Upload final fine-tuned Qwen3-0.6B model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,161 @@
+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen3-0.6B-Base
+tags:
+- generated_from_trainer
+datasets:
+- timarni/MNLP_M2_mcqa_dataset
+model-index:
+- name: outputs/base_test_set
+  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-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: timarni/MNLP_M2_mcqa_dataset
+    type: alpaca
+    split: train
+
+shuffle_merged_datasets: true
+
+val_set_size: 0.1
+output_dir: ./outputs/base_test_set
+dataset_prepared_path: last_run_prepared
+
+sequence_len: 4096 #2048
+sample_packing: true # was true -> need to check if it actually learns on the samples or not (better understand te hyperparam and event. install axolotl to debug)
+eval_sample_packing: false
+pad_to_sequence_len: true
+# train_on_inputs: true # NEW
+# group_by_length: false NEW?
+
+# To be sure that no LORA is done
+adapter: null
+lora: false
+merge_lora: false
+
+wandb_project: mnlp_project
+wandb_entity: tim-arni
+wandb_watch:
+wandb_name: base_test_set
+wandb_log_model:
+
+gradient_accumulation_steps: 16 # 2
+micro_batch_size: 2 # 1
+num_epochs: 25
+optimizer: adamw_torch
+lr_scheduler: cosine
+learning_rate: 0.00005 # 0.00005
+# cosine_min_lr_ratio: 0.1
+
+warmup_ratio: 0.05
+weight_decay: 0.01
+
+bf16: auto
+tf32: true
+
+gradient_checkpointing: offload
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+resume_from_checkpoint:
+logging_steps: 1
+gradient_clipping: 1.0 # or max_grad_norm?
+flash_attention: true
+
+evals_per_epoch: 4
+saves_per_epoch: 2
+save_total_limit: 25
+special_tokens:
+
+```
+
+</details><br>
+
+# outputs/base_test_set
+
+This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on the timarni/MNLP_M2_mcqa_dataset dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2652
+
+## 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: 2
+- eval_batch_size: 2
+- seed: 42
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 32
+- 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: 2
+- num_epochs: 25.0
+
+### Training results
+
+| Training Loss | Epoch   | Step | Validation Loss |
+|:-------------:|:-------:|:----:|:---------------:|
+| 0.4926        | 0.6957  | 1    | 0.6350          |
+| 0.4971        | 1.0     | 2    | 0.1976          |
+| 0.136         | 1.6957  | 3    | 0.1792          |
+| 0.112         | 2.0     | 4    | 0.2161          |
+| 0.1589        | 2.6957  | 5    | 0.1613          |
+| 0.1186        | 3.0     | 6    | 0.1703          |
+| 0.0949        | 3.6957  | 7    | 0.1849          |
+| 0.0879        | 4.0     | 8    | 0.1670          |
+| 0.0739        | 4.6957  | 9    | 0.1571          |
+| 0.0654        | 5.0     | 10   | 0.1650          |
+| 0.0565        | 5.6957  | 11   | 0.1853          |
+| 0.0501        | 6.0     | 12   | 0.2105          |
+| 0.0405        | 6.6957  | 13   | 0.2340          |
+| 0.0393        | 7.0     | 14   | 0.2389          |
+| 0.031         | 7.6957  | 15   | 0.2398          |
+| 0.0238        | 8.0     | 16   | 0.2427          |
+| 0.023         | 8.6957  | 17   | 0.2465          |
+| 0.0207        | 9.0     | 18   | 0.2538          |
+| 0.0182        | 9.6957  | 19   | 0.2618          |
+| 0.0217        | 10.0    | 20   | 0.2641          |
+| 0.0172        | 10.6957 | 21   | 0.2640          |
+| 0.0189        | 11.0    | 22   | 0.2685          |
+| 0.0167        | 11.6957 | 23   | 0.2686          |
+| 0.0184        | 12.0    | 24   | 0.2665          |
+| 0.0158        | 12.6957 | 25   | 0.2652          |
+
+
+### Framework versions
+
+- Transformers 4.51.3
+- Pytorch 2.5.1+cu121
+- Datasets 3.5.1
+- Tokenizers 0.21.1

added_tokens.json

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+}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.51.3"
+}

model.safetensors

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+size 1192135096

special_tokens_map.json

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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
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+  }
+}

tokenizer.json

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+size 11422654

tokenizer_config.json

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+}