Instructions to use kevin009/llamaRAGdrama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kevin009/llamaRAGdrama with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kevin009/llamaRAGdrama")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kevin009/llamaRAGdrama with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kevin009/llamaRAGdrama"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kevin009/llamaRAGdrama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kevin009/llamaRAGdrama

SGLang

How to use kevin009/llamaRAGdrama with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kevin009/llamaRAGdrama" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kevin009/llamaRAGdrama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kevin009/llamaRAGdrama" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kevin009/llamaRAGdrama",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kevin009/llamaRAGdrama with Docker Model Runner:
```
docker model run hf.co/kevin009/llamaRAGdrama
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Downloads last month: 335

Safetensors

Model size

7B params

Tensor type

F16

Model tree for kevin009/llamaRAGdrama

Quantizations

4 models

Spaces using kevin009/llamaRAGdrama 9

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

72.010
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.830
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.500
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

70.240
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

86.660
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

65.660