hyperparameters |
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## Evaluation |
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