DReP SFT Qwen3 8B
This model is a fine-tuned version of Qwen3-8B on the drepahalfsft dataset.
Modellquelle
- Art
- Text model
- Dateigröße
- Noch nicht aufgezeichnet
- Basismodell
- Qwen/Qwen3-8B
- Version
- vRevision a9d67df
- Aktualisiert
- 3. Okt. 2026
- Downloads
- 11
- Aufgaben
- Textgenerierung
- Quelle
- Hugging Face
Quellenbeschreibung
drep_a_half_sft
This model is a fine-tuned version of Qwen3-8B on the drep_a_half_sft dataset.
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: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.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_ratio: 0.1
- num_epochs: 4
Training results
Framework versions
- Transformers 4.57.1
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.22.2
Quellen
1 Quelle
Quellen
1 QuelleVerifiziert 3. Okt.
Quellenauszüge
3 Auszüge
Quellenauszüge
3 Auszüge--- library_name: transformers license: other base_model: Qwen/Qwen3-8B tags: - llama-factory - full - generated_from_trainer model-index: - name: drep_a_half_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. --> # drep_a_half_sft This model is a fine-tuned version of [Qwen3-8B](Qwen3-8B) on the drep_a_half_sft dataset. ## 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: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Use OptimizerNames.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_ratio: 0.1 - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.57.1 - Pytorch 2.6.0+cu124 - Datasets 4.0.0 - Tokenizers 0.22.2
Source context: 11 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo hahayhe/DReP-SFT-Qwen3-8B
Source context: 262 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo hahayhe/DReP-SFT-Qwen3-8B
