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Model: andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: unsloth/Qwen3-0.6B-Base
+library_name: transformers
+model_name: Qwen3-0.6B-MNLP_mcqa_model_text
+tags:
+- generated_from_trainer
+- unsloth
+- trl
+- sft
+licence: license
+datasets:
+- andresnowak/MNLP_MCQA_dataset
+---
+
+# Model Card for Qwen3-0.6B-MNLP_mcqa_model_text
+
+This model is a fine-tuned version of [unsloth/Qwen3-0.6B-Base](https://huggingface.co/unsloth/Qwen3-0.6B-Base).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+This model was trained with SFT by doing finetuning as a Seq2Seq MCQA method (so doing question\n Letter. Answer) starting from the Qwen3-0.6B-base model. And 
+it was trained doing Lanugage modelling (Loss on whole prompt and completion)
+
+```yaml
+environment:
+  seed: 42
+
+model:
+  name: Qwen/Qwen3-0.6B-Base
+  hub_model_id: andresnowak/Qwen3-0.6B-MNLP_mcqa_model_text
+
+dataset_train:
+  - name: andresnowak/MNLP_MCQA_dataset
+    config: train
+    subset_name: math_qa
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: ScienceQA
+    config: train
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: mmlu-auxiliary-train-auto-labelled
+    config: train
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: ai2_arc_challenge
+    config: train
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: ai2_arc_easy
+    config: train
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: medmcqa
+    config: train
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: openbookqa
+    config: train
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: sciq
+    config: train
+
+dataset_validation:
+  - name: andresnowak/MNLP_MCQA_dataset
+    config: validation
+    subset_name: math_qa
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: ScienceQA
+    config: validation
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: mmlu
+    config: validation
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: ai2_arc_challenge
+    config: validation
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: ai2_arc_easy
+    config: validation
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: medmcqa
+    config: validation
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: openbookqa
+    config: validation
+  - name: andresnowak/MNLP_MCQA_dataset
+    subset_name: sciq
+    config: validation
+
+dataset_mmlu:
+  - name: cais/mmlu
+    config: validation
+    subjects: ["abstract_algebra", "anatomy", "astronomy", "college_biology", "college_chemistry", "college_computer_science", "college_mathematics", "college_physics", "computer_security", "conceptual_physics", "electrical_engineering", "elementary_mathematics", "high_school_biology",  "high_school_chemistry", "high_school_computer_science", "high_school_mathematics", "high_school_physics", "high_school_statistics", "machine_learning"]
+
+
+training:
+  learning_rate: 1e-5
+  per_device_train_batch_size: 2
+  per_device_eval_batch_size: 2
+  gradient_accumulation_steps: 32
+  num_train_epochs: 2
+  weight_decay: 0.00
+  warmup_ratio: 0.1
+  max_grad_norm: 0.5
+  linear_layers_max_grad_norm: 1.0
+```
+
+### Framework versions
+
+- TRL: 0.15.2
+- Transformers: 4.51.3
+- Pytorch: 2.5.1+cu121
+- Datasets: 3.6.0
+- Tokenizers: 0.21.0
+
+## Evaluation Results
+
+The model was evaluated on a suite of Multiple Choice Question Answering (MCQA) benchmarks (on its validation and test sets repsectively for each one), 
+and NLP4education is only the approximated 1000 question and answers given to use.
+
+**Important Note on MCQA Evals Benchmark:**
+
+The performance on these benchmarks is as follows:
+
+| Benchmark          | Accuracy (Acc) | Normalized Accuracy (Acc Norm) |
+| :----------------- | :------------- | :----------------------------- |
+| ARC Challenge      | 60.6%          | 60.5%                          |
+| ARC Easy           | 78.6%          | 76.8%                          |
+| GPQA               | 30.1%          | 29.0%                          |
+| Math QA            | 29.3%          | 28.5%                          |
+| MCQA Evals         | 41.9%          | 38.7%                          |
+| MMLU               | 48.6%          | 48.6%                          |
+| MMLU Pro           | 14.5%          | 13.6%                          |
+| MuSR               | 47.6%          | 47.6%                          |
+| NLP4Education      | 43.7%          | 41.0%                          |
+| **Overall**        | **43.9%**      | **42.7%**                      |
+
+The tests where done with this prompt:
+```
+This question assesses challenging STEM problems as found on graduate standardized tests. Carefully evaluate the options and select the correct answer.
+
+---
+[Insert Question Here]
+---
+[Insert Choices Here, e.g.:
+A. Option 1
+B. Option 2
+C. Option 3
+D. Option 4]
+---
+
+Your response should include the letter and the exact text of the correct choice.
+Example: B. Entropy increases.
+Answer:
+```
+
+And the teseting was done on ``` [Letter]. [Text answer]```
+
+## Citations
+
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

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