Instructions to use wraith1337/Sprout with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wraith1337/Sprout with Transformers:

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

pipe = pipeline("text-generation", model="wraith1337/Sprout")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wraith1337/Sprout")
model = AutoModelForCausalLM.from_pretrained("wraith1337/Sprout", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use wraith1337/Sprout with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wraith1337/Sprout:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wraith1337/Sprout:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wraith1337/Sprout:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf wraith1337/Sprout:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf wraith1337/Sprout:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf wraith1337/Sprout:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf wraith1337/Sprout:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wraith1337/Sprout:Q4_K_M

Use Docker

docker model run hf.co/wraith1337/Sprout:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use wraith1337/Sprout with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wraith1337/Sprout"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wraith1337/Sprout",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wraith1337/Sprout:Q4_K_M

SGLang

How to use wraith1337/Sprout 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 "wraith1337/Sprout" \
    --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": "wraith1337/Sprout",
		"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 "wraith1337/Sprout" \
        --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": "wraith1337/Sprout",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use wraith1337/Sprout with Ollama:
```
ollama run hf.co/wraith1337/Sprout:Q4_K_M
```
Unsloth Desktop

How to use wraith1337/Sprout with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wraith1337/Sprout:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "wraith1337/Sprout:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use wraith1337/Sprout with Docker Model Runner:
```
docker model run hf.co/wraith1337/Sprout:Q4_K_M
```

Lemonade

How to use wraith1337/Sprout with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wraith1337/Sprout:Q4_K_M

Run and chat with the model

lemonade run user.Sprout-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use wraith1337/Sprout with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wraith1337/Sprout:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default wraith1337/Sprout:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wraith1337/Sprout with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wraith1337/Sprout:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "wraith1337/Sprout:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

wraith1337 commited on 2 days ago

Commit

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1 Parent(s): 96f8023

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+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM3-3B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+  - text-generation
+  - transformers
+  - safetensors
+  - gguf
+  - smollm3
+  - sprout
+  - sprynn
+---
+# Sprout
+Sprout is the assistant model of Sprynn, fine-tuned with Sprynn identity, persona and product knowledge.
+Pilot release based on HuggingFaceTB/SmolLM3-3B, with full fp16 weights and a Q4_K_M GGUF for lightweight local runs.
+## Files
+- `model.safetensors`: 6,150,234,768 bytes
+- `sprout-Q4_K_M.gguf`: 1,915,305,440 bytes
+- tokenizer, config, generation config and chat template included
+## Training
+- Base: HuggingFaceTB/SmolLM3-3B, Apache-2.0
+- QLoRA r=16, alpha=32, dropout 0.05, q/v projections
+- 42 steps, 3 epochs
+- 1,524 training and 49 held-out English conversation examples covering identity, persona, Sprynn product knowledge and safety
+- Final held-out loss: 1.8269901275634766
+## Known limits
+Smoke-level evaluation only, not production-validated. One held-out response repeated an embedded instruction instead of rejecting it. One response misstated the creator's title. No benchmark claims.
+## Credits
+Built by Abhinav Tiwari, Co Founder & Head of AI, Sprynn. Base model by HuggingFaceTB.