license: cc-by-nc-sa-4.0
datasets:
- Xilabs/PIPPA-alpaca
language:
- en
pipeline_tag: text-generation
Calypso 3B - Alpha V2 Model Card
Model Description
Model Name: Calypso 3B
Version: Calypso 3B - Alpha V2
Based on: openlm-research/open_llama_3b_v2
Calypso 3B is a language model designed for one-on-one chat interactions with a character or persona. It has been finetuned on the PIPPA-Alpaca dataset and a private dataset of human-generated chats. The model is particularly suited for providing conversational responses in a variety of contexts, making it suitable for role-playing, or one-on-one chatting.
Intended Use
Calypso 3B is intended to facilitate engaging and interactive one-on-one chat experiences.
Limitations and Ethical Considerations
Safety Note: Calypso 3B can produce content that may not be safe for all audiences. It may generate inappropriate, offensive, or sensitive content. User discretion is advised.
Factual Accuracy: The model's responses may not always be factually accurate. It should not be relied upon to provide accurate information, especially in critical or sensitive contexts.
Bias and Fairness: As with many language models, Calypso 3B might inadvertently exhibit biases present in the training data. Efforts have been made to mitigate this, but biases may still be present.
Example Usage
import gradio as gr
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
class Chat:
def __init__(self, model, tokenizer, conv_prompt, user_alias='User', character_name='Chatbot', message_history=[], chat_buffer_size=10):
self.model = model
self.tokenizer = tokenizer
self.conv_prompt = conv_prompt
self.user_alias = user_alias
self.character_name = character_name
self.chat_buffer_size = chat_buffer_size
self.message_history = message_history
self.display_messages = []
for message_pairs in message_history:
message1, message2 = message_pairs
self.display_messages.append([message1['text'], message2['text']])
def evaluate(self, message, temperature=0.6, top_p=0.75, top_k=50, num_beams=5, max_new_tokens=256, repetition_penalty=1.4, **kwargs):
prompt = self.prompt_gen_chat(self.message_history, message)
inputs = self.tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(self.model.device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
early_stopping=True,
repetition_penalty=repetition_penalty,
**kwargs,
)
with torch.no_grad():
generation_output = self.model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = self.tokenizer.decode(s, skip_special_tokens=True)
split_str = """### Response:\n{self.character_name}:"""
output = output.split(split_str)[1].strip()
return output
def gradio_helper(self, message):
# make response
response = self.evaluate(message)
# update message history
self.message_history.append(
(
{"speaker": self.user_alias, "text": message},
{"speaker": self.character_name, "text": response},
)
)
if len(self.message_history) > self.chat_buffer_size:
self.message_history = self.message_history[-self.chat_buffer_size:]
# update display messages
self.display_messages.append([message, response])
return self.display_messages
def prompt_gen_chat(self, message_history, message):
past_dialogue = []
for message_pairs in message_history:
message1, message2 = message_pairs
past_dialogue.append(f"{message1['speaker']}: {message1['text']}")
past_dialogue.append(f"{message2['speaker']}: {message2['text']}")
past_dialogue_formatted = "\n".join(past_dialogue)
prompt = f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{self.conv_prompt}
This is the conversation between {self.user_alias} and {self.character_name} till now:
{past_dialogue_formatted}
Continuing from the previous conversation, write what {self.character_name} says to {self.user_alias}:
### Input:
{self.user_alias}: {message}
### Response:
{self.character_name}:"""
return prompt
def launch_gradio(self):
with gr.Blocks(theme="JohnSmith9982/small_and_pretty") as demo:
chatbot = gr.Chatbot(elem_id="chatbot")
with gr.Row():
txt = gr.Textbox(show_label=False,
placeholder="Enter text and press enter")
txt.submit(self.gradio_helper, txt, chatbot)
txt.submit(lambda: "", None, txt)
demo.launch(debug=True, share=True)
if __name__ == "__main__":
model_path = "Xilabs/calypso-3b-alpha-v2"
load_in_8bit = False
model = LlamaForCausalLM.from_pretrained(
model_path, device_map="auto", load_in_8bit=load_in_8bit)
tokenizer = LlamaTokenizer.from_pretrained(model_path)
conv_prompt = "Two people are texting each other on a messaging platform."
message_history = [
(
{
"speaker": "Bob",
"text": "Hey, Alice! How are you doing? What's the status on those reports?",
},
{
"speaker": "Alice",
"text": "Hey, Bob! I'm doing well. I'm almost done with the reports. I'll send them to you by the end of the day.",
},
),
(
{
"speaker": "Bob",
"text": "That's great! Thanks, Alice. I'll be waiting for them. Btw, I have approved your leave for next week.",
},
{
"speaker": "Alice",
"text": "Oh, thanks, Bob! I really appreciate it. I will be sure to send you the reports before I leave. Anything else you need from me?",
},
)
]
chat_instance = Chat(model, tokenizer, conv_prompt, user_alias='Bob',
character_name='Alice', message_history=message_history)
chat_instance.launch_gradio()
Future Improvements
Calypso 3B is an ongoing project, and future iterations will focus on enhancing safety, improving factual accuracy, and reducing biases in its responses. The development team is committed to addressing user feedback and continuously improving the model's performance.
Licensing and Commercial Use
Larger and more permissive versions of Calypso will be released in the future. If you're interested in using Calypso 3B or its future iterations for commercial purposes, obtaining a license, or accessing the model via an API, please reach out to us for more information.
Disclaimer: This model card is provided for informational purposes only. Users are responsible for using the model in accordance with applicable laws and ethical considerations.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 37.52 |
| ARC (25-shot) | 41.55 |
| HellaSwag (10-shot) | 71.48 |
| MMLU (5-shot) | 25.82 |
| TruthfulQA (0-shot) | 35.73 |
| Winogrande (5-shot) | 65.27 |
| GSM8K (5-shot) | 0.68 |
| DROP (3-shot) | 22.08 |