metadata

license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
inference:
  parameters:
    max_new_tokens: 64
    do_sample: true
    temperature: 0.85
    repetition_penalty: 1.35
    no_repeat_ngram_size: 5
    eta_cutoff: 0.001
    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:
  - BEE-spoke-data/knowledge-inoc-concat-v1

verysmol_llama-v11-KIx2

Model description

This model is a fine-tuned version of v10 (refinedweb-3m dedup) further trained for 2 epochs on KI dataset.

It achieves the following results on the evaluation set:

Loss: 2.8876
Accuracy: 0.4502

evals

hf-causal-experimental (pretrained=pszemraj/verysmol_llama-v11-KIx2,revision=main,trust_remote_code=True,dtype='float'), limit: None, provide_description: False, num_fewshot: 0, batch_size: 16

Task	Version	Metric	Value		Stderr
arc_easy	0	acc	0.4024	±	0.0101
		acc_norm	0.3788	±	0.0100
boolq	1	acc	0.6199	±	0.0085
lambada_openai	0	ppl	111.9939	±	4.6906
		acc	0.2354	±	0.0059
openbookqa	0	acc	0.1440	±	0.0157
		acc_norm	0.2760	±	0.0200
piqa	0	acc	0.5713	±	0.0115
		acc_norm	0.5664	±	0.0116
winogrande	0	acc	0.5201	±	0.0140

Task	Version	Metric	Value		Stderr
arc_challenge	0	acc	0.1971	±	0.0116
		acc_norm	0.2278	±	0.0123

Task	Version	Metric	Value		Stderr
hellaswag	0	acc	0.2618	±	0.0088
		acc_norm	0.2797	±	0.0090

Task	Version	Metric	Value		Stderr
truthfulqa_mc	1	mc1	0.2509	±	0.0152
		mc2	0.4492	±	0.0156

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.00014
train_batch_size: 16
eval_batch_size: 16
seed: 17514
gradient_accumulation_steps: 8
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-06
lr_scheduler_type: inverse_sqrt
lr_scheduler_warmup_ratio: 0.05
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.0681	0.03	150	3.0689	0.4259
3.0113	0.07	300	3.0433	0.4278
2.9468	0.1	450	3.0362	0.4288
3.0162	0.13	600	3.0148	0.4326
2.9531	0.17	750	3.0012	0.4341
2.9282	0.2	900	2.9923	0.4358
2.9485	0.23	1050	2.9845	0.4357
2.9365	0.27	1200	2.9749	0.4375

...

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.8215	1.7	7650	2.8943	0.4496
2.7714	1.74	7800	2.8914	0.4501
2.8132	1.77	7950	2.8913	0.4500
2.8505	1.8	8100	2.8906	0.4502
2.8294	1.84	8250	2.8901	0.4502
2.7977	1.87	8400	2.8891	0.4499
2.7501	1.9	8550	2.8878	0.4505
2.8038	1.94	8700	2.8883	0.4504
2.7547	1.97	8850	2.8876	0.4502