README.md

---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-generation
library_name: transformers
tags:
- moe
- qwen3
- code
- math
- reasoning
- medical
- instruction
- if
datasets:
- nvidia/OpenCodeReasoning
- unsloth/OpenMathReasoning-mini
- patrickfleith/instruction-freak-reasoning
- FreedomIntelligence/medical-o1-reasoning-SFT
- Malikeh1375/medical-question-answering-datasets
- Myashka/SO-Python_QA-filtered-2023-no_code-tanh_score
- ArdentTJ/t1_daily_conversations
---

![The Imitation Game](qwen3-moe.jpg)

"*We are all experts at something, but we’re all also beginners at something else.*"

— *The Imitation Game (2014)*

# Arcana Qwen3 2.4B A0.6B

This is a MoE (Mixture of Experts) Qwen3 model which has total number of 2.4B parameters and 0.6B for each of 4 experts. All the expert models can be seen below.
This model aims to provide more accurate results with more efficiency and less memory usage!

## Expert Models:

### Key Training Parameters (SFTConfig)

* `per_device_train_batch_size = 2`
* `gradient_accumulation_steps = 4`
* `warmup_steps = 5`
* `num_train_epochs = 1`
* `learning_rate = 2e-5`
* `optim = "adamw_8bit"`
* `weight_decay = 0.01`
* `seed = 3407`
  
### Coding:
[suayptalha/Qwen3-0.6B-Code-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Code-Expert)

This model was fully fine-tuned with BF16 on first 20k rows of `nvidia/OpenCodeReasoning` dataset for 1 epoch.

### Math:
[suayptalha/Qwen3-0.6B-Math-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Math-Expert)

This model was fully fine-tuned with BF16 on entire `unsloth/OpenMathReasoning-mini` dataset for 1 epoch.

### Medical:
[suayptalha/Qwen3-0.6B-Medical-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Medical-Expert)

This model was fully fine-tuned with BF16 on first 20k rows of `FreedomIntelligence/medical-o1-reasoning-SFT` dataset for 1 epoch.

### Instruction Following:
[Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)

`Qwen/Qwen3-0.6B` model was directly used for this expert, no fine-tune was applied.

## Router Model:
The router model can be found [here](https://huggingface.co/suayptalha/MoE-Router-v2) which was trained version of `distilbert/distilbert-base-uncased` on 7 different datasets.

## Usage:
```py
import torch
from huggingface_hub import snapshot_download
from transformers import AutoModelForCausalLM, AutoTokenizer

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

local_dir = snapshot_download(
    repo_id="suayptalha/Qwen3-2.4B-A0.6B",
)

model = AutoModelForCausalLM.from_pretrained(
    local_dir,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
    local_dir,
)

model.to(device)
model.eval()

prompt = "I have pain in my chest, what should I do?"
messages = [{"role": "user", "content": prompt}]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

with torch.no_grad():
    output_ids = model.generate(
        text=prompt,
        max_new_tokens=1024,
        temperature=0.6,
        top_p=0.95,
    )
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(output_text)
```

## License:

This project is licensed under the Apache License 2.0. See the [LICENSE](./LICENSE) file for details.

## Support:

<a href="https://www.buymeacoffee.com/suayptalha" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>