l3bgi-sft-qlora-r64

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

axolotl version: 0.4.1

# 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>

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l3bgi-sft-qlora-r64

This model is a fine-tuned version of 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

Framework versions

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