nano-phi-115M-v0.1 / README.md
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metadata
language:
  - en
license: mit
library_name: transformers
inference:
  parameters:
    max_new_tokens: 64
    do_sample: true
    temperature: 0.1
    repetition_penalty: 10
    no_repeat_ngram_size: 4
    eta_cutoff: 0.0006
    renormalize_logits: true
widget:
  - text: My name is El Microondas the Wise, and
    example_title: El Microondas
  - text: Kennesaw State University is a public
    example_title: Kennesaw State University
  - text: >-
      Bungie Studios is an American video game developer. They are most famous
      for developing the award winning Halo series of video games. They also
      made Destiny. The studio was founded
    example_title: Bungie
  - text: The Mona Lisa is a world-renowned painting created by
    example_title: Mona Lisa
  - text: >-
      The Harry Potter series, written by J.K. Rowling, begins with the book
      titled
    example_title: Harry Potter Series
  - text: >-
      Question: I have cities, but no houses. I have mountains, but no trees. I
      have water, but no fish. What am I?

      Answer:
    example_title: Riddle
  - text: The process of photosynthesis involves the conversion of
    example_title: Photosynthesis
  - text: >-
      Jane went to the store to buy some groceries. She picked up apples,
      oranges, and a loaf of bread. When she got home, she realized she forgot
    example_title: Story Continuation
  - text: >-
      Problem 2: If a train leaves Station A at 9:00 AM and travels at 60 mph,
      and another train leaves Station B at 10:00 AM and travels at 80 mph, when
      will they meet if the distance between the stations is 300 miles?

      To determine
    example_title: Math Problem
  - text: In the context of computer programming, an algorithm is
    example_title: Algorithm Definition
pipeline_tag: text-generation
model-index:
  - name: nano-phi-115M-v0.1
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 21.93
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 27.86
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 25.34
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 46
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 50.83
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 0
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kenhktsui/nano-phi-115M-v0.1
          name: Open LLM Leaderboard
datasets:
  - kenhktsui/minipile_quality_score_v1
  - kenhktsui/simple_wikipedia_LM_quality_score_v1
  - kenhktsui/refinedweb-3m_quality_score_v1
  - kenhktsui/TM-DATA_quality_score_v1
  - kenhktsui/openwebtext_quality_score_v1

Model Card for nano-phi-115M-v0.1

Inspired by Phi2, and open source small language model attempts like smol_llama-101M-GQA.
Pre-trained with training 7B token from scratch, with application of quality filter to datasets resulting in 0.26B token.
The control is kenhktsui/nano-phi-115M-control-v0.1, where full dataset (0.6B) is used.
Not much degradation in performance despite only using 42% of the data due to the effective quality filter ("quality_score_v1" > 0.5). In fact, upon inspection, the 6000 steps chkpt achieves similar performance as this model, signaling underlying effective training due to high quality data. It just took 1d to train in Colab with a A100 40GB (<USD$ 50).
It achieves quite competitive results in evaluation given its training token, and training data size.
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. No alignment has been done yet.

How to use

To use the model, you will need transformer version >= 4.37.2

pip install transformers>=4.37.2
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kenhktsui/nano-phi-115M-v0.1")
pipe("I am a machine learning researcher. I work on", max_new_tokens=50, repetition_penalty=10.0)
# [{'generated_text': 'I am a machine learning researcher. I work on the problem of finding patterns in data, and it is not easy to find them all at once!\nThe first step was searching for pattern matching algorithms that are used by many people who have never seen an algorithm before (or even if they do).'}]

Some metrics

  • model
    • hidden_size: 768
    • num_key_value_heads: 8 (grouped query attention)
    • num_attention_heads: 24
    • num_hidden_layers: 6
    • context length: 1024
    • total params: 115M
  • training:
    • global steps: 14,000

Open LLM Leaderboard Evaluation Results

Metric kenhktsui/nano-phi-115M-v0.1 kenhktsui/nano-phi-115M-v0.1 (6000 steps) kenhktsui/nano-phi-115M-control-v0.1 microsoft/phi-2
Model Para 115M 115M 115M 2.7B
Dataset Size 0.26B 0.26B 0.6B 250B
Training Token 7B 3B 7B 1.4T
Context Length 1024 1024 1024 2048
Device 1xA100-40G 1xA100-40G 1xA100-40G 96xA100-80G
Training Time 2d4h 1d 2d4h 14d
Metric kenhktsui/nano-phi-115M-v0.1 kenhktsui/nano-phi-115M-v0.1 (6000 steps) kenhktsui/nano-phi-115M-control-v0.1 microsoft/phi-2 (Reproduced)
Avg. 28.68 29.03 28.75 61.53
ARC (25-shot) 21.93 22.27 21.67 61.52
HellaSwag (10-shot) 27.87 26.88 26.89 75.13
MMLU (5-shot) 25.30 25.01 24.76 58.23
TruthfulQA (0-shot) 46.01 48.03 47.69 44.46
Winogrande (5-shot) 50.99 52.01 51.46 74.51
GSM8K (5-shot) 0.0 0.0 0.0 55.34

