L-R/LLmRa-1.3B_V2

LLmRa-1.3B-V2

A conversational Open Pre-trained Transformer Language Model fine-tune.

LLmRa 1.3B-V2, as a proof-of-concept fine-tune of facebook/opt-1.3b optimized for dialogue.

Disclaimer: NSFW data was included in the fine-tuning of this model. Although SFW inputs will usually result in SFW outputs, you are advised to chat at your own risk. This model is not suitable for use by minors.

Warning: This model is NOT suitable for use by minors. It will output X-rated content under certain circumstances.

Model Fine-Tuned on LLmRa-100K conversational dataset - small version

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Usage Format

To effectively utilize the model, follow this structured format for engaging text-based conversations:

1. Initialization

Here is how you can define the personality of the language model:

<|system|>[Persona]

• Persona: You can define a specific persona or context for the AI, but it's optional. It can be a character, a role, or just a style of interaction.

2. AI Introduction

<|user|>[User input]<|model|>

• Users can start the conversation by entering their message within <|user|> and closing with <|model|>.

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Example Usage:

Here's an example of how to start a conversation with the AI:

<|system|>I'm here to provide information and assistance on a wide range of topics.
<|model|>Hello! Welcome to our AI-powered assistant. How can I assist you today?
<|user|>Tell me about the history of artificial intelligence.
<|model|>

Continue the conversation as needed. This structured format helps maintain a smooth and engaging interaction with the AI.

You are not required to include User, you can change it to your prefered name or leave it blank You may also add the AI name, example:

<|user|>YourNameHere: Hello.<|model|>CharacterName:

You can also use this instruct prompt example:

<|system|>What is one plus one?<|model|>

Loading The Model

To use the model and interact with it, use the Python code below:

from transformers import (AutoModelForCausalLM,
                          AutoTokenizer,
                          pipeline,
                          )

model = AutoModelForCausalLM.from_pretrained('L-R/LLmRa-1.3B-V2')
tokenizer = AutoTokenizer.from_pretrained('L-R/LLmRa-1.3B-V2')

pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=100)

input_question = 'QUESTION HERE'

question_formatted = f'<|system|>{input_question}<|model|>'

result = pipe(question_formatted)

print(f"[model]: {result[0]['generated_text'][len(question_formatted):]}")

Known issues

Model doesn't some of the times follow instructions.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	34.21
AI2 Reasoning Challenge (25-Shot)	30.46
HellaSwag (10-Shot)	53.03
MMLU (5-Shot)	26.06
TruthfulQA (0-shot)	36.46
Winogrande (5-shot)	59.27
GSM8k (5-shot)	0.00