llm-jp-3-13b-it-docker_lora

Model Card for llm-jp-3-13b-it-docker

Uses

Direct Use

The model can be used directly for generating responses to user-provided instructions. Example tasks include:

• Answering questions in Japanese.
• Generating Japanese text based on prompts.
• Following instructions to produce summarized or structured output.

Downstream Use [optional]

This model can be further fine-tuned on task-specific datasets. For example:

• Chatbot applications for Japanese customer support.
• Document summarization or question-answering systems in Japanese.
• Code explanation or tutoring in Japanese.

Out-of-Scope Use

The model may not perform well on:

• Non-Japanese languages (it is trained on Japanese).
• Tasks requiring extensive reasoning over large contexts beyond its maximum context length.
• Highly specialized domains not represented in the training data.
• Sensitive or personal identifiable information requests.

Bias, Risks, and Limitations

The model might reflect biases present in its training data. It may produce:

• Inaccurate or outdated factual information.
• Unintended, biased, or harmful responses if prompted with sensitive or adversarial content.

Recommendations

• Users should verify critical facts generated by the model.
• Avoid using the model for sensitive decision-making without human oversight.
• Consider employing content filtering and moderation strategies.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("joijwoafeij/llm-jp-3-13b-it-docker_lora")
model = AutoModelForCausalLM.from_pretrained("joijwoafeij/llm-jp-3-13b-it-docker_lora")

prompt = "### 指示\n日本の有名な詩人について教えてください\n### 回答\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))