rayonlabs/really-tiny-falcon-testing-databricks-dolly-15k-curated-en-e0db9855-1a0f-4fa4-9bf0-d34b5dbc4d74
07
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.4.1
adapter: lora
base_model: fxmarty/really-tiny-falcon-testing
bf16: auto
chat_template: llama3
cosine_min_lr_ratio: 0.1
data_processes: 4
dataset_prepared_path: null
datasets:
- data_files:
- 116ea3862184e752_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/116ea3862184e752_train_data.json
type:
field_input: new-context
field_instruction: new-instruction
field_output: new-response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map:
lm_head: 3
model.embed_tokens: 0
model.layers.0: 0
model.layers.1: 0
model.layers.10: 3
model.layers.11: 3
model.layers.2: 0
model.layers.3: 1
model.layers.4: 1
model.layers.5: 1
model.layers.6: 2
model.layers.7: 2
model.layers.8: 2
model.layers.9: 3
model.norm: 3
do_eval: true
early_stopping_patience: 1
eval_batch_size: 1
eval_sample_packing: false
eval_steps: 25
evaluation_strategy: steps
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: true
hub_model_id: sn56/e0db9855-1a0f-4fa4-9bf0-d34b5dbc4d74
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 0.3
max_memory:
0: 60GB
1: 70GB
2: 70GB
3: 70GB
cpu: 96GB
max_steps: 75
micro_batch_size: 1
mixed_precision: bf16
mlflow_experiment_name: /tmp/116ea3862184e752_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
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: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
torch_compile: false
torch_dtype: bfloat16
train_on_inputs: false
trust_remote_code: true
use_cache: false
val_set_size: 50
wandb_entity: null
wandb_mode: online
wandb_name: e0db9855-1a0f-4fa4-9bf0-d34b5dbc4d74
wandb_project: Public_TuningSN
wandb_runid: e0db9855-1a0f-4fa4-9bf0-d34b5dbc4d74
warmup_ratio: 0.05
weight_decay: 0.01
xformers_attention: null
</details><br>
e0db9855-1a0f-4fa4-9bf0-d34b5dbc4d74
This model is a fine-tuned version of fxmarty/really-tiny-falcon-testing on the None dataset. It achieves the following results on the evaluation set:
- Loss: 10.9545
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: 0.0001
- trainbatchsize: 1
- evalbatchsize: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradientaccumulationsteps: 32
- totaltrainbatch_size: 128
- totalevalbatch_size: 4
- optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizerargs=adambeta1=0.9,adambeta2=0.95,adam_epsilon=1e-5
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 3
- training_steps: 75
Training results
Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1