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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: 24.15
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: 29.99
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: 25.46
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: 44.3
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: 51.45
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
- HuggingFaceTB/cosmopedia
---
# Model Card for nano-phi-192M-v0.1
This is a continual effort from [kenhktsui/nano-phi-115M-v0.1](https://huggingface.co./kenhktsui/nano-phi-115M-v0.1).
The model is not aligned.
Major differences:
- bigger tokenizer's vocab size
- addition of [HuggingFaceTB/cosmopedia](https://huggingface.co./datasets/HuggingFaceTB/cosmopedia) as training dataset
- training token: 19B vs 7B
## 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-192M-v0.1")
pipe("I am a machine learning researcher. I work on", max_new_tokens=50, repetition_penalty=10.0)
```
## 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: 192M
- training:
- global steps: 36,000
## [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
| 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)|
|-----------------------|---------------------------|---------------------------|---------------------------|
| Avg. |29.24 | 28.68 |61.53 |
| ARC (25-shot) |24.15 | 21.93 |61.52 |
| HellaSwag (10-shot) | 29.99 | 27.87 |75.13 |
| MMLU (5-shot) |25.46 | 25.30 |58.23 |
| TruthfulQA (0-shot) |44.30 | 46.01 |44.46 |
| Winogrande (5-shot) |51.54 | 50.99 |74.51 |
| GSM8K (5-shot) |0.0 | 0.0 |55.34 |
Details:
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
| Task |Version| Metric |Value | |Stderr|
|--------|------:|--------|-----:|---|-----:|
|arc_easy| 0|acc |0.4596|± |0.0102|
| | |acc_norm|0.4070|± |0.0101|
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|
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
| Task |Version| Metric |Value | |Stderr|
|---------|------:|--------|-----:|---|-----:|
|hellaswag| 0|acc |0.2833|± |0.0045|
| | |acc_norm|0.2999|± |0.0046|
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
| Task |Version|Metric|Value | |Stderr|
|-------------|------:|------|-----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.2583|± |0.0153|
| | |mc2 |0.4430|± |0.0152|
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|
|-------------------------------------------------|------:|--------|-----:|---|-----:|
|hendrycksTest-abstract_algebra | 1|acc |0.2200|± |0.0416|
| | |acc_norm|0.2200|± |0.0416|
|hendrycksTest-anatomy | 1|acc |0.2593|± |0.0379|
| | |acc_norm|0.2593|± |0.0379|
|hendrycksTest-astronomy | 1|acc |0.1711|± |0.0306|
| | |acc_norm|0.1711|± |0.0306|
|hendrycksTest-business_ethics | 1|acc |0.2400|± |0.0429|
| | |acc_norm|0.2400|± |0.0429|
|hendrycksTest-clinical_knowledge | 1|acc |0.2566|± |0.0269|
| | |acc_norm|0.2566|± |0.0269|
|hendrycksTest-college_biology | 1|acc |0.2639|± |0.0369|
| | |acc_norm|0.2639|± |0.0369|
|hendrycksTest-college_chemistry | 1|acc |0.1800|± |0.0386|
| | |acc_norm|0.1800|± |0.0386|
|hendrycksTest-college_computer_science | 1|acc |0.3300|± |0.0473|
| | |acc_norm|0.3300|± |0.0473|
|hendrycksTest-college_mathematics | 1|acc |0.3000|± |0.0461|
| | |acc_norm|0.3000|± |0.0461|
|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|
|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|
|