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nttx/
13178e66-34b9-45d3-a754-523c19881ca7

Name: 13178e66-34b9-45d3-a754-523c19881ca7
Availability: InStock

generated_from_trainer

unsloth/SmolLM2-1.7B-Instruct

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: unsloth/SmolLM2-1.7B-Instruct
bf16: true
chat_template: llama3
dataloader_num_workers: 24
dataset_prepared_path: null
datasets:
- data_files:
  - 0c1367355c2510f8_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/0c1367355c2510f8_train_data.json
  type:
    field_instruction: instruction
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: 300
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
hub_model_id: nttx/13178e66-34b9-45d3-a754-523c19881ca7
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 3000
micro_batch_size: 2
mlflow_experiment_name: /tmp/0c1367355c2510f8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1000
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.999
  adam_epsilon: 1e-8
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 300
saves_per_epoch: null
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a6d954dd-91ca-429a-9051-83e5cd6e3724
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a6d954dd-91ca-429a-9051-83e5cd6e3724
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

13178e66-34b9-45d3-a754-523c19881ca7

This model is a fine-tuned version of unsloth/SmolLM2-1.7B-Instruct on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.2797

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
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 10
training_steps: 3000

Training results

Training Loss	Epoch	Step	Validation Loss
0.4547	0.0001	1	0.6432
0.2509	0.0244	300	0.3078
0.2003	0.0488	600	0.3000
0.2703	0.0732	900	0.2937
0.2414	0.0976	1200	0.2896
0.1817	0.1220	1500	0.2861
0.2008	0.1465	1800	0.2833
0.1987	0.1709	2100	0.2813
0.1985	0.1953	2400	0.2798
0.2195	0.2197	2700	0.2794
0.2734	0.2441	3000	0.2797

Framework versions

PEFT 0.13.2
Transformers 4.46.0
Pytorch 2.5.0+cu124
Datasets 3.0.1
Tokenizers 0.20.1