Details:

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

Task Version Metric Value Stderr
arc_easy 0 acc 0.4263 ± 0.0101
acc_norm 0.3864 ± 0.0100

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

Task Version Metric Value Stderr
arc_challenge 0 acc 0.1826 ± 0.0113
acc_norm 0.2193 ± 0.0121

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

Task Version Metric Value Stderr
hellaswag 0 acc 0.2733 ± 0.0044
acc_norm 0.2787 ± 0.0045

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

Task Version Metric Value Stderr
truthfulqa_mc 1 mc1 0.2521 ± 0.0152
mc2 0.4601 ± 0.0154

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

Task Version Metric Value Stderr
hendrycksTest-abstract_algebra 1 acc 0.2300 ± 0.0423
acc_norm 0.2300 ± 0.0423
hendrycksTest-anatomy 1 acc 0.3111 ± 0.0400
acc_norm 0.3111 ± 0.0400
hendrycksTest-astronomy 1 acc 0.2171 ± 0.0336
acc_norm 0.2171 ± 0.0336
hendrycksTest-business_ethics 1 acc 0.2500 ± 0.0435
acc_norm 0.2500 ± 0.0435
hendrycksTest-clinical_knowledge 1 acc 0.2226 ± 0.0256
acc_norm 0.2226 ± 0.0256
hendrycksTest-college_biology 1 acc 0.2292 ± 0.0351
acc_norm 0.2292 ± 0.0351
hendrycksTest-college_chemistry 1 acc 0.1700 ± 0.0378
acc_norm 0.1700 ± 0.0378
hendrycksTest-college_computer_science 1 acc 0.2500 ± 0.0435
acc_norm 0.2500 ± 0.0435
hendrycksTest-college_mathematics 1 acc 0.2500 ± 0.0435
acc_norm 0.2500 ± 0.0435
hendrycksTest-college_medicine 1 acc 0.2023 ± 0.0306
acc_norm 0.2023 ± 0.0306
hendrycksTest-college_physics 1 acc 0.3235 ± 0.0466
acc_norm 0.3235 ± 0.0466
hendrycksTest-computer_security 1 acc 0.2600 ± 0.0441
acc_norm 0.2600 ± 0.0441
hendrycksTest-conceptual_physics 1 acc 0.2511 ± 0.0283
acc_norm 0.2511 ± 0.0283
hendrycksTest-econometrics 1 acc 0.2281 ± 0.0395
acc_norm 0.2281 ± 0.0395
hendrycksTest-electrical_engineering 1 acc 0.2276 ± 0.0349
acc_norm 0.2276 ± 0.0349
hendrycksTest-elementary_mathematics 1 acc 0.2460 ± 0.0222
acc_norm 0.2460 ± 0.0222
hendrycksTest-formal_logic 1 acc 0.1508 ± 0.0320
acc_norm 0.1508 ± 0.0320
hendrycksTest-global_facts 1 acc 0.3000 ± 0.0461
acc_norm 0.3000 ± 0.0461
hendrycksTest-high_school_biology 1 acc 0.3387 ± 0.0269
acc_norm 0.3387 ± 0.0269
hendrycksTest-high_school_chemistry 1 acc 0.2906 ± 0.0319
acc_norm 0.2906 ± 0.0319
hendrycksTest-high_school_computer_science 1 acc 0.3100 ± 0.0465
acc_norm 0.3100 ± 0.0465
hendrycksTest-high_school_european_history 1 acc 0.2182 ± 0.0323
acc_norm 0.2182 ± 0.0323
hendrycksTest-high_school_geography 1 acc 0.3232 ± 0.0333
acc_norm 0.3232 ± 0.0333
hendrycksTest-high_school_government_and_politics 1 acc 0.2021 ± 0.0290
acc_norm 0.2021 ± 0.0290
hendrycksTest-high_school_macroeconomics 1 acc 0.2487 ± 0.0219
acc_norm 0.2487 ± 0.0219
hendrycksTest-high_school_mathematics 1 acc 0.2741 ± 0.0272
acc_norm 0.2741 ± 0.0272
hendrycksTest-high_school_microeconomics 1 acc 0.3319 ± 0.0306
