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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. |