Instructions to use AINovice2005/ElEmperador with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AINovice2005/ElEmperador with Transformers:

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

pipe = pipeline("text-generation", model="AINovice2005/ElEmperador")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AINovice2005/ElEmperador")
model = AutoModelForCausalLM.from_pretrained("AINovice2005/ElEmperador")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AINovice2005/ElEmperador with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AINovice2005/ElEmperador"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AINovice2005/ElEmperador",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AINovice2005/ElEmperador

SGLang

How to use AINovice2005/ElEmperador 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 "AINovice2005/ElEmperador" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AINovice2005/ElEmperador",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "AINovice2005/ElEmperador" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AINovice2005/ElEmperador",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AINovice2005/ElEmperador with Docker Model Runner:
```
docker model run hf.co/AINovice2005/ElEmperador
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Overview

𝐌𝐨𝐝𝐞𝐥 𝐍𝐚𝐦𝐞:ElEmperador

Model Description:

ElEmperador is an ORPO-based finetune derived from the Mistral-7B-v0.1 base model.

Evals:

BLEU:0.209

Inference Script:

def generate_response(model_name, input_text, max_new_tokens=50):
    # Load the tokenizer and model from Hugging Face Hub
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name)
    
    # Tokenize the input text
    input_ids = tokenizer(input_text, return_tensors='pt').input_ids
    
    # Generate a response using the model
    with torch.no_grad():
        generated_ids = model.generate(input_ids, max_new_tokens=max_new_tokens)
    
    # Decode the generated tokens into text
    generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
    
    return generated_text

if __name__ == "__main__":
    # Set the model name from Hugging Face Hub
    model_name = "AINovice2005/ElEmperador" 
    input_text = "Hello, how are you?"

    # Generate and print the model's response
    output = generate_response(model_name, input_text)
    
    print(f"Input: {input_text}")
    print(f"Output: {output}")

Results

Firstly,ORPO is a viable RLHF algorithm to improve the performance of your models along with SFT finetuning.Secondly, it also helps in aligning the model’s outputs more closely with human preferences, leading to more user-friendly and acceptable results.

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

F16

Model tree for AINovice2005/ElEmperador

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

this model

Quantizations

1 model

AINovice2005
/

ElEmperador