adapter: lora
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
bf16: false
bnb_4bit_compute_dtype: float16
bnb_4bit_quant_type: nf4
bnb_4bit_use_double_quant: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - b49a85028a8c0615_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_instruction: instruct
    field_output: output
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hf_upload_public: true
hf_upload_repo_type: model
hub_model_id: apriasmoro/df078e90-e591-41af-8103-53f2a603f0a6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 10
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_steps: 453
micro_batch_size: 4
mixed_precision: 'no'
mlflow_experiment_name: /tmp/b49a85028a8c0615_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
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: 67
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 2bef41c6-9d59-4a28-a028-6256225ed636
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: 2bef41c6-9d59-4a28-a028-6256225ed636
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

df078e90-e591-41af-8103-53f2a603f0a6

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Llama-70B on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.7074

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: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_BNB 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: 10
training_steps: 453

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0023	1	5.5957
1.2885	0.2645	114	0.9492
0.7491	0.5290	228	0.7519
0.5803	0.7935	342	0.7074

Framework versions

PEFT 0.15.2
Transformers 4.51.3
Pytorch 2.5.1+cu124
Datasets 3.5.1
Tokenizers 0.21.1