transformers safetensors llama text-generation generated_from_trainer dataset:JeanKaddour/minipile base_model:pszemraj/verysmol_llama-v7-KIx2 license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# 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   |