It remain factual and reliable even in dramatic situations.
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 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard72.010
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard88.830
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard64.500
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard70.240
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard86.660
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard65.660