Qwen3-4B-Thinking-2507-20250813-033307-1/README.md

---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- merge
- model-merging
- mergekit
- lazymergekit
- qwen3
- 4b
- text-generation
- causal-lm
datasets:
- Idavidrein/gpqa
metrics:
- accuracy
base_model:
- Qwen/Qwen3-4B-Thinking-2507
- Qwen/Qwen3-4B-Thinking-2507-FP8
- unsloth/Qwen3-4B-Thinking-2507
- ertghiu256/Qwen3-4b-tcomanr-merge-v2
- huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
- janhq/Jan-v1-4B
- BRlkl/BingoGuard-qwen3-4B-pt-grpo
- fireworks1231/agentic-4b-2607-sft
- sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning
- ReallyFloppyPenguin/Mastermind-2x4b-thinking
base_model_relation: merge
model-index:
- name: qwen3-4b-merged---configuration-1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      type: cais/mmlu
      name: MMLU (Massive Multitask Language Understanding)
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: accuracy
      value: 68.37
      name: MMLU (5-shot)
      verified: false
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      type: Idavidrein/gpqa
      name: GPQA (Graduate-level Physics Q&A)
      config: gpqa_diamond
      split: test
      args:
        num_few_shot: 0
    metrics:
    - type: accuracy
      value: 43.43
      name: GPQA Diamond (0-shot)
      verified: false
---

# Qwen3-4B Merged - Configuration 1

This is a Qwen3-4B based model created through layer-wise merging of multiple fine-tuned variants to optimize performance on GPQA Diamond.

## Performance Metrics

| Benchmark | Score | Description |
|-----------|-------|-------------|
| **MMLU (5-shot)** | 0.6837 (68.37%) | Massive Multitask Language Understanding |
| **GPQA Diamond (0-shot)** | 0.4343 (43.43%) | Graduate-level Physics Q&A |

### Benchmark Details

- **MMLU**: Evaluated on the test set with 5-shot prompting across 57 subjects
- **GPQA**: Evaluated on the diamond subset with 0-shot prompting on graduate-level physics questions

## Performance Visualizations

### GPQA Diamond Performance Comparison
![GPQA Performance](./gpqa_performance.png)

### MMLU and GPQA Diamond Combined Performance
![MMLU and GPQA Performance](./mmlu_gpqa_performance.png)

## Model Information

- **Run ID**: 20250813_033307
- **Optimization Task**: GPQA (Graduate-level Physics Q&A)
- **Number of Layers**: 36
- **Base Architecture**: Qwen3-4B

## Source Models

The following models were used in the layer-wise merge:

- Qwen/Qwen3-4B-Thinking-2507
- Qwen/Qwen3-4B-Thinking-2507-FP8
- unsloth/Qwen3-4B-Thinking-2507
- ertghiu256/Qwen3-4b-tcomanr-merge-v2
- huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated
- janhq/Jan-v1-4B
- BRlkl/BingoGuard-qwen3-4B-pt-grpo
- fireworks1231/agentic-4b-2607-sft
- sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning
- ReallyFloppyPenguin/Mastermind-2x4b-thinking

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    "EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1",
    torch_dtype=torch.float16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1")

# Example: MMLU-style question
prompt = '''Question: The study of the distribution and determinants of health and disease in populations is:
A) Epidemiology
B) Ecology  
C) Etiology
D) Endocrinology
Answer:'''

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_length=150,
    temperature=0.7,
    do_sample=True
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Inference with vLLM

```python
from vllm import LLM, SamplingParams

llm = LLM(model="EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1")
sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=256)

prompts = ["Question: Explain quantum entanglement in simple terms."]
outputs = llm.generate(prompts, sampling_params)
```

## Technical Details

This model uses a layer-wise merging approach where each transformer layer is selected from different source models based on optimization criteria. This technique allows combining strengths from multiple fine-tuned models.

### Merging Process

1. **Layer Selection**: Each layer (0-35 for this architecture) is independently selected from one of the source models
2. **Non-layer Weights**: Embeddings and final layers are taken from the base model
3. **Optimization**: The configuration was found through systematic optimization on the target benchmark

## Limitations

- This is an experimental merge and performance may vary on tasks outside the optimization targets
- The model inherits limitations from its source models
- Performance on general tasks may differ from benchmark scores

## Citation

If you use this model, please cite the original source models and egan.ai

## Note

This model is provided for research purposes. Always validate performance on your specific use case before deployment.