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saifrahmed/your_model_name
your_model_name is a machine learning model from saifrahmed. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- apache-2.0
- Model type
- llama
- Access
- Public
- Created
- Jul 9, 2024
- Updated
- Jul 9, 2024
- SHA
- aef1cc07
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
<details><summary>See axolotl config</summary>
axolotl version: 0.4.1
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: mhenrichsen/alpaca_2k_test
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out
sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
outputs/lora-out
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2115
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: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.4615 | 0.08 | 1 | 1.4899 |
| 1.3846 | 0.24 | 3 | 1.4859 |
| 1.3667 | 0.48 | 6 | 1.4399 |
| 1.267 | 0.72 | 9 | 1.3382 |
| 1.2276 | 0.96 | 12 | 1.2941 |
| 1.2515 | 1.16 | 15 | 1.2793 |
| 1.2275 | 1.4 | 18 | 1.2556 |
| 1.1351 | 1.6400 | 21 | 1.2347 |
| 1.2701 | 1.88 | 24 | 1.2253 |
| 1.1487 | 2.08 | 27 | 1.2213 |
| 1.1518 | 2.32 | 30 | 1.2209 |
| 1.1942 | 2.56 | 33 | 1.2171 |
| 1.1122 | 2.8 | 36 | 1.2147 |
| 1.1513 | 3.04 | 39 | 1.2139 |
| 1.1887 | 3.24 | 42 | 1.2128 |
| 1.1011 | 3.48 | 45 | 1.2114 |
| 1.1887 | 3.7200 | 48 | 1.2115 |
Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- peft
- Type
- llama
- License
- apache-2.0
- Created
- Jul 9, 2024
- Updated
- Jul 9, 2024