transformers safetensors llama text-generation generated_from_trainer conversational dataset:shisa-ai/shisa-v1-athenev2-reannotated-filtered dataset:shisa-ai/shisa-v2-roleplaying dataset:shisa-ai/translation-master-set base_model:unsloth/phi-4 base_model:finetune:unsloth/phi-4 license:mit autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: unsloth/phi-4
datasets:
- shisa-ai/shisa-v1-athenev2-reannotated-filtered
- shisa-ai/shisa-v2-roleplaying
- shisa-ai/translation-master-set
library_name: transformers
license: mit
tags:
- generated_from_trainer
model-index:
- name: outputs/ablation-73-rafathenev2.rp.tl.unphi42e6-shisa-v2-unphi-4-14b
  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. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.8.0.dev0`
```yaml
# 33 but w/ 5e-6 LR

base_model: unsloth/phi-4
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

# User Liger
plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

chat_template: tokenizer_default
datasets:
  - path: shisa-ai/shisa-v1-athenev2-reannotated-filtered
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
  - path: shisa-ai/shisa-v2-roleplaying
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      system:
        - system
      assistant:
        - gpt
        - model
        - assistant
      user:
        - human
        - user
    roles_to_train: ["assistant"]
  - path: shisa-ai/translation-master-set
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      system:
        - system
      assistant:
        - gpt
        - model
        - assistant
      user:
        - human
        - user
    roles_to_train: ["assistant"]
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/ablation-73-rafathenev2.rp.tl.unphi42e6-shisa-v2-unphi-4-14b

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

# marginal difference
neftune_noise_alpha: 5

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: ablation-73-rafathenev2.rp.tl.unphi42e6-shisa-v2-unphi-4-14b

gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 2e-6

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 0
save_total_limit: 1 # Only store a single checkpoint
debug:
deepspeed: zero3_bf16.json
weight_decay: 0.0001
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

```

</details><br>

# outputs/ablation-73-rafathenev2.rp.tl.unphi42e6-shisa-v2-unphi-4-14b

This model is a fine-tuned version of [unsloth/phi-4](https://huggingface.co/unsloth/phi-4) on the shisa-ai/shisa-v1-athenev2-reannotated-filtered, the shisa-ai/shisa-v2-roleplaying and the shisa-ai/translation-master-set datasets.
It achieves the following results on the evaluation set:
- Loss: 0.4463

## 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: 2e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 175
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6323        | 0.0017 | 1    | 0.6161          |
| 0.524         | 0.2506 | 147  | 0.5063          |
| 0.4596        | 0.5013 | 294  | 0.4791          |
| 0.4552        | 0.7519 | 441  | 0.4675          |
| 0.4389        | 1.0017 | 588  | 0.4603          |
| 0.4312        | 1.2523 | 735  | 0.4562          |
| 0.4404        | 1.5030 | 882  | 0.4527          |
| 0.4712        | 1.7536 | 1029 | 0.4498          |
| 0.4035        | 2.0034 | 1176 | 0.4477          |
| 0.4018        | 2.2540 | 1323 | 0.4480          |
| 0.3889        | 2.5047 | 1470 | 0.4469          |
| 0.4117        | 2.7553 | 1617 | 0.4463          |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
