kevin009/llamaRAGdrama

It remain factual and reliable even in dramatic situations.

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Model Card for kevin009/llamaRAGdrama

Model Details

• Model Name: kevin009/llamaRAGdrama
• Model Type: Fine-tuned for Q&A, RAG.
• Fine-tuning Objective: Synthesis text content in Q&A, RAG scenarios.

Intended Use

• Applications: RAG, Q&A

Training Data

• Sources: Includes a diverse dataset of dramatic texts, enriched with factual databases and reliable sources to train the model on generating content that remains true to real-world facts.
• Preprocessing: In addition to removing non-content text, data was annotated to distinguish between purely creative elements and those that require factual accuracy, ensuring a balanced training approach.

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kevin009/llamaRAGdrama")
model = AutoModelForCausalLM.from_pretrained("kevin009/llamaRAGdrama")

input_text = "Enter your prompt here"
input_tokens = tokenizer.encode(input_text, return_tensors='pt')
output_tokens = model.generate(input_tokens, max_length=100, num_return_sequences=1, temperature=0.9)
generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)

print(generated_text)

Replace "Enter your prompt here" with your starting text. Adjust temperature for creativity level.

Limitations and Biases

• Content Limitation: While designed to be truthful, It may not be considered safe.
• Biases: It may remain biases and inaccurate.

Licensing and Attribution

• License: Apache-2.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.65
AI2 Reasoning Challenge (25-Shot)	72.01
HellaSwag (10-Shot)	88.83
MMLU (5-Shot)	64.50
TruthfulQA (0-shot)	70.24
Winogrande (5-shot)	86.66
GSM8k (5-shot)	65.66