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apriasmoro/
5401ffa9-6e3d-478c-aac2-e3aff6d7efc7

Name: 5401ffa9-6e3d-478c-aac2-e3aff6d7efc7
Availability: InStock

generated_from_trainer

jhflow/mistral7b-lora-multi-turn-v2

adapter

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See axolotl config

axolotl version: 0.10.0.dev0

yaml

adapter: lora
base_model: jhflow/mistral7b-lora-multi-turn-v2
bf16: true
chat_template: llama3
datasets:
- data_files:
  - 073ecb803828cfed_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/
  type:
    field_input: input
    field_instruction: instruct
    field_output: output
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
eval_max_new_tokens: 256
evals_per_epoch: 2
flash_attention: false
fp16: false
gradient_accumulation_steps: 1
gradient_checkpointing: true
group_by_length: true
hub_model_id: apriasmoro/5401ffa9-6e3d-478c-aac2-e3aff6d7efc7
learning_rate: 0.0002
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: false
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 269
micro_batch_size: 4
mlflow_experiment_name: /tmp/073ecb803828cfed_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
save_steps: 67
sequence_len: 2048
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: 589dc2a5-c919-4731-be25-e0afc45a1708
wandb_project: Gradients-On-Demand
wandb_run: apriasmoro
wandb_runid: 589dc2a5-c919-4731-be25-e0afc45a1708
warmup_steps: 100
weight_decay: 0.01

5401ffa9-6e3d-478c-aac2-e3aff6d7efc7

This model is a fine-tuned version of jhflow/mistral7b-lora-multi-turn-v2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.5244

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
distributed_type: multi-GPU
num_devices: 8
total_train_batch_size: 32
total_eval_batch_size: 32
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: 100
training_steps: 269

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0045	1	0.9024
0.6584	0.2036	45	0.6136
0.5876	0.4072	90	0.5672
0.5148	0.6109	135	0.5598
0.4828	0.8145	180	0.5327
0.5225	1.0181	225	0.5244

Framework versions

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