Deepreneur
/

blue-lizard

@@ -6,195 +6,55 @@ language:
 # Deepreneur-blue-lizard
 <!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
 <!-- Provide a longer summary of what this model is. -->
 Deepreneur-blue-lizardは、MetaのLlama-2-7bに対して、Wikipediaや書籍等の日本語の学習データを用いて追加事前学習と独自データによるファインチューニングを実施したモデルです。
 70億パラメータと非常に軽量なモデルであるにも関わらず、JGLUE（日本語タスクにおける評価ベンチマーク）を用いた評価では、ChatGPT-3.5を超えるスコアが算出されており、公開されている日本語モデルの中では最高性能になります。
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-## Evaluation
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 # Deepreneur-blue-lizard
 <!-- Provide a quick summary of what the model is/does. -->
+![](deepreneur_Lizard_thumbnail.png)
+## Model Description
 <!-- Provide a longer summary of what this model is. -->
 Deepreneur-blue-lizardは、MetaのLlama-2-7bに対して、Wikipediaや書籍等の日本語の学習データを用いて追加事前学習と独自データによるファインチューニングを実施したモデルです。
 70億パラメータと非常に軽量なモデルであるにも関わらず、JGLUE（日本語タスクにおける評価ベンチマーク）を用いた評価では、ChatGPT-3.5を超えるスコアが算出されており、公開されている日本語モデルの中では最高性能になります。
+## How to use
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。"
+text = "deepreneurについて教えて"
+model_name = "Deepreneur/blue-lizard"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+)
+if torch.cuda.is_available():
+    model = model.to("cuda")
+prompt = "{bos_token}{b_inst} {system}{prompt} {e_inst}".format(
+  bos_token=tokenizer.bos_token,
+  b_inst=B_INST,
+  system=f"{B_SYS}{DEFAULT_SYSTEM_PROMPT}{E_SYS}",
+  prompt=text,
+  e_inst=E_INST,
+)
+with torch.no_grad():
+    token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
+    output_ids = model.generate(
+        token_ids.to(model.device),
+        max_new_tokens=256,
+        pad_token_id=tokenizer.pad_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+    )
+output = tokenizer.decode(output_ids.tolist()[0][token_ids.size(1) :], skip_special_tokens=True)
+print(output)
+```