---
library_name: peft
license: mit
base_model: migtissera/Tess-v2.5-Phi-3-medium-128k-14B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d5f68bc0-8f1d-4153-b8f9-3997b83f289c
  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: migtissera/Tess-v2.5-Phi-3-medium-128k-14B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 8ee7aa1fc2919db9_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/8ee7aa1fc2919db9_train_data.json
  type:
    field_input: smcdel_problem
    field_instruction: premise
    field_output: hypothesis
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/d5f68bc0-8f1d-4153-b8f9-3997b83f289c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 714
micro_batch_size: 4
mlflow_experiment_name: /tmp/8ee7aa1fc2919db9_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
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: 100
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: 0bb02c1f-facb-4b07-a068-3be62f4842c5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0bb02c1f-facb-4b07-a068-3be62f4842c5
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# d5f68bc0-8f1d-4153-b8f9-3997b83f289c

This model is a fine-tuned version of [migtissera/Tess-v2.5-Phi-3-medium-128k-14B](https://huggingface.co/migtissera/Tess-v2.5-Phi-3-medium-128k-14B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3062

## 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_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: 10
- training_steps: 714

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 23.25         | 0.0018 | 1    | 2.9340          |
| 2.4594        | 0.1823 | 100  | 0.3277          |
| 2.4931        | 0.3646 | 200  | 0.3137          |
| 2.4953        | 0.5469 | 300  | 0.3135          |
| 2.7622        | 0.7293 | 400  | 0.3095          |
| 2.6484        | 0.9116 | 500  | 0.3067          |
| 2.1272        | 1.0939 | 600  | 0.3063          |
| 1.4705        | 1.2762 | 700  | 0.3062          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1