ryota39
/

llm-jp-1b-sft-100k-LoRA-dpo-12k

@@ -3,197 +3,75 @@ library_name: transformers
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 tags: []
 ---
+## モデル
+- ベースモデル：[llm-jp/llm-jp-1.3b-v1.0](https://huggingface.co/llm-jp/llm-jp-1.3b-v1.0)
+- 学習データセット：[llm-jp/databricks-dolly-15k-ja](https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-ja)
+- 学習方式：フルパラメータチューニング
+## サンプル
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained(
+    "ryota39/llm-jp-1b-sft-15k"
+    )
+pad_token_id = tokenizer.pad_token_id
+model = AutoModelForCausalLM.from_pretrained(
+    "ryota39/llm-jp-1b-sft-15k",
+    device_map="auto",
+    )
+text = "###Input: 東京の観光名所を教えてください。\n###Output: "
+tokenized_input = tokenizer.encode(
+    text,
+    add_special_tokens=False,
+    return_tensors="pt"
+    ).to(model.device)
+attention_mask = torch.ones_like(tokenized_input)
+attention_mask[tokenized_input == pad_token_id] = 0
+with torch.no_grad():
+    output = model.generate(
+        tokenized_input,
+        attention_mask=attention_mask,
+        max_new_tokens=128,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.8,
+        repetition_penalty=1.0
+    )[0]
+print(tokenizer.decode(output))
+```
+## 出力例
+```
+###Input: 東京の観光名所を教えてください。
+###Output: 東京には多くの観光名所がある：
+1.皇居
+2.江戸東京博物館
+3.東京タワー
+4.東京スカイツリー
+5.芝公園
+6.東京タワー、増上寺、増上寺宝物館
+7.浜離宮恩賜庭園
+8.東京都庁
+9.増上寺
+10.新宿御苑
+11.浅草寺
+12.上野公園
+13.お台場
+14.明治神宮
+15.上野動物園
+16.東京国立博物館
+17.浅草寺、浅草寺仲見
+```