You can deploy this model and start earning money today!

Rodo-Sami/
6953bdbc-3639-4f1c-8d28-26628e7981a1

Name: 6953bdbc-3639-4f1c-8d28-26628e7981a1
Availability: InStock

generated_from_trainer

NousResearch/Nous-Capybara-7B-V1

adapter

mit

Views

No views yet

See axolotl config

axolotl version: 0.6.0

yaml

adapter: lora
base_model: NousResearch/Nous-Capybara-7B-V1
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - eb740de30ff7fe93_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/eb740de30ff7fe93_train_data.json
  type:
    field_input: title
    field_instruction: question
    field_output: answer
    format: '{instruction} {input}'
    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: 16
gradient_checkpointing: false
group_by_length: true
hub_model_id: Rodo-Sami/6953bdbc-3639-4f1c-8d28-26628e7981a1
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: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/eb740de30ff7fe93_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
saves_per_epoch: 4
sequence_len: 1024
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: offline
wandb_name: e2c64dd8-ac0d-4315-9953-54dcef591325
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: e2c64dd8-ac0d-4315-9953-54dcef591325
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

6953bdbc-3639-4f1c-8d28-26628e7981a1

This model is a fine-tuned version of NousResearch/Nous-Capybara-7B-V1 on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.6209

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: 16
total_train_batch_size: 32
optimizer: Use adamw_bnb_8bit 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: 1036

Training results

Training Loss	Epoch	Step	Validation Loss
1.8559	0.2501	259	1.8184
1.6623	0.5002	518	1.7169
1.492	0.7503	777	1.6220
3.5089	1.0008	1036	1.6209

Framework versions

PEFT 0.14.0
Transformers 4.46.3
Pytorch 2.5.1+cu124
Datasets 3.1.0
Tokenizers 0.20.3