File size: 3,922 Bytes
e9e32c0 |
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 |
---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
datasets:
- Magpie-Align/Llama-3-8B-Self-Instruct-100K
model-index:
- name: Llama-3-8B-Self-Instruct-100K
results: []
---
# QuantFactory/Llama-3-8B-Self-Instruct-100K-GGUF
This is quantized version of [Magpie-Align/Llama-3-8B-Self-Instruct-100K](https://huggingface.co/Magpie-Align/Llama-3-8B-Self-Instruct-100K) created using llama.cpp
# Original Model Card
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
chat_template: llama3
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Magpie-Align/Llama-3-8B-Self-Instruct-100K
type: sharegpt
conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: axolotl_out/Llama-3-8B-self-instruct-100K
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3-8B-Self-Instruct
wandb_log_model:
hub_model_id: Magpie-Align/Llama-3-8B-Self-Instruct-100K
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# Llama-3-8B-Self-Instruct-100K
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the Magpie-Align/Llama-3-8B-Self-Instruct-100K dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6245
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3442 | 0.0190 | 1 | 2.3110 |
| 0.9581 | 0.2095 | 11 | 1.1476 |
| 0.8258 | 0.4190 | 22 | 0.9256 |
| 0.717 | 0.6286 | 33 | 0.7341 |
| 0.6746 | 0.8381 | 44 | 0.6497 |
| 0.5601 | 1.0333 | 55 | 0.6268 |
| 0.5571 | 1.2429 | 66 | 0.6285 |
| 0.538 | 1.4524 | 77 | 0.6258 |
| 0.548 | 1.6619 | 88 | 0.6251 |
| 0.5467 | 1.8714 | 99 | 0.6245 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|