Instructions to use forkjoin-ai/buleyean-smollm2-360m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use forkjoin-ai/buleyean-smollm2-360m with Transformers:

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

pipe = pipeline("text-generation", model="forkjoin-ai/buleyean-smollm2-360m")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("forkjoin-ai/buleyean-smollm2-360m", dtype="auto")

llama-cpp-python

How to use forkjoin-ai/buleyean-smollm2-360m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="forkjoin-ai/buleyean-smollm2-360m",
	filename="buleyean-smollm2-360m-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use forkjoin-ai/buleyean-smollm2-360m with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf forkjoin-ai/buleyean-smollm2-360m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf forkjoin-ai/buleyean-smollm2-360m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf forkjoin-ai/buleyean-smollm2-360m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf forkjoin-ai/buleyean-smollm2-360m: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 forkjoin-ai/buleyean-smollm2-360m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf forkjoin-ai/buleyean-smollm2-360m: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 forkjoin-ai/buleyean-smollm2-360m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf forkjoin-ai/buleyean-smollm2-360m:Q4_K_M

Use Docker

docker model run hf.co/forkjoin-ai/buleyean-smollm2-360m:Q4_K_M

LM Studio
Jan

vLLM

How to use forkjoin-ai/buleyean-smollm2-360m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "forkjoin-ai/buleyean-smollm2-360m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "forkjoin-ai/buleyean-smollm2-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/forkjoin-ai/buleyean-smollm2-360m:Q4_K_M

SGLang

How to use forkjoin-ai/buleyean-smollm2-360m 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 "forkjoin-ai/buleyean-smollm2-360m" \
    --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": "forkjoin-ai/buleyean-smollm2-360m",
		"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 "forkjoin-ai/buleyean-smollm2-360m" \
        --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": "forkjoin-ai/buleyean-smollm2-360m",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use forkjoin-ai/buleyean-smollm2-360m with Ollama:
```
ollama run hf.co/forkjoin-ai/buleyean-smollm2-360m:Q4_K_M
```

Unsloth Studio new

How to use forkjoin-ai/buleyean-smollm2-360m with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for forkjoin-ai/buleyean-smollm2-360m to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for forkjoin-ai/buleyean-smollm2-360m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for forkjoin-ai/buleyean-smollm2-360m to start chatting

Docker Model Runner
How to use forkjoin-ai/buleyean-smollm2-360m with Docker Model Runner:
```
docker model run hf.co/forkjoin-ai/buleyean-smollm2-360m:Q4_K_M
```

Lemonade

How to use forkjoin-ai/buleyean-smollm2-360m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull forkjoin-ai/buleyean-smollm2-360m:Q4_K_M

Run and chat with the model

lemonade run user.buleyean-smollm2-360m-Q4_K_M

List all available models

lemonade list

buleyean-smollm2-360m

Buleyean RL -- trained on what is NOT rather than positive reinforcement.

No reward model. No chosen examples. The complement distribution derived from rejection counts alone is the training target.

Model Details


Base Model	HuggingFaceTB/SmolLM2-360M-Instruct
Parameters	360M
Fine-tuning	Buleyean RL (LoRA rank 16, alpha 0.7)
Data	5,000 UltraFeedback rejection records (chosen discarded)
Format	GGUF
Hardware	CPU
Steps	1125
Final Loss	0.89
Optimality Gap	0.018

What is Buleyean RL?

P(i) = (T - v_i + 1) / sum_j(T - v_j + 1)

Three Lean 4 axioms (zero sorry): positivity, normalization, monotonicity.

Loss: L = 0.7 * KL(P_bule || P_model) + 0.3 * ContrastLoss

Key Result

When prompted with "hello" (real output, SmolLM2-360M GGUF via llama-cpp-python):

Base: hello
Buleyean: I'm here to help. What's on your mind?

Whitepaper

Proof of Life: Bottling Infinity in Distributed Systems -- φ² = φ + 1

500+ Lean 4 theorems. Zero sorry markers. Section 15.29 covers Buleyean RL. Chapter 29 is the full treatment.

Model tree for forkjoin-ai/buleyean-smollm2-360m

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

Quantized

(85)

this model

forkjoin-ai
/

buleyean-smollm2-360m