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README.md

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+---
+tags:
+- DeepCode-7B-Aurora-v4
+base_model:
+- DeepCode-7B-Aurora-v4
+- DeepCode-7B-Aurora-v4
+- DeepCode-7B-Aurora-v4
+- DeepCode-7B-Aurora-v4
+- DeepCode-7B-Aurora-v4
+- DeepCode-7B-Aurora-v4
+- DeepCode-7B-Aurora-v4
+---
+
+# DeepCode-7B-Aurora-v7
+
+DeepCode-7B-Aurora-v7 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
+* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
+* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
+* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
+* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
+* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
+* [DeepCode-7B-Aurora-v4](https://huggingface.co/DeepCode-7B-Aurora-v4)
+
+## 🧩 Configuration
+
+```yaml
+models:
+  - model: DeepCode-7B-Aurora-v4
+    parameters:
+      weight: 1
+  - model: DeepCode-7B-Aurora-v4
+    parameters:
+      weight: 1
+  - model: DeepCode-7B-Aurora-v4
+    parameters:
+      weight: 1
+  - model: DeepCode-7B-Aurora-v4
+    parameters:
+      weight: 1
+  - model: DeepCode-7B-Aurora-v4
+    parameters:
+      weight: 1
+  - model: DeepCode-7B-Aurora-v4
+    parameters:
+      weight: 1
+  - model: DeepCode-7B-Aurora-v4
+    parameters:
+      weight: 1
+merge_method: task_arithmetic
+base_model: DeepCode-7B-Aurora-v4
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: float16
+```
+
+## 💻 Usage
+
+```python
+os.system(f"pip install -qU transformers accelerate")
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+
+model = "ALBADDAWI/DeepCode-7B-Aurora-v7"
+messages = [{"role": "user", "content": "What is a large language model?"}]
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```