library_name: transformers
language:
- en
inference:
parameters:
max_new_tokens: 64
do_sample: true
temperature: 0.8
repetition_penalty: 1.15
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
datasets:
- JeanKaddour/minipile
- pszemraj/simple_wikipedia_LM
- mattymchen/refinedweb-3m
- Locutusque/TM-DATA
- Skylion007/openwebtext
Model Card for nano-phi-115M-control-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 a dataset of 0.6B token.
This model acts as a control of kenhktsui/nano-phi-115M-v0.1 which applies quality filter to dataset resulting in small dataset.
It just took 2d 4h to train in Colab with a A100 40GB (~USD$ 100).
It achieves quite competitive results in evaluation given its training token, and training data size.
No alignment has been done yet.
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 | Value |
|---|---|
| Avg. | 28.75 |
| ARC (25-shot) | 21.67 |
| HellaSwag (10-shot) | 26.89 |
| MMLU (5-shot) | 24.76 |
| TruthfulQA (0-shot) | 47.69 |
| Winogrande (5-shot) | 51.46 |
| GSM8K (5-shot) | 0.0 |
Details:
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
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| arc_easy | 0 | acc | 0.3973 | ± | 0.0100 |
| acc_norm | 0.3531 | ± | 0.0098 |
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
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| arc_challenge | 0 | acc | 0.1843 | ± | 0.0113 |
| acc_norm | 0.2167 | ± | 0.0120 |
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
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| hellaswag | 0 | acc | 0.2682 | ± | 0.0044 |
| acc_norm | 0.2689 | ± | 0.0044 |
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
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| truthfulqa_mc | 1 | mc1 | 0.2619 | ± | 0.0154 |
| mc2 | 0.4769 | ± | 0.0156 |
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
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| hendrycksTest-abstract_algebra | 1 | acc | 0.2200 | ± | 0.0416 |
| acc_norm | 0.2200 | ± | 0.0416 | ||
| hendrycksTest-anatomy | 1 | acc | 0.3333 | ± | 0.0407 |
| acc_norm | 0.3333 | ± | 0.0407 | ||
| hendrycksTest-astronomy | 1 | acc | 0.2895 | ± | 0.0369 |
| acc_norm | 0.2895 | ± | 0.0369 | ||
| hendrycksTest-business_ethics | 1 | acc | 0.2000 | ± | 0.0402 |
| acc_norm | 0.2000 | ± | 0.0402 | ||
| hendrycksTest-clinical_knowledge | 1 | acc | 0.2189 | ± | 0.0254 |
| acc_norm | 0.2189 | ± | 0.0254 | ||
| hendrycksTest-college_biology | 1 | acc | 0.2222 | ± | 0.0348 |
| acc_norm | 0.2222 | ± | 0.0348 | ||
| hendrycksTest-college_chemistry | 1 | acc | 0.1700 | ± | 0.0378 |
| acc_norm | 0.1700 | ± | 0.0378 | ||
| hendrycksTest-college_computer_science | 1 | acc | 0.3000 | ± | 0.0461 |
| acc_norm | 0.3000 | ± | 0.0461 | ||
| hendrycksTest-college_mathematics | 1 | acc | 0.2500 | ± | 0.0435 |
| acc_norm | 0.2500 | ± | 0.0435 | ||
| hendrycksTest-college_medicine | 1 | acc | 0.1965 | ± | 0.0303 |
| acc_norm | 0.1965 | ± | 0.0303 | ||
| hendrycksTest-college_physics | 1 | acc | 0.2353 | ± | 0.0422 |
| acc_norm | 0.2353 | ± | 0.0422 | ||
| hendrycksTest-computer_security | 1 | acc | 0.2000 | ± | 0.0402 |
| acc_norm | 0.2000 | ± | 0.0402 | ||
| hendrycksTest-conceptual_physics | 1 | acc | 0.2043 | ± | 0.0264 |
| acc_norm | 0.2043 | ± | 0.0264 | ||
| hendrycksTest-econometrics | 1 | acc | 0.2456 | ± | 0.0405 |
| acc_norm | 0.2456 | ± | 0.0405 | ||
| hendrycksTest-electrical_engineering | 1 | acc | 0.2621 | ± | 0.0366 |
| acc_norm | 0.2621 | ± | 0.0366 | ||
| hendrycksTest-elementary_mathematics | 1 | acc | 0.2566 | ± | 0.0225 |
| acc_norm | 0.2566 | ± | 0.0225 | ||
| hendrycksTest-formal_logic | 1 | acc | 0.1587 | ± | 0.0327 |
| acc_norm | 0.1587 | ± | 0.0327 | ||
| hendrycksTest-global_facts | 1 | acc | 0.1600 | ± | 0.0368 |
| acc_norm | 0.1600 | ± | 0.0368 | ||
| hendrycksTest-high_school_biology | 1 | acc | 0.3226 | ± | 0.0266 |
| acc_norm | 0.3226 | ± | 0.0266 | ||
| hendrycksTest-high_school_chemistry | 1 | acc | 0.2956 | ± | 0.0321 |
| acc_norm | 0.2956 | ± | 0.0321 | ||
| hendrycksTest-high_school_computer_science | 1 | acc | 0.2800 | ± | 0.0451 |
