--- license: apache-2.0 model-index: - name: llamaRAGdrama results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 72.01 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kevin009/llamaRAGdrama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 88.83 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kevin009/llamaRAGdrama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 64.5 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kevin009/llamaRAGdrama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 70.24 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kevin009/llamaRAGdrama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 86.66 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kevin009/llamaRAGdrama name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 65.66 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kevin009/llamaRAGdrama name: Open LLM Leaderboard --- 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 ```python 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_kevin009__llamaRAGdrama) | 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|