Instructions to use nbeerbower/Lyra4-Gutenberg2-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nbeerbower/Lyra4-Gutenberg2-12B with Transformers:

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

pipe = pipeline("text-generation", model="nbeerbower/Lyra4-Gutenberg2-12B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nbeerbower/Lyra4-Gutenberg2-12B")
model = AutoModelForCausalLM.from_pretrained("nbeerbower/Lyra4-Gutenberg2-12B", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nbeerbower/Lyra4-Gutenberg2-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/Lyra4-Gutenberg2-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nbeerbower/Lyra4-Gutenberg2-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nbeerbower/Lyra4-Gutenberg2-12B

SGLang

How to use nbeerbower/Lyra4-Gutenberg2-12B 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 "nbeerbower/Lyra4-Gutenberg2-12B" \
    --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": "nbeerbower/Lyra4-Gutenberg2-12B",
		"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 "nbeerbower/Lyra4-Gutenberg2-12B" \
        --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": "nbeerbower/Lyra4-Gutenberg2-12B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nbeerbower/Lyra4-Gutenberg2-12B with Docker Model Runner:
```
docker model run hf.co/nbeerbower/Lyra4-Gutenberg2-12B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lyra4-Gutenberg2-12B

Sao10K/MN-12B-Lyra-v4 finetuned on jondurbin/gutenberg-dpo-v0.1 and nbeerbower/gutenberg2-dpo.

Features an increased sequence length from Lyra4-Gutenberg-12B.

Method

ORPO Finetuned using 2x RTX 3090 for 3 epochs.

Training data was formatted with ChatML.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	19.74
IFEval (0-Shot)	25.85
BBH (3-Shot)	33.73
MATH Lvl 5 (4-Shot)	10.50
GPQA (0-shot)	8.39
MuSR (0-shot)	11.49
MMLU-PRO (5-shot)	28.51

Downloads last month: 15

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for nbeerbower/Lyra4-Gutenberg2-12B

Base model

Sao10K/MN-12B-Lyra-v4

Finetuned

(3)

this model

Merges

5 models

Quantizations

9 models

Datasets used to train nbeerbower/Lyra4-Gutenberg2-12B

Spaces using nbeerbower/Lyra4-Gutenberg2-12B 6

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

25.850
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

33.730
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

10.500
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

8.390
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

11.490
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

28.510