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VERSIL91/
5ccf0954-4539-4b8e-b5e2-7ea744169299

Name: 5ccf0954-4539-4b8e-b5e2-7ea744169299
Availability: InStock

generated_from_trainer

unsloth/Qwen2-7B

adapter

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

adapter: lora
base_model: unsloth/Qwen2-7B
bf16: false
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 22a3375d601f5255_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/22a3375d601f5255_train_data.json
  type:
    field_instruction: question
    field_output: context
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
devices:
- 0
- 1
- 2
- 3
- 4
- 5
- 6
- 7
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: null
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: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 1
mlflow_experiment_name: /tmp/22a3375d601f5255_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
num_gpus: 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
saves_per_epoch: 4
sequence_len: 4056
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: sn56-miner
wandb_mode: disabled
wandb_name: null
wandb_project: god
wandb_run: n1le
wandb_runid: null
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

781933bf-438a-4747-b44c-89eac63ae5c1

This model is a fine-tuned version of unsloth/Qwen2-7B on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.0893

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: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 16
total_eval_batch_size: 4
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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.4026	0.0009	1	1.4490
1.5323	0.0028	3	1.4160
1.3197	0.0056	6	1.2489
1.0192	0.0084	9	1.0893

Framework versions

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

VERSIL91/5ccf0954-4539-4b8e-b5e2-7ea744169299

781933bf-438a-4747-b44c-89eac63ae5c1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

VERSIL91/
5ccf0954-4539-4b8e-b5e2-7ea744169299