ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e2
04
<!-- 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"/> <details><summary>See axolotl config</summary>
axolotl version: 0.4.1
base_model: meta-llama/Meta-Llama-3.1-8B # same model you originally used
# Load your previously fine-tuned model as a PEFT adapter
peft_model: ahmedelgebaly/llama-3.1-8b-squadv2_e2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: ahmedelgebaly/SciQ_Alpaca
type: alpaca
split: train
test_datasets:
- path: ahmedelgebaly/SciQ_Alpaca
type: alpaca
split: validation
dataset_prepared_path:
output_dir: ./outputs/qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: llama-3.1-8b-squadv2_SciQ_e2
wandb_entity:
wandb_watch:
wandb_name: llama-3.1-8b-squadv2-v0_SciQ_e2
wandb_log_model:
hub_model_id: ahmedelgebaly/llama-3.1-8b-squadv2_SciQ_e2
gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 2
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: "<|end_of_text|>"
</details><br>
llama-3.1-8b-squadv2SciQe2
This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.9066
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: 0.0002
- trainbatchsize: 4
- evalbatchsize: 4
- seed: 42
- gradientaccumulationsteps: 4
- totaltrainbatch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 10
- num_epochs: 2
Training results
Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1