Instructions to use maxcurrent/Peagle-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maxcurrent/Peagle-9b with Transformers:

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

pipe = pipeline("text-generation", model="maxcurrent/Peagle-9b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maxcurrent/Peagle-9b")
model = AutoModelForCausalLM.from_pretrained("maxcurrent/Peagle-9b")
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 maxcurrent/Peagle-9b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maxcurrent/Peagle-9b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maxcurrent/Peagle-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maxcurrent/Peagle-9b

SGLang

How to use maxcurrent/Peagle-9b 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 "maxcurrent/Peagle-9b" \
    --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": "maxcurrent/Peagle-9b",
		"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 "maxcurrent/Peagle-9b" \
        --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": "maxcurrent/Peagle-9b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maxcurrent/Peagle-9b with Docker Model Runner:
```
docker model run hf.co/maxcurrent/Peagle-9b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Peagle-9b

Hey there! 👋 Welcome to the Peagle-14b! This is a merge of multiple models brought together using the awesome VortexMerge kit.

Let's see what we've got in this merge:

mlabonne/NeuralBeagle14-7B 🚀
eldogbbhed/NeuralPearlBeagle 🚀

🧩 Configuration

slices:
  - sources:
      - model: mlabonne/NeuralBeagle14-7B
        layer_range: [0, 20]
  - sources:
      - model: eldogbbhed/NeuralPearlBeagle
        layer_range: [12, 32]
merge_method: passthrough

Downloads last month: 13,413

Safetensors

Model size

9B params

Tensor type

F16

Model tree for maxcurrent/Peagle-9b

Quantizations

1 model

maxcurrent
/

Peagle-9b

Peagle-9b

🧩 Configuration

Model tree for maxcurrent/Peagle-9b

Space using maxcurrent/Peagle-9b 1