Instructions to use shadowml/DareBeagel-2x7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shadowml/DareBeagel-2x7B with Transformers:

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

pipe = pipeline("text-generation", model="shadowml/DareBeagel-2x7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shadowml/DareBeagel-2x7B")
model = AutoModelForCausalLM.from_pretrained("shadowml/DareBeagel-2x7B")
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 shadowml/DareBeagel-2x7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shadowml/DareBeagel-2x7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shadowml/DareBeagel-2x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shadowml/DareBeagel-2x7B

SGLang

How to use shadowml/DareBeagel-2x7B 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 "shadowml/DareBeagel-2x7B" \
    --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": "shadowml/DareBeagel-2x7B",
		"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 "shadowml/DareBeagel-2x7B" \
        --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": "shadowml/DareBeagel-2x7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shadowml/DareBeagel-2x7B with Docker Model Runner:
```
docker model run hf.co/shadowml/DareBeagel-2x7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Beyonder-2x7B-v2

Beyonder-2x7B-v2 is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

🧩 Configuration

base_model: mlabonne/NeuralBeagle14-7B
gate_mode: random
experts:
  - source_model: mlabonne/NeuralBeagle14-7B
    positive_prompts: [""]
  - source_model: mlabonne/NeuralDaredevil-7B
    positive_prompts: [""]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "shadowml/Beyonder-2x7B-v2"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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.	74.49
AI2 Reasoning Challenge (25-Shot)	72.01
HellaSwag (10-Shot)	88.12
MMLU (5-Shot)	64.51
TruthfulQA (0-shot)	69.09
Winogrande (5-shot)	82.72
GSM8k (5-shot)	70.51

Downloads last month: 201

Safetensors

Model size

13B params

Tensor type

F16

Model tree for shadowml/DareBeagel-2x7B

Quantizations

3 models

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

88.120
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.510
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

69.090
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

82.720
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

70.510