axolotl version: 0.4.1
base_model: IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
type: sharegpt
conversation: chatml
- path: NewEden/Kalo-Opus-Instruct-22k-Refusal-Murdered
type: sharegpt
conversation: chatml
- path: Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
type: sharegpt
conversation: chatml
- path: NewEden/Gryphe-Sonnet-3.5-35k-Subset
type: sharegpt
conversation: chatml
- path: NewEden/Claude-Instruct-5k
type: sharegpt
conversation: chatml
- path: Magpie-Align/Magpie-Gemma2-Pro-200K-Filtered
type: sharegpt
conversation: chatml
chat_template: chatml
val_set_size: 0.01
output_dir: ./outputs/out
adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
sequence_len: 8192
# sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: true
wandb_project: Neo4B
wandb_entity:
wandb_watch:
wandb_name: Neo4B
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 2
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001
weight_decay: 0.05
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3.json
fsdp:
fsdp_config:
special_tokens:
pad_token: <|finetune_right_pad_id|>
outputs/out
This model is a fine-tuned version of IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.5576
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
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- 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: 27
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2446 | 0.0070 | 1 | 2.0325 |
| 0.8915 | 0.2532 | 36 | 1.6714 |
| 0.8262 | 0.5064 | 72 | 1.5993 |
| 0.7705 | 0.7596 | 108 | 1.5687 |
| 0.7547 | 1.0015 | 144 | 1.5541 |
| 0.6979 | 1.2547 | 180 | 1.5614 |
| 0.6854 | 1.5079 | 216 | 1.5596 |
| 0.6836 | 1.7611 | 252 | 1.5576 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1