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Rexhaif
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Qwen3-0.6B-MTEval-SFT

Text Generation
Transformers
Safetensors
qwen3
axolotl
Generated from Trainer
conversational
text-generation-inference
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Qwen3-0.6B-MTEval-SFT
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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B
tags:
- axolotl
- generated_from_trainer
datasets:
- Rexhaif/wmt23-pairs-sft
model-index:
- name: Qwen3-0.6B-MTEval-SFT
  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: `0.9.2`
```yaml
base_model: Qwen/Qwen3-0.6B
# Automatically upload checkpoint and final model to HF
hub_model_id: Rexhaif/Qwen3-0.6B-MTEval-SFT
hub_private_repo: false

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: tokenizer_default
datasets:
  - path: Rexhaif/wmt23-pairs-sft
    split: "train"
    type: chat_template
    field_messages: messages
    roles_to_train: ["assistant"]

shuffle_merged_datasets: true

skip_prepare_dataset: false
dataset_prepared_path: ./data/wmt23-pairs-sft
output_dir: /hnvme/workspace/v106be28-outputs/sft-0.6b

dataloader_prefetch_factor: 32
dataloader_num_workers: 2
dataloader_pin_memory: true

gc_steps: 1

sequence_len: 512
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: false

wandb_project: llm-reasoning-mt-eval
wandb_entity:
wandb_name: qw3-0.6b-sft

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
gradient_accumulation_steps: 1
micro_batch_size: 64  # should match num_generations / num_gpus

optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 5.0e-5
cosine_min_lr_ratio: 1.0e-7
max_grad_norm: 1.0
weight_decay: 0.1

bf16: true
tf32: true

flash_attention: true
flash_attn_fuse_qkv: true
flash_attn_fuse_mlp: true
auto_resume_from_checkpoints: true

n_epochs: 3
logging_steps: 10
warmup_ratio: 0.1
evals_per_epoch: 10
saves_per_epoch: 10
save_total_limit: 1
#max_steps: 5000
seed: 42
val_set_size: 0.01

gradient_checkpointing: false
gradient_checkpointing_kwargs:
  use_reentrant: false

```

</details><br>

# Qwen3-0.6B-MTEval-SFT

This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) on the Rexhaif/wmt23-pairs-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0486

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 101
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0010 | 1    | 7.5881          |
| 0.2427        | 0.1003 | 102  | 0.2504          |
| 0.2062        | 0.2006 | 204  | 0.1936          |
| 0.1631        | 0.3009 | 306  | 0.1606          |
| 0.1315        | 0.4012 | 408  | 0.1243          |
| 0.0999        | 0.5015 | 510  | 0.1098          |
| 0.0871        | 0.6018 | 612  | 0.0871          |
| 0.0611        | 0.7021 | 714  | 0.0702          |
| 0.0586        | 0.8024 | 816  | 0.0564          |
| 0.0478        | 0.9027 | 918  | 0.0486          |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1