Instructions to use Kromtao/7f55a22d-27c6-40c0-9242-7c3a5f04e3f6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Kromtao/7f55a22d-27c6-40c0-9242-7c3a5f04e3f6 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-135M") model = PeftModel.from_pretrained(base_model, "Kromtao/7f55a22d-27c6-40c0-9242-7c3a5f04e3f6") - Notebooks
- Google Colab
- Kaggle
File size: 3,773 Bytes
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library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM2-135M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7f55a22d-27c6-40c0-9242-7c3a5f04e3f6
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: lora
base_model: unsloth/SmolLM2-135M
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 14efd411bd3f1df0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/14efd411bd3f1df0_train_data.json
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
eval_batch_size: 8
eval_max_new_tokens: 128
eval_steps: 2174
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: Kromtao/7f55a22d-27c6-40c0-9242-7c3a5f04e3f6
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
local_rank: null
logging_steps: 50
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: false
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 4348
micro_batch_size: 8
mlflow_experiment_name: /tmp/14efd411bd3f1df0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
save_steps: 2174
saves_per_epoch: null
seed: 9103
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: online
wandb_name: 05a93f74-543d-453e-bebf-b28930df5cdc
wandb_project: kr03
wandb_run: your_name
wandb_runid: 05a93f74-543d-453e-bebf-b28930df5cdc
warmup_steps: 100
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 7f55a22d-27c6-40c0-9242-7c3a5f04e3f6
This model is a fine-tuned version of [unsloth/SmolLM2-135M](https://huggingface.co/unsloth/SmolLM2-135M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7674
## 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: 8
- eval_batch_size: 8
- seed: 9103
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 100
- training_steps: 4348
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 2.4202 |
| 1.7645 | 0.2442 | 2174 | 1.7905 |
| 1.8049 | 0.4884 | 4348 | 1.7674 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |