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Model: AI-ModelScope/Qwen2.5-14B-DeepSeek-R1-1M
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
+- Qwen/Qwen2.5-14B-Instruct-1M
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Qwen2.5-14B-DeepSeek-R1-1M
+
+A merged model combines the reasoning model's strengths (Qwen2.5-14B-DeepSeek-R1) and the long-context model capabilities (Qwen2.5-14B-Instruct-1M) for versatile performance.
+
+# Merge config
+
+```yaml
+models:
+  - model: "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
+    parameters:
+      weight: 1
+      density: 1
+
+merge_method: ties
+base_model: "Qwen/Qwen2.5-14B-Instruct-1M"
+parameters:
+  density: 1
+  normalize: true
+  int8_mask: true
+dtype: bfloat16
+```
+
+and I needed to make some minor adjustments to the tokenizer configuration.
+
+# How to Use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "mkurman/Qwen2.5-14B-DeepSeek-R1-1M"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Write a Python script to merge two CSV files."
+messages = [
+    {"role": "system", "content": "You are an expert programmer."},
+    {"role": "user", "content": prompt}
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+outputs = model.generate(inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+You can use it on `LM Studio` or `Ollama` by utilizing the provided GGUF files.
+
+# License
+Apache 2.0 for open-source contribution and collaboration.