Instructions to use eren23/FrankenBeagle-SmallOverlap-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eren23/FrankenBeagle-SmallOverlap-test with Transformers:

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

pipe = pipeline("text-generation", model="eren23/FrankenBeagle-SmallOverlap-test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("eren23/FrankenBeagle-SmallOverlap-test")
model = AutoModelForMultimodalLM.from_pretrained("eren23/FrankenBeagle-SmallOverlap-test")
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 Settings

vLLM

How to use eren23/FrankenBeagle-SmallOverlap-test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eren23/FrankenBeagle-SmallOverlap-test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eren23/FrankenBeagle-SmallOverlap-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eren23/FrankenBeagle-SmallOverlap-test

SGLang

How to use eren23/FrankenBeagle-SmallOverlap-test 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 "eren23/FrankenBeagle-SmallOverlap-test" \
    --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": "eren23/FrankenBeagle-SmallOverlap-test",
		"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 "eren23/FrankenBeagle-SmallOverlap-test" \
        --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": "eren23/FrankenBeagle-SmallOverlap-test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eren23/FrankenBeagle-SmallOverlap-test with Docker Model Runner:
```
docker model run hf.co/eren23/FrankenBeagle-SmallOverlap-test
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FrankenBeagle-SmallOverlap-test

FrankenBeagle-SmallOverlap-test is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
    - model: mlabonne/NeuralBeagle14-7B
      layer_range: [0, 24]
  - sources:
    - model: mlabonne/NeuralBeagle14-7B
      layer_range: [18, 32]
merge_method: passthrough
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "eren23/FrankenBeagle-SmallOverlap-test"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.30
AI2 Reasoning Challenge (25-Shot)	72.01
HellaSwag (10-Shot)	88.16
MMLU (5-Shot)	64.71
TruthfulQA (0-shot)	69.69
Winogrande (5-shot)	81.85
GSM8k (5-shot)	63.38

Downloads last month: 57

Safetensors

Model size

9B params

Tensor type

BF16

Model tree for eren23/FrankenBeagle-SmallOverlap-test

Base model

mlabonne/Beagle14-7B

Finetuned

mlabonne/NeuralBeagle14-7B

Finetuned

(10)

this model

Quantizations

1 model

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

88.160
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.710
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

69.690
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

81.850
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

63.380