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Qwen1.5-DeepSeek-Merge

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----
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+license: mit
+language:
+- en
+base_model:
+- Qwen/Qwen1.5-1.8B
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
 library_name: transformers
+tags:
+- mergekit
+- merged-model
+- qwen
+- deepseek
+- language-model
 ---
+# 🤖 Qwen1.5-DeepSeek-Merge: Uniting Precision & Efficiency
+## 📌 Overview
+**Qwen1.5-DeepSeek-Merge** is an **experimental hybrid model** merging the capabilities of **Qwen/Qwen1.5-1.8B** and **DeepSeek-R1-Distill-Qwen-1.5B** using the **Linear Merge** method via **MergeKit**. This fusion aims to capture the strengths of both models—balancing linguistic nuance with distilled performance.
+🔗 **Created by**: [Matteo Khan]
+🎓 **Affiliation**: Apprentice at TW3 Partners (Generative AI Research)
+📍 **License**: MIT
+🔗 [Connect on LinkedIn](https://www.linkedin.com/in/matteo-khan-a10309263/)
+🔗 [Model on Hugging Face](https://huggingface.co/MatteoKhan/Qwen1.5-DeepSeek-Merge)
+## 🧠 Model Details
+- **Model Type**: Merged Language Model
+- **Parent Models**:
+  - [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B)
+  - [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
+- **Merge Method**: Linear Merge (via MergeKit)
+- **Precision**: bfloat16
+## 🎯 Intended Use
+This model is primarily intended for **research and experimentation** in language model merging. Potential applications include:
+- ✅ General Text Generation
+- ✅ Dialogue Systems
+- ✅ Prompt Engineering Research
+- ✅ Evaluation of Merging Strategies
+## ⚠️ Limitations & Considerations
+While this model may exhibit improved behavior in some cases, it also inherits limitations from its parent models:
+- ❌ May generate hallucinated or unverified information
+- ⚠️ Susceptible to biases or offensive outputs
+- 🔀 Merge effects may introduce unexpected behaviors
+- 📉 Task-specific performance not guaranteed
+## 🔬 Merging Process & Configuration
+This model is a **merge**, not a newly fine-tuned one. Below is the exact configuration used:
+```python
+hf_repo_name = "MatteoKhan/Qwen1.5-DeepSeek-Merge"
+config = {
+    "merge_method": "linear",
+    "dtype": torch.bfloat16,
+    "models": [
+        {
+            "model": "Qwen/Qwen1.5-1.8B",
+            "parameters": {
+                "weight": 0.5
+            }
+        },
+        {
+            "model": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+            "parameters": {
+                "weight": 0.5
+            }
+        }
+    ],
+    "parameters": {
+        "normalize": True
+    },
+    "layers": [
+        {"pattern": "model."}
+    ]
+}
+📊 No formal benchmark yet—community testing is welcome!
+🌱 Environmental Impact
+By merging pre-trained models instead of training from scratch, this approach saves substantial compute and reduces carbon emissions.
+🚀 How to Use
+python
+Copier
+Modifier
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "MatteoKhan/Qwen1.5-DeepSeek-Merge"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto")
+prompt = "What are the implications of quantum computing on AI?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+📬 Questions or feedback? Contact via Hugging Face or LinkedIn