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llm-jp-3-13b-instruct-finetune_1217

Transformers

Safetensors

Japanese

Inference Endpoints

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 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+datasets:
+- elyza/ELYZA-tasks-100
+license: apache-2.0
+language:
+- ja
+base_model:
+- llm-jp/llm-jp-3-13b-instruct
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
+## Required Libraries and Their Versions
+- trl==0.12.2
+- transformers<4.47.0
+- tokenizers==0.21.0
+## Usage
+```py
+results = []
+system_text = "以下は、タスクを説明する指示です。要求を適切に満たす回答を**簡潔に**書きなさい。"
+for data in tqdm(datasets):
+  input_text = data["input"]
+  prompt = f"""
+  {system_text}
+  ### 指示
+  {input_text}
+  ### 応答
+  """
+  tokenized_input = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
+  attention_mask = torch.ones_like(tokenized_input)
+  with torch.no_grad():
+      outputs = model.generate(
+          tokenized_input,
+          attention_mask=attention_mask,
+          max_new_tokens=100,
+          do_sample=False,
+          repetition_penalty=1.2,
+          pad_token_id=tokenizer.eos_token_id
+      )[0]
+  output = tokenizer.decode(outputs[tokenized_input.size(1):], skip_special_tokens=True)
+  results.append({"task_id": data["task_id"], "input": input_text, "output": output})
+```
+## Model Details
+- **Model type:** Transformer-based Language Model
+## Datasets
+### Instruction tuning
+| Language | Dataset | description |
+|:---|:---|:---|
+|Japanese|[elyza/ELYZA-tasks-100](https://huggingface.co/datasets/elyza/ELYZA-tasks-100)| A manually constructed instruction dataset |
+## License
+[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)