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@ -126,7 +126,7 @@ In line with Grok-1, we have evaluated the model's mathematical capabilities usi
<img src="images/mathexam.png" alt="result" width="70%">
</div>
**Remark:** Some results are obtained by DeepSeek authors, while others are done by Grok-1 authors. We found some models count the score of the last question (Llemma 34b and Mammoth) while some (MetaMath-7B) are not in the original evaluation. In our evaluation, we count the last question score. Evaluation details are [here](https://github.com/deepseek-ai/DeepSeek-LLM/tree/HEAD/evaluation/hungarian_national_hs_solutions).
**Remark:** Some results are obtained by DeepSeek LLM authors, while others are done by Grok-1 authors. We found some models count the score of the last question (Llemma 34b and Mammoth) while some (MetaMath-7B) are not in the original evaluation. In our evaluation, we count the last question score. Evaluation details are [here](https://github.com/deepseek-ai/DeepSeek-LLM/tree/HEAD/evaluation/hungarian_national_hs_solutions).
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@ -299,7 +299,7 @@ print(generated_text)
### Could You Provide the tokenizer.model File for Model Quantization?
DeepSeek Coder utilizes the [HuggingFace Tokenizer](https://huggingface.co/docs/tokenizers/index) to implement the Bytelevel-BPE algorithm, with specially designed pre-tokenizers to ensure optimal performance. Currently, there is no direct way to convert the tokenizer into a SentencePiece tokenizer. We are contributing to the open-source quantization methods facilitate the usage of HuggingFace Tokenizer.
DeepSeek LLM utilizes the [HuggingFace Tokenizer](https://huggingface.co/docs/tokenizers/index) to implement the Byte-level-BPE algorithm, with specially designed pre-tokenizers to ensure optimal performance. Currently, there is no direct way to convert the tokenizer into a SentencePiece tokenizer. We are contributing to the open-source quantization methods facilitate the usage of HuggingFace Tokenizer.
#### GGUF(llama.cpp)