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Alignment-Lab-AI
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ABS-adapter

PEFT
Safetensors
llama
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
ABS-adapter
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---
base_model: unsloth/Meta-Llama-3.1-8B
library_name: peft
license: llama3.1
tags:
- generated_from_trainer
model-index:
- name: outputs/out/qlora-llama3_1-8b
  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

base_model: unsloth/Meta-Llama-3.1-8B
tokenizer_type: AutoTokenizer

#load_in_8bit: true
load_in_4bit: true
strict: false

datasets:
  - path: Alignment-Lab-AI/claudeopus-sharegpt
    type: sharegpt


chat_template: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out/qlora-llama3_1-8b
save_safetensors: true

adapter: qlora

sequence_len: 8192
sample_packing: true
#pad_to_sequence_len: true

lora_r: 16
lora_alpha: 64
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true

gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00035

train_on_inputs: false
group_by_length: true
bf16: true
tf32: true
eval_sample_packing: true
pad_to_sequence_len: true
wandb_project: ARBIUS-8b

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
logging_steps: 1
flash_attention: true
neft_tune_alpha: 3
warmup_ratio: 0.5
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
fsdp:
  - full_shard
  - auto_wrap
fsdp_config:
  fsdp_limit_all_gathers: true
  fsdp_sync_module_states: true
  fsdp_offload_params: true
  fsdp_use_orig_params: false
  fsdp_cpu_ram_efficient_loading: true
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
  fsdp_state_dict_type: FULL_STATE_DICT
  fsdp_sharding_strategy: FULL_SHARD
special_tokens:
  pad_token: <|end_of_text|>
  bos_token: <|begin_of_text|>
  eos_token: <|eot_id|>

```

</details><br>

# outputs/out/qlora-llama3_1-8b

This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset.

## 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: 0.00035
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 6
- gradient_accumulation_steps: 16
- total_train_batch_size: 192
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 34
- num_epochs: 2

### Training results



### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.1.2+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1