Qwen3-0.6B-MNLP_M2_mcqa_model

This model is a fine-tuned version of unsloth/Qwen3-0.6B-Base on an unknown dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

Training was done on the training splits of

• MEDMCQA
• MMLU
• Sciq
• Ai2 Arc
• Math_qa
• ScienceQa
• Openbookqa

Training procedure

The procedure for training was to only leave the question that have only 4 choices to chose from, and from there we do the training by only grabbing the last logit form doing a feedforward on the whole prompt (question with choices) and we do cross entropy loss on this last logit with the 4 options to choose 4 from (so we don't do cross entyropy on the whole vocabulary we only do it on the tokens of the letters of the 4 options (A, B, C and D))

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• gradient_accumulation_steps: 32
• total_train_batch_size: 64
• optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.04
• num_epochs: 2

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:

First evaluation: The tests where done with this prompt (type 5):

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]

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	66.28%	64.92%
ARC Easy	84.22%	81.33%
GPQA	38.84%	36.61%
Math QA	25.03%	24.67%
MCQA Evals	43.51%	40.91%
MMLU	52.17%	52.17%
MMLU Pro	16.45%	15.04%
MuSR	53.17%	52.25%
NLP4Education	44.45%	42.65%
Overall	47.12%	45.62%

Second evaluation: (type 0)

The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.

---
*[Insert Question Here]*
---
*[Insert Choices Here, e.g.:*
*A. Option 1*
*B. Option 2*
*C. Option 3*
*D. Option 4]*
---
Answer:

And the teseting was done on [Letter]. [Text answer]

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	69.95%	65.33%
ARC Easy	84.45%	78.51%
GPQA	31.92%	28.57%
Math QA	27.02%	26.88%
MCQA Evals	43.90%	35.32%
MMLU	52.17%	52.17%
MMLU Pro	15.04%	13.27%
MuSR	53.17%	52.25%
NLP4Education	49.14%	42.85%
Overall	47.42%	43.91%

Third evaluation: (type 2)

This is part of an assessment on graduate-level science, technology, engineering, and mathematics (STEM) concepts. Each question is multiple-choice and requires a single correct answer.

---
*[Insert Question Here]*
---
*[Insert Choices Here, e.g.:*
*A. Option 1*
*B. Option 2*
*C. Option 3*
*D. Option 4]*
---
For grading purposes, respond with: [LETTER]. [VERBATIM TEXT]
Example: D. Planck constant
Your Response:

And the teseting was done on [Letter]. [Text answer]

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	55.34%	55.34%
ARC Easy	74.00%	74.00%
GPQA	29.69%	29.69%
Math QA	22.35%	22.35%
MCQA Evals	37.92%	37.92%
MMLU	52.14%	52.14%
MMLU Pro	12.98%	12.98%
MuSR	53.04%	53.04%
NLP4Education	36.36%	36.36%
Overall	41.53%	41.53%

First evaluation: (type 0)

The following are multiple choice questions (with answers) about knowledge and skills in advanced master-level STEM courses.

---
*[Insert Question Here]*
---
*[Insert Choices Here, e.g.:*
*A. Option 1*
*B. Option 2*
*C. Option 3*
*D. Option 4]*
---
Answer:

And the teseting was done on [Letter]

Benchmark	Accuracy (Acc)	Normalized Accuracy (Acc Norm)
ARC Challenge	70.63%	70.63%
ARC Easy	85.13%	85.13%
GPQA	25.45%	25.45%
Math QA	27.35%	27.35%
MCQA Evals	45.97%	45.97%
MMLU	52.14%	52.14%
MMLU Pro	14.97%	14.97%
MuSR	53.04%	53.04%
NLP4Education	50.86%	50.86%
Overall	47.28%	47.28%

Framework versions

• Transformers 4.51.3
• Pytorch 2.5.1+cu121
• Datasets 3.6.0
• Tokenizers 0.21.0