See axolotl config
axolotl version: 0.4.1
adapter: lora
base_model: NousResearch/Yarn-Llama-2-7b-128k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 4f01bd2925bc8a13_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/4f01bd2925bc8a13_train_data.json
type:
field_instruction: Title
field_output: Genre
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: 5
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: fedovtt/08c45d92-b447-4bdb-87ed-81e5e7053752
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: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 74GiB
max_steps: 50
micro_batch_size: 4
mlflow_experiment_name: /tmp/4f01bd2925bc8a13_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 25
save_strategy: steps
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: null
wandb_mode: online
wandb_name: 08c45d92-b447-4bdb-87ed-81e5e7053752
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 08c45d92-b447-4bdb-87ed-81e5e7053752
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null
08c45d92-b447-4bdb-87ed-81e5e7053752
This model is a fine-tuned version of NousResearch/Yarn-Llama-2-7b-128k on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.3162
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
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH 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: 50
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
13.5674 | 0.0343 | 1 | 1.6728 |
11.7391 | 0.1373 | 4 | 1.4845 |
7.9807 | 0.2747 | 8 | 0.9227 |
6.5926 | 0.4120 | 12 | 0.6636 |
3.9636 | 0.5494 | 16 | 0.5392 |
3.0476 | 0.6867 | 20 | 0.4746 |
3.0432 | 0.8240 | 24 | 0.4330 |
3.4371 | 0.9614 | 28 | 0.4020 |
2.1396 | 1.0987 | 32 | 0.3588 |
1.6073 | 1.2361 | 36 | 0.3393 |
3.2822 | 1.3734 | 40 | 0.3231 |
1.7314 | 1.5107 | 44 | 0.3193 |
1.7266 | 1.6481 | 48 | 0.3162 |
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
- Downloads last month
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Model tree for fedovtt/08c45d92-b447-4bdb-87ed-81e5e7053752
Base model
NousResearch/Yarn-Llama-2-7b-128k