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---
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](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).
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](https://huggingface.co/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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| Metric | kenhktsui/nano-phi-115M-v0.1|kenhktsui/nano-phi-115M-v0.1 (6000 steps)|[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)|
|-----------------------|---------------------------|---------------------------|---------------------------|---------------------------|
| 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](https://huggingface.co/kenhktsui/nano-phi-115M-control-v0.1)|[microsoft/phi-2](https://huggingface.co/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|
## Model Details
### Model Description
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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.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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## Training Details
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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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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_kenhktsui__nano-phi-115M-v0.1)
| 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| |