acc_norm 0.3319 ± 0.0306
hendrycksTest-high_school_physics 1 acc 0.3179 ± 0.0380
acc_norm 0.3179 ± 0.0380
hendrycksTest-high_school_psychology 1 acc 0.2477 ± 0.0185
acc_norm 0.2477 ± 0.0185
hendrycksTest-high_school_statistics 1 acc 0.4722 ± 0.0340
acc_norm 0.4722 ± 0.0340
hendrycksTest-high_school_us_history 1 acc 0.2696 ± 0.0311
acc_norm 0.2696 ± 0.0311
hendrycksTest-high_school_world_history 1 acc 0.2152 ± 0.0268
acc_norm 0.2152 ± 0.0268
hendrycksTest-human_aging 1 acc 0.1973 ± 0.0267
acc_norm 0.1973 ± 0.0267
hendrycksTest-human_sexuality 1 acc 0.2824 ± 0.0395
acc_norm 0.2824 ± 0.0395
hendrycksTest-international_law 1 acc 0.2231 ± 0.0380
acc_norm 0.2231 ± 0.0380
hendrycksTest-jurisprudence 1 acc 0.2222 ± 0.0402
acc_norm 0.2222 ± 0.0402
hendrycksTest-logical_fallacies 1 acc 0.2822 ± 0.0354
acc_norm 0.2822 ± 0.0354
hendrycksTest-machine_learning 1 acc 0.2768 ± 0.0425
acc_norm 0.2768 ± 0.0425
hendrycksTest-management 1 acc 0.2039 ± 0.0399
acc_norm 0.2039 ± 0.0399
hendrycksTest-marketing 1 acc 0.1966 ± 0.0260
acc_norm 0.1966 ± 0.0260
hendrycksTest-medical_genetics 1 acc 0.2800 ± 0.0451
acc_norm 0.2800 ± 0.0451
hendrycksTest-miscellaneous 1 acc 0.2746 ± 0.0160
acc_norm 0.2746 ± 0.0160
hendrycksTest-moral_disputes 1 acc 0.2081 ± 0.0219
acc_norm 0.2081 ± 0.0219
hendrycksTest-moral_scenarios 1 acc 0.2469 ± 0.0144
acc_norm 0.2469 ± 0.0144
hendrycksTest-nutrition 1 acc 0.2647 ± 0.0253
acc_norm 0.2647 ± 0.0253
hendrycksTest-philosophy 1 acc 0.1897 ± 0.0223
acc_norm 0.1897 ± 0.0223
hendrycksTest-prehistory 1 acc 0.2377 ± 0.0237
acc_norm 0.2377 ± 0.0237
hendrycksTest-professional_accounting 1 acc 0.2482 ± 0.0258
acc_norm 0.2482 ± 0.0258
hendrycksTest-professional_law 1 acc 0.2464 ± 0.0110
acc_norm 0.2464 ± 0.0110
hendrycksTest-professional_medicine 1 acc 0.4265 ± 0.0300
acc_norm 0.4265 ± 0.0300
hendrycksTest-professional_psychology 1 acc 0.2614 ± 0.0178
acc_norm 0.2614 ± 0.0178
hendrycksTest-public_relations 1 acc 0.1818 ± 0.0369
acc_norm 0.1818 ± 0.0369
hendrycksTest-security_studies 1 acc 0.1959 ± 0.0254
acc_norm 0.1959 ± 0.0254
hendrycksTest-sociology 1 acc 0.2289 ± 0.0297
acc_norm 0.2289 ± 0.0297
hendrycksTest-us_foreign_policy 1 acc 0.2400 ± 0.0429
acc_norm 0.2400 ± 0.0429
hendrycksTest-virology 1 acc 0.2048 ± 0.0314
acc_norm 0.2048 ± 0.0314
hendrycksTest-world_religions 1 acc 0.2222 ± 0.0319
acc_norm 0.2222 ± 0.0319

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

Task Version Metric Value Stderr
winogrande 0 acc 0.5099 ± 0.014

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

Task Version Metric Value Stderr
gsm8k 0 acc 0.0 ± 0.0

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 28.66
AI2 Reasoning Challenge (25-Shot) 21.93
HellaSwag (10-Shot) 27.86
MMLU (5-Shot) 25.34
TruthfulQA (0-shot) 46.00
Winogrande (5-shot) 50.83
GSM8k (5-shot) 0.00