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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: 0.1 |
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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 |
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for developing the award winning Halo series of video games. They also |
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made 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, |
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oranges, 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, |
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and another train leaves Station B at 10:00 AM and travels at 80 mph, when |
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will 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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--- |
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# Model Card for nano-phi-115M-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 application of quality filter to datasets resulting in 0.26B token. |
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The control is [kenhktsui/nano-phi-115M-control-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-control-v0.1), where full dataset (0.6B) is used. |
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Not much degradation in performance despite only using 42% of the data. |
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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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Yet, there are still large gaps (particularly in ARC, HellaSwag, MMLU and GSM8K) between nano-phi-115M-v0.1 and phi-2, where author will attempt to narrow down the gap in the future. |
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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 | kenhktsui/nano-phi-115M-v0.1|[kenhktsui/nano-phi-115M-control-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-control-v0.1)|[microsoft/phi-2](https://huggingface.co/microsoft/phi-2)| |
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|-----------------------|---------------------------|---------------------------|---------------------------| |
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| Model Para | 115M |115M |2.7B | |
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| Dataset Size | 0.26B |0.6B |250B | |
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| Training Token | 0.26B |0.6B |1.4T | |
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| Context Length |1024 |1024 |2048| |
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| Device |1xA100-40G|1xA100-40G |96xA100-80G| |
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| Training Time |2d4h |2d4h |14d| |
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| Metric | kenhktsui/nano-phi-115M-v0.1|[kenhktsui/nano-phi-115M-control-v0.1](https://huggingface.co/kenhktsui/nano-phi-115M-control-v0.1)|[microsoft/phi-2](https://huggingface.co/microsoft/phi-2) (Reproduced)| |
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|-----------------------|---------------------------|---------------------------|---------------------------| |
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| Avg. | 28.68 |28.75 |61.53 | |
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| ARC (25-shot) | 21.93 |21.67 |61.52 | |
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| HellaSwag (10-shot) | 27.87 |26.89 |75.13 | |
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| MMLU (5-shot) | 25.30 |24.76 |58.23 | |
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| TruthfulQA (0-shot) | 46.01 |47.69 |44.46 | |
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| Winogrande (5-shot) | 50.99 |51.46 |74.51 | |
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| GSM8K (5-shot) | 0.0 |0.0 |55.34 | |
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Details: |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-pegfss6f: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 |0.4263|± |0.0101| |
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| | |acc_norm|0.3864|± |0.0100| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-pegfss6f: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 |0.1826|± |0.0113| |
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| | |acc_norm|0.2193|± |0.0121| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-pegfss6f: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 |0.2733|± |0.0044| |
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| | |acc_norm|0.2787|± |0.0045| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-pegfss6f: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 |0.2521|± |0.0152| |
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| | |mc2 |0.4601|± |0.0154| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-pegfss6f: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 |0.2300|± |0.0423| |
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| | |acc_norm|0.2300|± |0.0423| |
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|hendrycksTest-anatomy | 1|acc |0.3111|± |0.0400| |
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| | |acc_norm|0.3111|± |0.0400| |
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|hendrycksTest-astronomy | 1|acc |0.2171|± |0.0336| |
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| | |acc_norm|0.2171|± |0.0336| |
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|hendrycksTest-business_ethics | 1|acc |0.2500|± |0.0435| |
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| | |acc_norm|0.2500|± |0.0435| |
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|hendrycksTest-clinical_knowledge | 1|acc |0.2226|± |0.0256| |
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| | |acc_norm|0.2226|± |0.0256| |
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|hendrycksTest-college_biology | 1|acc |0.2292|± |0.0351| |
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| | |acc_norm|0.2292|± |0.0351| |
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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 |0.2500|± |0.0435| |
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| | |acc_norm|0.2500|± |0.0435| |
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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 |0.2023|± |0.0306| |
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| | |acc_norm|0.2023|± |0.0306| |
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|hendrycksTest-college_physics | 1|acc |0.3235|± |0.0466| |
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| | |acc_norm|0.3235|± |0.0466| |
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|hendrycksTest-computer_security | 1|acc |0.2600|± |0.0441| |
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| | |acc_norm|0.2600|± |0.0441| |
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|hendrycksTest-conceptual_physics | 1|acc |0.2511|± |0.0283| |
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| | |acc_norm|0.2511|± |0.0283| |
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|hendrycksTest-econometrics | 1|acc |0.2281|± |0.0395| |
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| | |acc_norm|0.2281|± |0.0395| |
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|hendrycksTest-electrical_engineering | 1|acc |0.2276|± |0.0349| |
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| | |acc_norm|0.2276|± |0.0349| |
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|hendrycksTest-elementary_mathematics | 1|acc |0.2460|± |0.0222| |
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| | |acc_norm|0.2460|± |0.0222| |
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|hendrycksTest-formal_logic | 1|acc |0.1508|± |0.0320| |
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| | |acc_norm|0.1508|± |0.0320| |
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|hendrycksTest-global_facts | 1|acc |0.3000|± |0.0461| |
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| | |acc_norm|0.3000|± |0.0461| |
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|hendrycksTest-high_school_biology | 1|acc |0.3387|± |0.0269| |
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| | |acc_norm|0.3387|± |0.0269| |
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|hendrycksTest-high_school_chemistry | 1|acc |0.2906|± |0.0319| |
