README.md

metadata

tags:
  - deepseek-ai/deepseek-math-7b-instruct
  - deepseek-ai/deepseek-math-7b-base
  - deepseek-ai/deepseek-coder-7b-instruct-v1.5
base_model:
  - deepseek-ai/deepseek-math-7b-instruct
  - deepseek-ai/deepseek-math-7b-base
  - deepseek-ai/deepseek-coder-7b-instruct-v1.5
license: mit

DeepCode-7B-Aurora

DeepCode-7B-Aurora is a merge of the following models using LazyMergekit:

• deepseek-ai/deepseek-math-7b-instruct
• deepseek-ai/deepseek-math-7b-base
• deepseek-ai/deepseek-coder-7b-instruct-v1.5

🧩 Configuration

models:
  - model: deepseek-ai/deepseek-math-7b-rl
    # No parameters necessary for base model
  - model: deepseek-ai/deepseek-math-7b-instruct
    parameters:
      density: 0.53
      weight: 0.4
  - model: deepseek-ai/deepseek-math-7b-base
    parameters:
      density: 0.53
      weight: 0.3
  - model: deepseek-ai/deepseek-coder-7b-instruct-v1.5
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: deepseek-ai/deepseek-math-7b-rl
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ALBADDAWI/DeepCode-7B-Aurora"
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"])