exl2 quant (measurement.json in main branch)
check revisions for quants
See axolotl config
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: MangoHQ/Gryphe-3.5-16k-Subset
type: sharegpt
conversation: chatml
- path: Epiculous/Synthstruct-Gens-v1-Filtered-n-Cleaned
type: sharegpt
conversation: chatml
- path: anthracite-org/Stheno-Data-Filtered
type: sharegpt
conversation: chatml
- path: Epiculous/SynthRP-Gens-v1-Filtered-n-Cleaned
type: sharegpt
conversation: chatml
- path: lodrick-the-lafted/NopmWritingStruct
type: sharegpt
conversation: chatml
- path: anthracite-org/kalo-opus-instruct-22k-no-refusal
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: 16384
# sequence_len: 32768
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: tinymagnumv2
wandb_entity:
wandb_watch:
wandb_name: tinymagnumv2
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00002
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: 1
debug:
deepspeed:
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: 1.2014
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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 36
- num_epochs: 2
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.6733 | 0.0051 | 1 | 1.6590 |
| 1.4425 | 0.2523 | 49 | 1.3040 |
| 1.3564 | 0.5047 | 98 | 1.2451 |
| 1.333 | 0.7570 | 147 | 1.2201 |
| 1.2936 | 1.0093 | 196 | 1.2077 |
| 1.2235 | 1.2462 | 245 | 1.2041 |
| 1.2651 | 1.4986 | 294 | 1.2018 |
| 1.238 | 1.7509 | 343 | 1.2014 |
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1