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---
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|
|