---
base_model: meta-llama/Meta-Llama-3-8B
tags:
- generated_from_trainer
model-index:
- name: llama-3-8b-2.9.3
  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: `0.4.1`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

# load_in_4bit: true

chat_template: chatml
datasets:
  - path: /workspace/datasets/dolphin201-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/SystemChat_filtered_sharegpt.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/SystemChat_multilingual_sharegpt.jsonl
    type: sharegpt
    conversation: chatml
  # - path: /workspace/datasets/SystemChat-2.0-Arabic/SystemChatArabic_sharegpt.jsonl
  #   type: sharegpt
  #   conversation: chatml
  - path: /workspace/datasets/dolphin-coder-translate-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/dolphin-coder-codegen-sharegpt2.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/not_samantha_norefusals.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/Orca-Math-resort-unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/agent_instruct_react_unfiltered.jsonl
    type: sharegpt  
    conversation: chatml
  - path: /workspace/datasets/toolbench_instruct_j1s1_3k_unfiltered.jsonl
    type: sharegpt  
    conversation: chatml
  - path: /workspace/datasets/toolbench_negative_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/toolbench_react_10p_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/toolbench_tflan_cot_30p_unfiltered.jsonl
    type: sharegpt
    conversation: chatml
  - path: /workspace/datasets/openhermes200k_unfiltered.jsonl
    type: sharegpt 
    conversation: chatml

dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./llama-3-8b-2.9.3

sequence_len: 8192
sample_packing: false
pad_to_sequence_len: false

# adapter: qlora
# lora_r: 16
# lora_alpha: 32
# lora_dropout: 0.05
# lora_target_modules:
#   - q_proj
#   - k_proj
#   - v_proj
#   - o_proj
#   - gate_proj
#   - up_proj
#   - down_proj

wandb_project: 2.9.3-llama-3-8b
# wandb_entity: oaaic
# wandb_watch:
# wandb_name:
# wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
# max_grad_norm: 1.0

train_on_inputs: false
group_by_length: false
bf16: true
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
logging_steps: 1
flash_attention: true
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
warmup_steps: 10
evals_per_epoch: 2
saves_per_epoch: 2
save_total_limit: 2
weight_decay: 0.1
special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
tokens:
  - "<|im_start|>"
  - "<|im_end|>"

```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/lucasatkins81/2.9.3-llama-3-8b/runs/tufa1uut)
# llama-3-8b-2.9.3

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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5771

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 1.005         | 0.0001 | 1     | 0.9649          |
| 0.6468        | 0.5000 | 5058  | 0.6022          |
| 0.6648        | 1.0000 | 10116 | 0.5731          |
| 0.4983        | 1.5000 | 15174 | 0.5668          |
| 0.394         | 2.0000 | 20232 | 0.5478          |
| 0.3182        | 2.4999 | 25290 | 0.5781          |
| 0.2916        | 2.9999 | 30348 | 0.5771          |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1