license: apache-2.0
base_model: pszemraj/verysmol_llama-v7-KIx2
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: 0.0006
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
BEE-spoke-data/verysmol_llama-v8-minipile_x2
This is still a work-in-progress and should be treated as such.
Model description
This is an autogressive smol language model. It generates text.
It achieves the following results on the evaluation set:
- Loss: 2.7521
- Accuracy: 0.4686
Intended uses & limitations
Doing things. Limitations are that it is smol.
Additionally, <insert generic, emotionless, and corporate statement about bias in language models here>.
Data
Most recent training run was on JeanKaddour/minipile for 2 epochs. Otherwise, please refer to the below quote:
UnFoRtUnAtElY We'rE UnAbLe tO ShArE DeTaIlS AbOuT ThE TrAiNiNg aNd tHe dAtAsEtS (eXtRaCtEd fRoM ThE OpEn wEb) DuE To tHe hIgHlY CoMpEtItIvE NaTuRe oF ThE FiElD.
evals
| eval metrics | |
|---|---|
| epoch | 2.0 |
| eval_accuracy | 0.4685 |
| eval_loss | 2.7521 |
| eval_runtime | 0:00:03.89 |
| eval_samples | 300 |
| eval_samples_per_second | 77.049 |
| eval_steps_per_second | 9.759 |
| perplexity | 15.675 |
harness
some improvements and some degradations over prev versions. May indicate the last dataset in curricula matters/needs to be chosen specifically
hf-causal-experimental (pretrained=BEE-spoke-data/verysmol_llama-v8-minipile_x2,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 | 0.3662 | ± | 0.0099 |
| acc_norm | 0.3460 | ± | 0.0098 | ||
| boolq | 1 | acc | 0.6052 | ± | 0.0085 |
| lambada_openai | 0 | ppl | 156.8153 | ± | 6.5985 |
| acc | 0.2010 | ± | 0.0056 | ||
| openbookqa | 0 | acc | 0.1280 | ± | 0.0150 |
| acc_norm | 0.2660 | ± | 0.0198 | ||
| piqa | 0 | acc | 0.5865 | ± | 0.0115 |
| acc_norm | 0.5805 | ± | 0.0115 | ||
| winogrande | 0 | acc | 0.5217 | ± | 0.0140 |
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| arc_challenge | 0 | acc | 0.1877 | ± | 0.0114 |
| acc_norm | 0.2235 | ± | 0.0122 |
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| hellaswag | 0 | acc | 0.2622 | ± | 0.0088 |
| acc_norm | 0.2777 | ± | 0.0089 |
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| truthfulqa_mc | 1 | mc1 | 0.2705 | ± | 0.0156 |
| mc2 | 0.4729 | ± | 0.0155 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00015
- train_batch_size: 8
- eval_batch_size: 8
- seed: 5404
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-07
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.7625 | 0.02 | 200 | 2.8982 | 0.4457 |
| 2.7377 | 0.03 | 400 | 2.8812 | 0.4477 |
| 2.6883 | 0.05 | 600 | 2.8774 | 0.4489 |
| 2.7654 | 0.06 | 800 | 2.8811 | 0.4479 |
| 2.744 | 0.08 | 1000 | 2.8838 | 0.4464 |
| 2.6922 | 0.09 | 1200 | 2.8921 | 0.4461 |
| 2.7416 | 0.11 | 1400 | 2.8930 | 0.4464 |
| 2.7337 | 0.12 | 1600 | 2.8972 | 0.4465 |
| 2.7046 | 0.14 | 1800 | 2.8933 | 0.4472 |
| 2.673 | 0.15 | 2000 | 2.8926 | 0.4483 |
...
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.5155 | 1.88 | 24800 | 2.7524 | 0.4685 |
| 2.5092 | 1.89 | 25000 | 2.7522 | 0.4686 |
| 2.5093 | 1.91 | 25200 | 2.7523 | 0.4685 |
| 2.4574 | 1.92 | 25400 | 2.7521 | 0.4686 |
| 2.5137 | 1.94 | 25600 | 2.7522 | 0.4686 |
| 2.4598 | 1.95 | 25800 | 2.7521 | 0.4686 |
| 2.515 | 1.97 | 26000 | 2.7521 | 0.4685 |
| 2.5429 | 1.98 | 26200 | 2.7521 | 0.4686 |
| 2.4789 | 2.0 | 26400 | 2.7521 | 0.4686 |