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Update README.md (#4)

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* Update README.md
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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/dev/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).
---
@ -159,7 +159,7 @@ The specific questions and test cases will be released soon. Stay tuned!
| DeepSeek LLM 7B Chat | 57.9 | 49.4 | 62.6 | 48.2 | 42.3 | 47.0 | 49.7 | 75.0 |
| DeepSeek LLM 67B Chat | 81.5 | 71.1 | 84.1 | 73.8 | 71.7 | 65.2 | 67.8 | 85.1 |
**Note:** We evaluate chat models with 0-shot for MMLU, GSM8K, C-Eval, and CMMLU. More evaluation results can be found [here](https://github.com/deepseek-ai/DeepSeek-LLM/blob/dev/evaluation/more_results.md).
**Note:** We evaluate chat models with 0-shot for MMLU, GSM8K, C-Eval, and CMMLU. More evaluation results can be found [here](https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/evaluation/more_results.md).
**Revisit Multi-Choice Question Benchmarks**
@ -297,9 +297,13 @@ print(generated_text)
## 6. FAQ
### Could You Provide the tokenizer.model File for GGUF Model Quantization?
### Could You Provide the tokenizer.model File for Model Quantization?
DeepSeek LLM 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 have submitted a [PR](https://github.com/ggerganov/llama.cpp/pull/4070) to the popular quantization repository [llama.cpp](https://github.com/ggerganov/llama.cpp) to fully support all HuggingFace pre-tokenizers, including ours.
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)
We have submitted a [PR](https://github.com/ggerganov/llama.cpp/pull/4070) to the popular quantization repository [llama.cpp](https://github.com/ggerganov/llama.cpp) to fully support all HuggingFace pre-tokenizers, including ours.
While waiting for the PR to be merged, you can generate your GGUF model using the following steps:
@ -311,11 +315,15 @@ git checkout regex_gpt2_preprocess
make
python3 -m pip install -r requirements.txt
# generate GGUF model
python convert-hf-to-gguf.py <MODEL_PATH> --outfile <GGUF_PATH> --model-name deepseekcoder
python convert-hf-to-gguf.py <MODEL_PATH> --outfile <GGUF_PATH> --model-name deepseekllm
# use q4_0 quantization as an example
./quantize <GGUF_PATH> <OUTPUT_PATH> q4_0
./main -m <OUTPUT_PATH> -n 128 -p <PROMPT>
```
#### GPTQ(exllamav2)
`UPDATE:`[exllamav2](https://github.com/turboderp/exllamav2) has been able to support HuggingFace Tokenizer. Please pull the latest version and try out.
## 7. Limitation