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neginashz
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star-sft-intellect-instruct-2

Text Generation
Transformers
PyTorch
Safetensors
llama
axolotl
Generated from Trainer
conversational
text-generation-inference
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star-sft-intellect-instruct-2 / README.md
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metadata
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: []

Built with Axolotl

See axolotl config

axolotl version: 0.6.0

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

star-sft-intellect-instruct-2

This model is a fine-tuned version of 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