metadata
language:
- en
license: llama3
tags:
- axolotl
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- BEE-spoke-data/bees-internal
pipeline_tag: text-generation
model-index:
- name: Meta-Llama-3-8Bee
results: []
See axolotl config
axolotl version: 0.4.0
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
strict: false
# dataset
datasets:
- path: BEE-spoke-data/bees-internal
type: completion # format from earlier
field: text # Optional[str] default: text, field to use for completion data
val_set_size: 0.05
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false
# WANDB
wandb_project: llama3-8bee
wandb_entity: pszemraj
wandb_watch: gradients
wandb_name: llama3-8bee-8192
hub_model_id: pszemraj/Meta-Llama-3-8Bee
hub_strategy: every_save
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-5
load_in_8bit: false
load_in_4bit: false
bf16: auto
fp16:
tf32: true
torch_compile: true # requires >= torch 2.0, may sometimes cause problems
torch_compile_backend: inductor # Optional[str]
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
logging_steps: 10
xformers_attention:
flash_attention: true
warmup_steps: 25
# hyperparams for freq of evals, saving, etc
evals_per_epoch: 3
saves_per_epoch: 3
save_safetensors: true
save_total_limit: 1 # Checkpoints saved at a time
output_dir: ./output-axolotl/output-model-gamma
resume_from_checkpoint:
deepspeed:
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
Meta-Llama-3-8Bee
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the BEE-spoke-data/bees-internal dataset (continued pretraining).
It achieves the following results on the evaluation set:
- Loss: 2.3319
Intended uses & limitations
- unveiling knowledge about bees and apiary practice
- needs further tuning to be used in 'instruct' type settings
Training and evaluation data
🐝🍯
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
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0 | 1 | 2.5339 |
| 2.3719 | 0.33 | 232 | 2.3658 |
| 2.2914 | 0.67 | 464 | 2.3319 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.3.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 14.49 |
| IFEval (0-Shot) | 19.51 |
| BBH (3-Shot) | 24.20 |
| MATH Lvl 5 (4-Shot) | 3.85 |
| GPQA (0-shot) | 8.50 |
| MuSR (0-shot) | 6.24 |
| MMLU-PRO (5-shot) | 24.66 |