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JVCGPT-Mini-beta

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metadata
library_name: transformers
tags:
  - generated_from_trainer
datasets:
  - 2025-01_conversations_truncated.jsonl
model-index:
  - name: outputs/
    results: []

Built with Axolotl

See axolotl config

axolotl version: 0.6.0

base_model: ./meta-llama_Llama-3.2-3B
# optionally might have model_type or tokenizer_type
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: 2025-01_conversations_truncated.jsonl
    type: chat_template
    chat_template: llama3
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      user:
        - human
      assistant:
        - gpt
      system:
        - system
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/
dataset_prepared_path: last_run_prepared

sequence_len: 4096
eval_sample_packing: false
sample_packing: true
pad_to_sequence_len: true

wandb_project: JVCGPT Light 3b base
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.000007

train_on_inputs: true
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 100
eval_table_size:
saves_per_epoch: 2
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
   pad_token: <|end_of_text|>
save_safetensors: true
save_total_limit: 10

outputs/

This model was trained from scratch on the 2025-01_conversations_truncated.jsonl dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1520

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: 7e-06
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • total_eval_batch_size: 8
  • optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 4

Training results

Training Loss Epoch Step Validation Loss
0.6055 1.0006 789 1.1893
0.5619 2.0006 1578 1.1576
0.4873 3.0006 2367 1.1522
1.2133 3.9917 3148 1.1520

Framework versions

  • Transformers 4.47.1
  • Pytorch 2.5.1+cu124
  • Datasets 3.2.0
  • Tokenizers 0.21.0