SaiCD/flan-t5-large-finetuned-ai-assistant

flan-t5-large-finetuned-ai-assistant

Instruction-tuned FLAN-T5-Large checkpoint intended for helpful assistant-style responses.

What I Improved (Config Tuning)

This repo currently contains model artifacts (weights + tokenizer + config), but no training dataset or training pipeline. As a result, this update focuses on inference-quality tuning rather than weight-level fine-tuning:

• —Added stronger default decoding settings in generation_config.json
• —Tuned for more accurate / less repetitive / more stable responses by default

New default decoding behavior

• —do_sample = false (deterministic outputs)
• —num_beams = 4 (better search quality than greedy)
• —no_repeat_ngram_size = 3 (reduces repetitive phrasing)
• —max_new_tokens = 192 (enough for helpful answers without rambling)
• —repetition_penalty = 1.05
• —early_stopping = true

These defaults favor precision and helpfulness over creativity. For brainstorming/creative writing, override with do_sample=True, temperature, and top_p.

Recommended Prompting Format

FLAN-T5 generally performs best with explicit instructions. Use a structured prompt like:

You are a helpful assistant. Answer precisely and clearly.

Question: {user_question}
Answer:

For tasks:

• —Summarization: Summarize the following text in 5 bullet points: ...
• —Extraction: Extract the key risks from the text: ...
• —Classification: Classify the sentiment (positive/neutral/negative): ...

Usage (Transformers)

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_id = "SaiCD/flan-t5-large-finetuned-ai-assistant"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

prompt = """You are a helpful assistant. Answer precisely and clearly.

Question: What are three ways to improve sleep quality?
Answer:"""

inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
outputs = model.generate(**inputs)  # uses generation_config.json defaults
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Override for More Creative Responses

outputs = model.generate(
    **inputs,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    max_new_tokens=192,
)

Limitations

• —This update does not retrain weights (no dataset/training code is present in this repo).
• —FLAN-T5-Large can still hallucinate facts; use grounding / retrieval for high-stakes tasks.
• —Input length is constrained by model context limits.

If You Want True Fine-Tuning Next

To perform weight-level fine-tuning, provide:

1. 1.Training dataset (instruction-response pairs)
2. 2.Target task(s) and evaluation criteria
3. 3.Compute environment (GPU recommended)

Then fine-tune with PEFT/LoRA or full training and push a new checkpoint.