--- license: other license_name: deepseek license_link: https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL --- # deepseek-math-7b-instruct-RK3588-1.1.4 This version of deepseek-math-7b-instruct has been converted to run on the RK3588 NPU using ['w8a8', 'w8a8_g128', 'w8a8_g256', 'w8a8_g512'] quantization. This model has been optimized with the following LoRA: Compatible with RKLLM version: 1.1.4 ## Useful links: [Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) [RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) [EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit # Original Model Card for base model, deepseek-math-7b-instruct, below:

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### 1. Introduction to DeepSeekMath See the [Introduction](https://github.com/deepseek-ai/DeepSeek-Math) for more details. ### 2. How to Use Here give some examples of how to use our model. **Chat Completion** ❗❗❗ **Please use chain-of-thought prompt to test DeepSeekMath-Instruct and DeepSeekMath-RL:** - English questions: **{question}\nPlease reason step by step, and put your final answer within \\boxed{}.** - Chinese questions: **{question}\n请通过逐步推理来解答问题,并把最终答案放置于\\boxed{}中。** ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "deepseek-ai/deepseek-math-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") model.generation_config = GenerationConfig.from_pretrained(model_name) model.generation_config.pad_token_id = model.generation_config.eos_token_id messages = [ {"role": "user", "content": "what is the integral of x^2 from 0 to 2?\nPlease reason step by step, and put your final answer within \\boxed{}."} ] input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt") outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100) result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True) print(result) ``` Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input. ``` User: {messages[0]['content']} Assistant: {messages[1]['content']}<|end▁of▁sentence|>User: {messages[2]['content']} Assistant: ``` **Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<|begin▁of▁sentence|>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input. ### 3. License This code repository is licensed under the MIT License. The use of DeepSeekMath models is subject to the Model License. DeepSeekMath supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/DeepSeek-Math/blob/main/LICENSE-MODEL) for more details. ### 4. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).