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Model: andresnowak/Qwen3-0.6B-instruction-finetuned Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/Qwen3-0.6B-Base
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library_name: transformers
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model_name: Qwen3-0.6B-instruction-finetuned
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tags:
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- generated_from_trainer
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- unsloth
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- trl
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- sft
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licence: license
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datasets:
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- andresnowak/Instruction-finetuning-mixture-mnlp
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language:
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- en
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---
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# Model Card for Qwen3-0.6B-instruction-finetuned
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This model is a fine-tuned version of [unsloth/Qwen3-0.6B-Base](https://huggingface.co/unsloth/Qwen3-0.6B-Base).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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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?"
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generator = pipeline("text-generation", model="andresnowak/Qwen3-0.6B-instruction-finetuned", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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This model was done using Language modelling (loss done on prompt and completion) Supervised instruction finetuning and this model was also trained by applying some ranom templates
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as to be able to have more robustness as how questions will be asked apart from the dataest already bein high quality and having a lot of this examples, this was done as we weren't
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allowed to use chat templates for the evaluation.
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But this model probably had two problems during training, one being that we didn't filter the dataset to just have examples that combined (prompt and completion) have a size of 2048 (the max size we are using) and instead
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doing a truncation. Also this model uses left side padding in the tokenizer as flash-attention 2 needs this
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```yaml
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environment:
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seed: 42
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use_template: True
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model:
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name: Qwen/Qwen3-0.6B-Base
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hub_model_id: andresnowak/Qwen3-0.6B-instruction-finetuned
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dataset:
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: codeAlpaca
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size: 0.3
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: noRobots
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size: 0.8
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: openMathGsm8k
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size: 0.3
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: codeV2
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size: 0.3
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: flanV2
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size: 0.8
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: ifData
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size: 0.8
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: mathAlgebra
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size: 0.3
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: mathGrade
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size: 0.3
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: oasst1
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size: 0.6
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: sciriff
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size: 0.8
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: tableGpt
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size: 0.3
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: tirMath
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size: 0.4
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: wildChat
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size: 0.7
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- name: andresnowak/Instruction-finetuning-mixture-mnlp
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config: mathV5
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size: 0.2
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dataset_evaluation:
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- name: cais/mmlu
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config: validation
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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"]
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training:
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learning_rate: 1e-5
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per_device_train_batch_size: 16
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per_device_eval_batch_size: 16
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gradient_accumulation_steps: 8
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num_train_epochs: 2
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weight_decay: 0.00
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warmup_ratio: 0.03
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max_grad_norm: 0.5
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lr_scheduler: "linear"
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```
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This model was trained with SFT.
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## Evaluation results
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The performance is as follows:
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| Benchmark | Accuracy (Acc) | Normalized Accuracy (Acc Norm) |
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| :----------------- | :------------- | :----------------------------- |
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| ARC Challenge | 46.0% | 45.3% |
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| ARC Easy | 59.3% | 54.2% |
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| GPQA | 29.9% | 27.0% |
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| Math QA | 24.0% | 24.8% |
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| MCQA Evals | 37.9% | 34.9% |
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| MMLU | 47.2% | 47.2% |
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| MMLU Pro | 13.2% | 12.0% |
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| MuSR | 43.5% | 42.1% |
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| NLP4Education | 38.8% | 36.5% |
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| **Overall** | **37.8%** | **36.0%** |
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The tests where done with this prompt (And only MusR used a different one where you add the Question: and Narrative: )
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```
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This question assesses challenging STEM problems as found on graduate standardized tests. Carefully evaluate the options and select the correct answer.
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---
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[Insert Question Here]
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---
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[Insert Choices Here, e.g.:
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A. Option 1
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B. Option 2
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C. Option 3
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D. Option 4]
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---
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Your response should include the letter and the exact text of the correct choice.
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Example: B. Entropy increases.
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Answer:
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```
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And the teseting was done on ``` [Letter]. [Text answer]```
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### Framework versions
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- TRL: 0.15.2
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- Transformers: 4.51.3
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- Pytorch: 2.5.1+cu121
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- Datasets: 3.6.0
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- Tokenizers: 0.21.0
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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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},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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