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SalesBot2_CoT_lora_w_neg

PEFT
Safetensors
llama
Generated from Trainer
4-bit precision
bitsandbytes
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metadata
base_model: NousResearch/Meta-Llama-3-8B
library_name: peft
license: other
tags:
  - generated_from_trainer
model-index:
  - name: outputs/salesagent-qlora-out
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.4.1

base_model: NousResearch/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: ../SalesAgent/train_CoT_comb.json
    type: sharegpt

    conversation: # Options (see Conversation 'name'): https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py
    field_human: human # Optional[str]. Human key to use for conversation.
    field_model: gpt # Optional[str]. Assistant key to use for conversation.
    # Add additional keys from your dataset as input or output roles
    roles:
      input: # Optional[List[str]]. These will be masked based on train_on_input
      output: # Optional[List[str]].:
dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./outputs/salesagent-qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: salesagent_neg
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 3
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|>"

outputs/salesagent-qlora-out

This model is a fine-tuned version of NousResearch/Meta-Llama-3-8B 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: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 32
  • total_eval_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: 3

Training results

Framework versions

  • PEFT 0.13.0
  • Transformers 4.45.1
  • Pytorch 2.4.1
  • Datasets 2.21.0
  • Tokenizers 0.20.1