File size: 5,478 Bytes
9a6080b f44a0ea 4a8cba0 f44a0ea a50acce f44a0ea 9a6080b fc9bdd9 9a6080b 1cd271d 9a6080b 686386f 9a6080b fc9bdd9 9a6080b fc9bdd9 9a6080b fc9bdd9 9a6080b 686386f b22cdaf 686386f 9a6080b 4a8cba0 9a6080b 686386f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 |
---
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
---
<!-- 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. -->
# 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 |
---
|