Llama-3-6B-v0.1-adapters

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

base_model: prince-canuma/Llama-3-6B-v0
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: prince-canuma/fineweb-CC-MAIN-2024-10-1B-en
    type: completion
    split: train
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./llama-3-6b
save_safetensors: true
adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: false
pad_to_sequence_len: false

lora_r: 128
lora_alpha: 128
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:


wandb_project: llama-3-6b
wandb_entity: 
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 2
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-4

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: 100
evals_per_epoch: 4
eval_table_size:
save_steps: 4000
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
    pad_token: "<|reserved_special_token_0|>"

</details><br>

llama-3-6b

This model is a fine-tuned version of prince-canuma/Llama-3-6B-v0.1 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.4942

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
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 100
• num_epochs: 2

Training results

Framework versions

• PEFT 0.10.0
• Transformers 4.40.0.dev0
• Pytorch 2.2.0+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0