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fozziethebeat
/
l3bgi-sft-qlora-r64

PEFT
Safetensors
llama
axolotl
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
l3bgi-sft-qlora-r64
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---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: peft
license: llama3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: l3bgi-sft-qlora-r64
  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/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
# Allow cli options to override these settings.
strict: false

# Base model settings.
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tokenizer_config: meta-llama/Meta-Llama-3-8B-Instruct
model_type: AutoModelForCausalLM

# Wandb settings
wandb_entity: collinear
wandb_project: template-training
wandb_name: l3smi-sft-qlora-r64

# Output settings
save_safetensors: true
hub_model_id: fozziethebeat/l3bgi-sft-qlora-r64
dataset_prepared_path: data/l3bgi-sft-qlora-r64
output_dir: models/l3bgi-sft-qlora-r64

# Data format settings
chat_template: llama3
datasets:
  - path: fozziethebeat/alpaca_messages_2k_test
    split: train
    type: chat_template
    chat_template: llama3
    field_messages: messages 
    message_field_role: role
    message_field_content: content
test_datasets:
  - path: fozziethebeat/alpaca_messages_2k_test
    split: test
    type: chat_template
    chat_template: llama3
    field_messages: messages 
    message_field_role: role
    message_field_content: content

# Data packing settings
sequence_len: 512
train_on_inputs: false
pad_to_sequence_len: true
group_by_length: false
sample_packing: false
eval_sample_packing: false

# Adapter settings
adapter: qlora
lora_model_dir:
load_in_8bit: false
load_in_4bit: true
lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

# Computation Format settings
bf16: true
fp16:
tf32: false

# Trainer settings
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 1e-5
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
#flash_attention: true

warmup_steps: 10
eval_table_size:
eval_max_new_tokens: 128
evals_per_epoch: 4
saves_per_epoch: 1
debug:
weight_decay: 0.01
special_tokens:
  pad_token: <|end_of_text|>
deepspeed:
fsdp:

```

</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/collinear/template-training/runs/pav37wt6)
# l3bgi-sft-qlora-r64

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0220

## 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
- gradient_accumulation_steps: 2
- total_train_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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.0859        | 0.0022 | 1    | 1.3374          |
| 0.9847        | 0.2497 | 111  | 1.1122          |
| 1.203         | 0.4994 | 222  | 1.0451          |
| 1.3916        | 0.7492 | 333  | 1.0307          |
| 0.7893        | 0.9989 | 444  | 1.0251          |
| 1.0244        | 1.2486 | 555  | 1.0228          |
| 0.6814        | 1.4983 | 666  | 1.0221          |
| 0.9408        | 1.7480 | 777  | 1.0224          |
| 1.0832        | 1.9978 | 888  | 1.0220          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1