---
library_name: transformers
license: apache-2.0
base_model: PrimeIntellect/INTELLECT-1-Instruct
tags:
- axolotl
- generated_from_trainer
datasets:
- neginashz/rationale-llama-chat-dataset
model-index:
- name: star-sft-intellect-instruct-2
  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: `0.6.0`
```yaml
base_model: PrimeIntellect/INTELLECT-1-Instruct
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

gpu_memory_limit: 

load_in_8bit: 
load_in_4bit:
strict: false

chat_template: llama3
datasets:
  - path: neginashz/rationale-llama-chat-dataset
    type: chat_template
    field_messages: messages
    message_field_role: role
    message_field_content: content

    
dataset_prepared_path:
val_set_size: 0.1
output_dir: ./star-sft-intellect-2




sequence_len: 8192
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true


wandb_project: star-sft-intellect-instruct-2
wandb_entity: 
wandb_watch:
wandb_name: 
wandb_log_model: 

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
  
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps:
eval_steps: 
save_steps:

evals_per_epoch: 16
saves_per_epoch: 1

debug:
deepspeed: deepspeed_configs/zero2.json
weight_decay:
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>
  pad_token: <|finetune_right_pad_id|>

hub_model_id: neginashz/star-sft-intellect-instruct-2
hub_strategy: 
early_stopping_patience:

resume_from_checkpoint:
auto_resume_from_checkpoints: true



```

</details><br>

# star-sft-intellect-instruct-2

This model is a fine-tuned version of [PrimeIntellect/INTELLECT-1-Instruct](https://huggingface.co/PrimeIntellect/INTELLECT-1-Instruct) on the neginashz/rationale-llama-chat-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3719

## 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-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Use adamw_torch 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: 6
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5033        | 0.0686 | 7    | 0.4057          |
| 0.4303        | 0.1373 | 14   | 0.3986          |
| 0.4496        | 0.2059 | 21   | 0.3977          |
| 0.4223        | 0.2745 | 28   | 0.3973          |
| 0.4083        | 0.3431 | 35   | 0.3940          |
| 0.4191        | 0.4118 | 42   | 0.3893          |
| 0.412         | 0.4804 | 49   | 0.3859          |
| 0.3912        | 0.5490 | 56   | 0.3812          |
| 0.3995        | 0.6176 | 63   | 0.3749          |
| 0.4236        | 0.6863 | 70   | 0.3703          |
| 0.3833        | 0.7549 | 77   | 0.3663          |
| 0.3605        | 0.8235 | 84   | 0.3614          |
| 0.3952        | 0.8922 | 91   | 0.3576          |
| 0.3744        | 0.9608 | 98   | 0.3540          |
| 0.199         | 1.0196 | 105  | 0.3536          |
| 0.1762        | 1.0882 | 112  | 0.4128          |
| 0.1704        | 1.1569 | 119  | 0.3808          |
| 0.1603        | 1.2255 | 126  | 0.3781          |
| 0.1727        | 1.2941 | 133  | 0.3874          |
| 0.1624        | 1.3627 | 140  | 0.3841          |
| 0.1546        | 1.4314 | 147  | 0.3793          |
| 0.1602        | 1.5    | 154  | 0.3776          |
| 0.1501        | 1.5686 | 161  | 0.3745          |
| 0.146         | 1.6373 | 168  | 0.3734          |
| 0.1512        | 1.7059 | 175  | 0.3733          |
| 0.146         | 1.7745 | 182  | 0.3725          |
| 0.1479        | 1.8431 | 189  | 0.3721          |
| 0.1395        | 1.9118 | 196  | 0.3720          |
| 0.1472        | 1.9804 | 203  | 0.3719          |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0