File size: 4,445 Bytes
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library_name: peft
base_model: samoline/5255b81c-a162-4f3a-9a96-f4d344cb99c8
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 47615d62-b7f3-4efd-b567-61a937215b9f
results: []
---
<!-- 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"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: qlora
auto_resume_from_checkpoints: true
base_model: samoline/5255b81c-a162-4f3a-9a96-f4d344cb99c8
bf16: auto
chat_template: llama3
dataset_prepared_path: null
dataset_processes: 6
datasets:
- data_files:
- 6af671d7f222ce26_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/6af671d7f222ce26_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 5
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: error577/47615d62-b7f3-4efd-b567-61a937215b9f
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: null
micro_batch_size: 4
mlflow_experiment_name: /tmp/6af671d7f222ce26_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 200
sequence_len: 256
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.005
wandb_entity: null
wandb_mode: online
wandb_name: 5b76a1a8-f96b-44f2-9330-30f496d3f59a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5b76a1a8-f96b-44f2-9330-30f496d3f59a
warmup_steps: 30
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 47615d62-b7f3-4efd-b567-61a937215b9f
This model is a fine-tuned version of [samoline/5255b81c-a162-4f3a-9a96-f4d344cb99c8](https://huggingface.co/samoline/5255b81c-a162-4f3a-9a96-f4d344cb99c8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8456
## 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: 4
- total_train_batch_size: 16
- 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: 30
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7552 | 0.0001 | 1 | 0.7747 |
| 1.0145 | 0.0219 | 200 | 0.8068 |
| 1.0147 | 0.0437 | 400 | 0.8067 |
| 0.7413 | 0.0656 | 600 | 0.8176 |
| 0.8655 | 0.0875 | 800 | 0.8182 |
| 0.5897 | 0.1094 | 1000 | 0.8338 |
| 0.866 | 0.1312 | 1200 | 0.8312 |
| 0.5207 | 0.1531 | 1400 | 0.8338 |
| 0.8087 | 0.1750 | 1600 | 0.8419 |
| 0.8481 | 0.1969 | 1800 | 0.8370 |
| 0.6158 | 0.2187 | 2000 | 0.8424 |
| 0.7539 | 0.2406 | 2200 | 0.8447 |
| 0.8562 | 0.2625 | 2400 | 0.8456 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |