Instructions to use Monster-Code/Boomslang with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Monster-Code/Boomslang with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Monster-Code/Boomslang")
model = AutoModelForCausalLM.from_pretrained("Monster-Code/Boomslang", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Monster-Code/Boomslang 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 Monster-Code/Boomslang
# Run inference directly in the terminal:
llama cli -hf Monster-Code/Boomslang

Install from WinGet (Windows)

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

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 Monster-Code/Boomslang
# Run inference directly in the terminal:
./llama-cli -hf Monster-Code/Boomslang

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 Monster-Code/Boomslang
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Monster-Code/Boomslang

Use Docker

docker model run hf.co/Monster-Code/Boomslang

LM Studio
Jan

vLLM

How to use Monster-Code/Boomslang with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Monster-Code/Boomslang

SGLang

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

Ollama
How to use Monster-Code/Boomslang with Ollama:
```
ollama run hf.co/Monster-Code/Boomslang
```
Unsloth Desktop

How to use Monster-Code/Boomslang with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Monster-Code/Boomslang

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": "Monster-Code/Boomslang"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Monster-Code/Boomslang with Docker Model Runner:
```
docker model run hf.co/Monster-Code/Boomslang
```

Lemonade

How to use Monster-Code/Boomslang with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Monster-Code/Boomslang

Run and chat with the model

lemonade run user.Boomslang-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Monster-Code/Boomslang with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Monster-Code/Boomslang

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 Monster-Code/Boomslang

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Monster-Code/Boomslang with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Monster-Code/Boomslang

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 "Monster-Code/Boomslang" \
  --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"

🐍 Boomslang (3B Reasoning & Math Engine)

A compact, high-efficiency 3-billion-parameter model fine-tuned for deep chain-of-thought mathematical reasoning, logic, and algebra—without sacrificing natural conversational ability.

❤️ If you find Boomslang useful, please hit the Like button at the top of this page and Follow @Monster-Code for more open-weights AI releases!

💡 What is Boomslang?

Most small models (1B–3B parameters) struggle with two extremes: they are either polite chatbots that completely hallucinate basic arithmetic, or narrow math models that forget how to hold a conversation and start writing unprompted proofs when you simply say "Hi."

Boomslang was trained to bridge that gap.

Starting from the strong foundation of Qwen/Qwen2.5-3B-Instruct, Boomslang was post-trained on an NVIDIA RTX PRO 6000 Blackwell across a curated ~17,500-sample reasoning mixture:

DeepSeek-R1 Distilled Proofs (open-r1/OpenR1-Math-220k): Teaches the network an internal self-reflection loop (<think> ... </think>) to break down complex algebraic expressions, geometry, and multi-step deduction before committing to an answer.
Step-by-Step Arithmetic Rigor (openai/gsm8k): Calibrates attention heads on strict order-of-operations arithmetic and unambiguous answer derivation.

The result is an edge-friendly 3B model that works through tricky algebra and word puzzles methodically, but still greets you warmly and follows instructions when you just want to talk.

📦 What's Inside This Repository?

Single Standalone model.safetensors: No multi-part file splits. The entire 3-billion parameter model is packed into a single, clean ~6.1 GB file.
Pre-Converted GGUF (boomslang-3b-qwen.gguf): Directly ready for Ollama, LM Studio, and llama.cpp on your local machine (MacBook, laptop, or home GPU).
Full configuration and tokenizer files for immediate transformers plug-and-play.

⚡ Quickstart: Python & Transformers

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

MODEL_ID = "Monster-Code/Boomslang"

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
    device_map="auto"
)

# Standard ChatML format
messages = [
    {
        "role": "system",
        "content": "You are Boomslang, a helpful and precise reasoning assistant. Reason step-by-step before providing your final answer."
    },
    {
        "role": "user",
        "content": "A box has 36 chocolates. Tom eats 1/4 of them, and Sarah eats 9. How many chocolates are left in the box?"
    }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

with torch.no_grad():
    model.generate(
        **inputs,
        streamer=streamer,
        max_new_tokens=1024,
        temperature=0.6,
        top_p=0.9
    )

💻 Running Locally with Ollama / LM Studio

1. Using Ollama:

Create a Modelfile in the same directory as the downloaded .gguf:

FROM ./boomslang-3b-qwen.gguf

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.6

Build and run:

ollama create boomslang -f Modelfile
ollama run boomslang

2. Using LM Studio:

Download boomslang-3b-qwen.gguf from the Files and versions tab.
Drop it into your local LM Studio models folder.
Start chatting with GPU offloading enabled!

🧠 Sample Interaction

User:

"Solve for x: 5x - 10 = 25. Show your work."

Boomslang:

<think>
The equation is 5x - 10 = 25.
Our goal is to isolate the variable x on one side.

Step 1: Add 10 to both sides to cancel out the -10 constant:
5x - 10 + 10 = 25 + 10
5x = 35

Step 2: Divide both sides by the coefficient 5:
5x / 5 = 35 / 5
x = 7

Verification:
Substitute x = 7 into the original expression:
5(7) - 10 = 35 - 10 = 25.
Both sides match.
</think>

The answer is x = 7.

🛠️ Training Details

Base Architecture: Qwen2.5 3B (Decoder-only Transformer)
Hardware: NVIDIA RTX PRO 6000 Blackwell Server Edition
Precision: BF16 Native Mixed Precision with Fused AdamW
Effective Batch Size: 32 (8 per device × 4 gradient accumulation steps)
Learning Rate: 1.5e-4 with dynamic linear warmup
Label Masking: Dynamic batch padding via DataCollatorForSeq2Seq with -100 masking to guarantee loss is never calculated on padding noise

🤝 Community & Support

👤 Creator: Monster-Code
💬 Have suggestions, evaluation runs, or dataset ideas? Leave a note in the Discussions tab!
⭐ If Boomslang helps your workflow, please consider starring/liking the repository!

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for Monster-Code/Boomslang

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(289)

this model