FINGU-AI
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QwQ-Buddy-32B-Alpha

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 library_name: transformers
+license: mit
 ---
+# QwQ-Buddy-32B-Alpha
+## Model Summary
+QwQ-Buddy-32B-Alpha is a **merged 32B model** created by fusing two high-performing models:
+- **huihui-ai/QwQ-32B-Coder-Fusion-9010** (strong in coding and logical reasoning)
+- **OpenBuddy/openbuddy-qwq-32b-v24.2-200k** (strong in general knowledge and reasoning)
+The merge was performed using **Spherical Linear Interpolation (SLERP)** to ensure a smooth and balanced integration of capabilities from both source models. The result is a **powerful and versatile 32B model** that excels in both **coding and reasoning tasks**, making it one of the top candidates for leaderboard evaluations.
 ## Model Details
+- **Model Type:** Merged LLM (Qwen-2.5 32B architecture-based)
+- **Precision:** `bfloat16`
+- **Merge Method:** SLERP (Spherical Linear Interpolation)
+- **Weight Type:** **Original** (fully merged model, NOT delta-based)
+- **Context Length:** 200K tokens (inherits capabilities from OpenBuddy-QwQ)
+- **Training Base Models:**
+  - `huihui-ai/QwQ-32B-Coder-Fusion-9010`
+  - `OpenBuddy/openbuddy-qwq-32b-v24.2-200k`
+- **Merged Layers:**
+  - `0-32` equally distributed from both models
+  - `24-64` optimized for knowledge reasoning and logical computations
+## Performance Improvements
+✅ **Stronger coding capabilities** (inherits high performance from QwQ-32B-Coder-Fusion-9010)
+✅ **Enhanced general knowledge & reasoning** (boosted by OpenBuddy-QwQ)
+✅ **Balanced self-attention and MLP layers** for smoother response generation
+✅ **Higher robustness in multilingual support** (OpenBuddy-QwQ contributions)
+✅ **Fine-tuned SLERP weighting for best accuracy in benchmarks**
+## Expected Leaderboard Performance
+Based on internal testing and model comparisons, **QwQ-Buddy-32B-Alpha** is expected to achieve **top 20 rankings** in:
+- **HumanEval** (coding tasks)
+- **MMLU** (multi-task language understanding)
+- **HellaSwag** (commonsense reasoning)
+- **BBH (Big Bench Hard)** (complex problem-solving)
+## Limitations & Considerations
+- 🚧 **Not fine-tuned post-merge** (raw merge evaluation may have slight instabilities)
+- 🚧 **No explicit safety alignment applied** (inherits behavior from base models)
+- 🚧 **Performance on unseen edge cases requires additional evaluation**
+## How to Use
+To load the model for inference:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "FINGU-AI/QwQ-Buddy-32B-Alpha"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="bfloat16")
+inputs = tokenizer("Write a Python function to compute Fibonacci numbers:", return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0]))
+```
+## Acknowledgments
+This model was built using:
+- **MergeKit** for SLERP-based weight interpolation
+- **Hugging Face Transformers** for model loading and testing
+- **Leaderboard Evaluation Benchmarks** for performance comparisons
+## Contact & Feedback
+For any inquiries, issues, or feedback regarding **QwQ-Buddy-32B-Alpha**, please reach out via GitHub or Hugging Face discussions.