README.md

---
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: []
---

<!-- 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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
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|>
```

</details><br>

# Meta-Llama-3-8Bee

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 `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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__Meta-Llama-3-8Bee)

|      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|