added plots

README.md

@@ -17,11 +17,11 @@ This base model was able to attain class-leading Japanese performance in standar
 
 We used a slightly modified [llm-jp-eval](https://github.com/llm-jp/llm-jp-eval) (our base model requires a `bos_token` to be prepended to the prompt; we tested other models with and without the modification and took the higher results for all models tested). Here we validate versus the original Mistral 7B base model as well as [Japanese Stable LM Instruct Gamma 7B](https://huggingface.co/stabilityai/japanese-stablelm-instruct-gamma-7b), which is a Mistral 7B base with an additional 100B tokens of JA/EN pre-training. We also include [Japanese-StableLM-Base-Beta-70B](https://huggingface.co/stabilityai/japanese-stablelm-base-beta-70b), which is a Llama 2 70B that also has an additional 100B tokens of JA/EN pre-training as a reference:
 
-![Mistral llm-jp-eval Comparison]()
+![Mistral llm-jp-eval Comparison](https://huggingface.co/augmxnt/mistral-7b-ja-v0.1/resolve/main/llm-jp-eval.mistral.png)
 
 Here we also compare `mistral-7b-ja-v0.1` to other recently-released similar classed (7B parameter) Japanese-tuned models. [ELYZA 7B fast model](https://huggingface.co/elyza/ELYZA-japanese-Llama-2-7b-fast) and [Youri 7B](https://huggingface.co/rinna/youri-7b) are Llama 2 7B models with 18B and 40B of additional pre-training respectively, and [CALM2-7B](https://huggingface.co/cyberagent/calm2-7b) and [llm-jp-13b]() are pretrained models with 1.3T and 300B JA/EN tokens of pre-training:
 
-![7B llm-jp-eval Performance]()
+![7B llm-jp-eval Performance](https://huggingface.co/augmxnt/mistral-7b-ja-v0.1/resolve/main/llm-jp-eval.ja.png)
 
 ## Tokenizer
 As mentioned in the introduction, our tokenizer is an extended version of the Mistral 7B tokenizer, with a vocab size of  120073 and aligned to 128K. The remaining unused tokens are assigned as average-weighted `<|extra_{idx}|>` tokens.