---
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-3
  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-3




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


wandb_project: star-sft-intellect-instruct-3
wandb_entity: 
wandb_watch:
wandb_name: 
wandb_log_model: 

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
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:

hub_model_id: neginashz/star-sft-intellect-instruct-3
hub_strategy: 
early_stopping_patience:

resume_from_checkpoint:
auto_resume_from_checkpoints: true



```

</details><br>

# star-sft-intellect-instruct-3

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: 0.3380

## 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: 3
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5519        | 0.0686 | 7    | 0.4405          |
| 0.4453        | 0.1373 | 14   | 0.4080          |
| 0.4511        | 0.2059 | 21   | 0.4004          |
| 0.4243        | 0.2745 | 28   | 0.3979          |
| 0.405         | 0.3431 | 35   | 0.3893          |
| 0.4134        | 0.4118 | 42   | 0.3832          |
| 0.4028        | 0.4804 | 49   | 0.3753          |
| 0.3801        | 0.5490 | 56   | 0.3682          |
| 0.3878        | 0.6176 | 63   | 0.3593          |
| 0.4085        | 0.6863 | 70   | 0.3523          |
| 0.3649        | 0.7549 | 77   | 0.3460          |
| 0.3378        | 0.8235 | 84   | 0.3416          |
| 0.377         | 0.8922 | 91   | 0.3390          |
| 0.3542        | 0.9608 | 98   | 0.3380          |


### Framework versions

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