| acc_norm | 0.2800 | ± | 0.0451 | ||
| hendrycksTest-high_school_european_history | 1 | acc | 0.2606 | ± | 0.0343 |
| acc_norm | 0.2606 | ± | 0.0343 | ||
| hendrycksTest-high_school_geography | 1 | acc | 0.2626 | ± | 0.0314 |
| acc_norm | 0.2626 | ± | 0.0314 | ||
| hendrycksTest-high_school_government_and_politics | 1 | acc | 0.2176 | ± | 0.0298 |
| acc_norm | 0.2176 | ± | 0.0298 | ||
| hendrycksTest-high_school_macroeconomics | 1 | acc | 0.2128 | ± | 0.0208 |
| acc_norm | 0.2128 | ± | 0.0208 | ||
| hendrycksTest-high_school_mathematics | 1 | acc | 0.2630 | ± | 0.0268 |
| acc_norm | 0.2630 | ± | 0.0268 | ||
| hendrycksTest-high_school_microeconomics | 1 | acc | 0.2227 | ± | 0.0270 |
| acc_norm | 0.2227 | ± | 0.0270 | ||
| hendrycksTest-high_school_physics | 1 | acc | 0.3046 | ± | 0.0376 |
| acc_norm | 0.3046 | ± | 0.0376 | ||
| hendrycksTest-high_school_psychology | 1 | acc | 0.2055 | ± | 0.0173 |
| acc_norm | 0.2055 | ± | 0.0173 | ||
| hendrycksTest-high_school_statistics | 1 | acc | 0.4815 | ± | 0.0341 |
| acc_norm | 0.4815 | ± | 0.0341 | ||
| hendrycksTest-high_school_us_history | 1 | acc | 0.2059 | ± | 0.0284 |
| acc_norm | 0.2059 | ± | 0.0284 | ||
| hendrycksTest-high_school_world_history | 1 | acc | 0.2574 | ± | 0.0285 |
| acc_norm | 0.2574 | ± | 0.0285 | ||
| hendrycksTest-human_aging | 1 | acc | 0.2063 | ± | 0.0272 |
| acc_norm | 0.2063 | ± | 0.0272 | ||
| hendrycksTest-human_sexuality | 1 | acc | 0.2443 | ± | 0.0377 |
| acc_norm | 0.2443 | ± | 0.0377 | ||
| hendrycksTest-international_law | 1 | acc | 0.2727 | ± | 0.0407 |
| acc_norm | 0.2727 | ± | 0.0407 | ||
| hendrycksTest-jurisprudence | 1 | acc | 0.2130 | ± | 0.0396 |
| acc_norm | 0.2130 | ± | 0.0396 | ||
| hendrycksTest-logical_fallacies | 1 | acc | 0.2515 | ± | 0.0341 |
| acc_norm | 0.2515 | ± | 0.0341 | ||
| hendrycksTest-machine_learning | 1 | acc | 0.2321 | ± | 0.0401 |
| acc_norm | 0.2321 | ± | 0.0401 | ||
| 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 | 0.3000 | ± | 0.0461 |
| acc_norm | 0.3000 | ± | 0.0461 | ||
| hendrycksTest-miscellaneous | 1 | acc | 0.2631 | ± | 0.0157 |
| acc_norm | 0.2631 | ± | 0.0157 | ||
| hendrycksTest-moral_disputes | 1 | acc | 0.2457 | ± | 0.0232 |
| acc_norm | 0.2457 | ± | 0.0232 | ||
| hendrycksTest-moral_scenarios | 1 | acc | 0.2682 | ± | 0.0148 |
| acc_norm | 0.2682 | ± | 0.0148 | ||
| hendrycksTest-nutrition | 1 | acc | 0.2451 | ± | 0.0246 |
| acc_norm | 0.2451 | ± | 0.0246 | ||
| hendrycksTest-philosophy | 1 | acc | 0.2605 | ± | 0.0249 |
| acc_norm | 0.2605 | ± | 0.0249 | ||
| hendrycksTest-prehistory | 1 | acc | 0.2932 | ± | 0.0253 |
| acc_norm | 0.2932 | ± | 0.0253 | ||
| hendrycksTest-professional_accounting | 1 | acc | 0.2340 | ± | 0.0253 |
| acc_norm | 0.2340 | ± | 0.0253 | ||
| hendrycksTest-professional_law | 1 | acc | 0.2432 | ± | 0.0110 |
| acc_norm | 0.2432 | ± | 0.0110 | ||
| hendrycksTest-professional_medicine | 1 | acc | 0.4301 | ± | 0.0301 |
| acc_norm | 0.4301 | ± | 0.0301 | ||
| hendrycksTest-professional_psychology | 1 | acc | 0.2369 | ± | 0.0172 |
| acc_norm | 0.2369 | ± | 0.0172 | ||
| hendrycksTest-public_relations | 1 | acc | 0.2091 | ± | 0.0390 |
| acc_norm | 0.2091 | ± | 0.0390 | ||
| hendrycksTest-security_studies | 1 | acc | 0.2408 | ± | 0.0274 |
| acc_norm | 0.2408 | ± | 0.0274 | ||
| hendrycksTest-sociology | 1 | acc | 0.2388 | ± | 0.0301 |
| acc_norm | 0.2388 | ± | 0.0301 | ||
| hendrycksTest-us_foreign_policy | 1 | acc | 0.2600 | ± | 0.0441 |
| acc_norm | 0.2600 | ± | 0.0441 | ||
| hendrycksTest-virology | 1 | acc | 0.2048 | ± | 0.0314 |
| acc_norm | 0.2048 | ± | 0.0314 | ||
| hendrycksTest-world_religions | 1 | acc | 0.2047 | ± | 0.0309 |
| acc_norm | 0.2047 | ± | 0.0309 |
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
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| winogrande | 0 | acc | 0.5146 | ± | 0.014 |
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
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| gsm8k | 0 | acc | 0 | ± | 0 |
Model Details
Model Description
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]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]