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

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

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

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

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

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

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

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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Open LLM Leaderboard Evaluation Results

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