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| | |acc_norm|0.2906|± |0.0319| |
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|hendrycksTest-high_school_computer_science | 1|acc |0.3100|± |0.0465| |
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| | |acc_norm|0.3100|± |0.0465| |
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|hendrycksTest-high_school_european_history | 1|acc |0.2182|± |0.0323| |
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| | |acc_norm|0.2182|± |0.0323| |
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|hendrycksTest-high_school_geography | 1|acc |0.3232|± |0.0333| |
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| | |acc_norm|0.3232|± |0.0333| |
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|hendrycksTest-high_school_government_and_politics| 1|acc |0.2021|± |0.0290| |
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| | |acc_norm|0.2021|± |0.0290| |
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|hendrycksTest-high_school_macroeconomics | 1|acc |0.2487|± |0.0219| |
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| | |acc_norm|0.2487|± |0.0219| |
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|hendrycksTest-high_school_mathematics | 1|acc |0.2741|± |0.0272| |
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| | |acc_norm|0.2741|± |0.0272| |
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|hendrycksTest-high_school_microeconomics | 1|acc |0.3319|± |0.0306| |
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| | |acc_norm|0.3319|± |0.0306| |
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|hendrycksTest-high_school_physics | 1|acc |0.3179|± |0.0380| |
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| | |acc_norm|0.3179|± |0.0380| |
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|hendrycksTest-high_school_psychology | 1|acc |0.2477|± |0.0185| |
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| | |acc_norm|0.2477|± |0.0185| |
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|hendrycksTest-high_school_statistics | 1|acc |0.4722|± |0.0340| |
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| | |acc_norm|0.4722|± |0.0340| |
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|hendrycksTest-high_school_us_history | 1|acc |0.2696|± |0.0311| |
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| | |acc_norm|0.2696|± |0.0311| |
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|hendrycksTest-high_school_world_history | 1|acc |0.2152|± |0.0268| |
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| | |acc_norm|0.2152|± |0.0268| |
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|hendrycksTest-human_aging | 1|acc |0.1973|± |0.0267| |
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| | |acc_norm|0.1973|± |0.0267| |
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|hendrycksTest-human_sexuality | 1|acc |0.2824|± |0.0395| |
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| | |acc_norm|0.2824|± |0.0395| |
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|hendrycksTest-international_law | 1|acc |0.2231|± |0.0380| |
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| | |acc_norm|0.2231|± |0.0380| |
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|hendrycksTest-jurisprudence | 1|acc |0.2222|± |0.0402| |
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| | |acc_norm|0.2222|± |0.0402| |
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|hendrycksTest-logical_fallacies | 1|acc |0.2822|± |0.0354| |
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| | |acc_norm|0.2822|± |0.0354| |
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|hendrycksTest-machine_learning | 1|acc |0.2768|± |0.0425| |
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| | |acc_norm|0.2768|± |0.0425| |
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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 |0.2800|± |0.0451| |
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| | |acc_norm|0.2800|± |0.0451| |
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|hendrycksTest-miscellaneous | 1|acc |0.2746|± |0.0160| |
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| | |acc_norm|0.2746|± |0.0160| |
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|hendrycksTest-moral_disputes | 1|acc |0.2081|± |0.0219| |
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| | |acc_norm|0.2081|± |0.0219| |
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|hendrycksTest-moral_scenarios | 1|acc |0.2469|± |0.0144| |
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| | |acc_norm|0.2469|± |0.0144| |
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|hendrycksTest-nutrition | 1|acc |0.2647|± |0.0253| |
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| | |acc_norm|0.2647|± |0.0253| |
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|hendrycksTest-philosophy | 1|acc |0.1897|± |0.0223| |
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| | |acc_norm|0.1897|± |0.0223| |
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|hendrycksTest-prehistory | 1|acc |0.2377|± |0.0237| |
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| | |acc_norm|0.2377|± |0.0237| |
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|hendrycksTest-professional_accounting | 1|acc |0.2482|± |0.0258| |
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| | |acc_norm|0.2482|± |0.0258| |
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|hendrycksTest-professional_law | 1|acc |0.2464|± |0.0110| |
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| | |acc_norm|0.2464|± |0.0110| |
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|hendrycksTest-professional_medicine | 1|acc |0.4265|± |0.0300| |
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| | |acc_norm|0.4265|± |0.0300| |
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|hendrycksTest-professional_psychology | 1|acc |0.2614|± |0.0178| |
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| | |acc_norm|0.2614|± |0.0178| |
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|hendrycksTest-public_relations | 1|acc |0.1818|± |0.0369| |
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| | |acc_norm|0.1818|± |0.0369| |
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|hendrycksTest-security_studies | 1|acc |0.1959|± |0.0254| |
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| | |acc_norm|0.1959|± |0.0254| |
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|hendrycksTest-sociology | 1|acc |0.2289|± |0.0297| |
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| | |acc_norm|0.2289|± |0.0297| |
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|hendrycksTest-us_foreign_policy | 1|acc |0.2400|± |0.0429| |
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| | |acc_norm|0.2400|± |0.0429| |
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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 |0.2222|± |0.0319| |
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| | |acc_norm|0.2222|± |0.0319| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-pegfss6f: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 |0.5099|± | 0.014| |
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hf-causal-experimental (pretrained=/content/lm-evaluation-harness/artifacts/checkpoint-pegfss6f: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|± | 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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- **Funded by [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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- **Repository:** [More Information Needed] |
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## Uses |
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### Direct Use |
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## Bias, Risks, and Limitations |
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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 Procedure |
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#### Preprocessing [optional] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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## Evaluation |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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## Technical Specifications [optional] |
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## More Information [optional] |
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