qwen3_s1k / README.md
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Upload final fine-tuned Qwen3-0.6B model
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---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
datasets:
- timarni/s1k_r1_clean
model-index:
- name: outputs/qwen3_s1k
results: []
---
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should probably proofread and complete it, then remove this comment. -->
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<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
base_model: Qwen/Qwen3-0.6B-Base
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
plugins:
- axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false
chat_template: qwen3
datasets:
- path: /mloscratch/users/arni/Workspace/mnlp_sft/datasets/s1k.json
type: chat_template
split: train
field_messages: conversations
# message_property_mappings:
# role: from
# content: value
output_dir: ./outputs/qwen3_s1k
dataset_prepared_path: last_run_prepared
# To be sure that no LORA is done
adapter: null
lora: false
merge_lora: false
sequence_len: 4096 #2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_watch:
wandb_name: qwen3_s1k
wandb_log_model:
gradient_accumulation_steps: 2 # 16 following https://unsloth.ai/blog/qwen3
micro_batch_size: 1 # 2
num_epochs: 5
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00005 # 0.0002
cosine_min_lr_ratio: 0.1
bf16: auto
tf32: true
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
gradient_clipping: 1.0
flash_attention: true
warmup_ratio: 0.03
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 1e-4
special_tokens:
```
</details><br>
# outputs/qwen3_s1k
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on the /mloscratch/users/arni/Workspace/mnlp_sft/datasets/s1k.json 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- 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: 5.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1