Model description

The tiiuae/falcon-7b model finetuned for Paraphrasing, Changing the Tone of the input sentence(to casual/professional/witty), Summary and Topic generation from a dialogue. Data for Paraphrasing and Changing the Tone was generated using gpt-35-turbo and a sample of roughly 1000 data points from the Dialogsum dataset was used for Summary and Topic generation.

Look at the repo llm-toys for usage and other details.

Try in colab (you might need the pro version): Open In Colab

Installation

pip install llm-toys
from llm_toys.tasks import GeneralTaskAssitant
from llm_toys.config import TaskType

gta = GeneralTaskAssitant()
gta.complete(TaskType.PARAPHRASE_TONE, "Hey, can yuo hepl me cancel my last order?")
# "Could you assist me in canceling my previous order?"

gta.complete(TaskType.PARAPHRASE_TONE, "Hey, can yuo hepl me cancel my last order?", tone="casual")
# "Hey, can you help me cancel my last order?"

gta.complete(TaskType.PARAPHRASE_TONE, "Hey, can yuo hepl me cancel my last order?", tone="professional")
# "I would appreciate if you could assist me in canceling my previous order."

gta.complete(TaskType.PARAPHRASE_TONE, "Hey, can yuo hepl me cancel my last order?", tone="witty")
# "Oops! Looks like I got a little carried away with my shopping spree. Can you help me cancel my last order?"

chat = """
#Person1#: I'm so excited for the premiere of the latest Studio Ghibli movie!
#Person2#: What's got you so hyped?
#Person1#: Studio Ghibli movies are pure magic! The animation, storytelling, everything is incredible.
#Person2#: Which movie is it?
#Person1#: It's called "Whisper of the Wind." It's about a girl on a magical journey to save her village.
#Person2#: Sounds amazing! I'm in for the premiere.
#Person1#: Great! We're in for a visual masterpiece and a heartfelt story.
#Person2#: Can't wait to be transported to their world.
#Person1#: It'll be an unforgettable experience, for sure!
""".strip()
gta.complete(TaskType.DIALOGUE_SUMMARY_TOPIC, chat)
# {"summary": "#Person1# tells #Person2# about the upcoming Studio Ghibli movie.
#              #Person1# thinks it's magical and #Person2#'s excited to watch it.",
#  "topic": "Movie premiere"}

Sample training data

[
{
  "original": "If you have any further questions, feel free to ask.",
  "casual": "Got more questions? Feel free to ask away. I'm here to help!",
  "professional": "Should you have any additional inquiries, please don't hesitate to ask.",
  "witty": "Curiosity is always in style! If you have more mysteries to solve, I'm all ears!",
  "paraphrase": "Don't hesitate to ask if you have any more questions."
},
{
  "fname": "dev_473",
  "dialogue": "#Person1#: Did you enjoy your weekend at the highland hotel? I heard it's and excellent place to stay and has good facilities.\n#Person2#: I had a wonderful time. The rooms are not very big, but they are well furnished. The restaurant is excellent and reasonably priced. There's a sauna and a Jacuzzi.\n#Person1#: Do they have a swimming pool?\n#Person2#: No, they don't. they have a beauty parlor, but I didn't go there.\n#Person1#: What's the service like?\n#Person2#: It's very good. Check in and check out at the reception only took a few minutes. The wait staff is very good. A waiter recommended their baked fish, which tasted wonderful. The hotel was quite full, so I'd suggest making a reservation if you intend to go there. The hotel offers a discount at the weekends.\n#Person1#: It sounds perfect. Did you have any complaints at all?\n#Person2#: There was a problem with the internet access, so I couldn't check my email, but I didn't complain about it to the management.\n#Person1#: I suppose you were happy to forget about the outside world.\n#Person2#: Yes, I was. Here's their business card.\n#Person1#: Thanks. Was there a mina bar in the room?\n#Person2#: No, there wasn't. There is a bar on the ground floor and of course you can buy drinks in the restaurant to go with your meal.\n#Person1#: One of the things I dislike about hotels is that everyone expects tips.\n#Person2#: I know. At the inland hotel, they have an interesting policy. When you check out, you put some money in a special box at reception. Each evening, the money in the box is shared equally by the hotel staff.",
  "summary": "#Person2# enjoys #Person2#'s weekend at the highland hotel because of the hotel's excellent and reasonably priced restaurant and good service. #Person2# introduces the hotel's facilities, weekend discount, and its interesting tip policy and suggests #Person1# make a reservation in advance.",
  "topic": "Experience in hotel"
}
]

Training params

{
  "batch_size": 1,
  "eval_ratio": 0.05,
  "eval_steps": 100,
  "gradient_accumulation_steps": 4,
  "learning_rate": 0.0001,
  "logging_steps": 100,
  "lora_alpha": 32,
  "lora_dropout": 0.05,
  "lora_r": 16,
  "max_length": 1024,
  "model_name": "tiiuae/falcon-7b",
  "num_train_epochs": 3,
  "seed": 10,
  "task_type": "paraphrase_tone,dialogue_summary_topic",
  "use_aim": True
}

Training curve

train_eval_loss

Training procedure

The following bitsandbytes quantization config was used during training:

  • load_in_8bit: False
  • load_in_4bit: True
  • llm_int8_threshold: 6.0
  • llm_int8_skip_modules: None
  • llm_int8_enable_fp32_cpu_offload: False
  • llm_int8_has_fp16_weight: False
  • bnb_4bit_quant_type: nf4
  • bnb_4bit_use_double_quant: True
  • bnb_4bit_compute_dtype: bfloat16

Framework versions

  • PEFT 0.4.0.dev0
Downloads last month
5
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.