Instructions to use h4bbo/FuseLLM-112M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h4bbo/FuseLLM-112M with Transformers:

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

pipe = pipeline("text-generation", model="h4bbo/FuseLLM-112M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("h4bbo/FuseLLM-112M")
model = AutoModelForCausalLM.from_pretrained("h4bbo/FuseLLM-112M")
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]:]))

llama-cpp-python

How to use h4bbo/FuseLLM-112M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="h4bbo/FuseLLM-112M",
	filename="FuseLLM-112M.bf16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use h4bbo/FuseLLM-112M 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 h4bbo/FuseLLM-112M:BF16
# Run inference directly in the terminal:
llama cli -hf h4bbo/FuseLLM-112M:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h4bbo/FuseLLM-112M:BF16
# Run inference directly in the terminal:
llama cli -hf h4bbo/FuseLLM-112M:BF16

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 h4bbo/FuseLLM-112M:BF16
# Run inference directly in the terminal:
./llama-cli -hf h4bbo/FuseLLM-112M:BF16

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 h4bbo/FuseLLM-112M:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h4bbo/FuseLLM-112M:BF16

Use Docker

docker model run hf.co/h4bbo/FuseLLM-112M:BF16

LM Studio
Jan

vLLM

How to use h4bbo/FuseLLM-112M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h4bbo/FuseLLM-112M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h4bbo/FuseLLM-112M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/h4bbo/FuseLLM-112M:BF16

SGLang

How to use h4bbo/FuseLLM-112M 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 "h4bbo/FuseLLM-112M" \
    --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": "h4bbo/FuseLLM-112M",
		"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 "h4bbo/FuseLLM-112M" \
        --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": "h4bbo/FuseLLM-112M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use h4bbo/FuseLLM-112M with Ollama:
```
ollama run hf.co/h4bbo/FuseLLM-112M:BF16
```

Unsloth Studio

How to use h4bbo/FuseLLM-112M 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 h4bbo/FuseLLM-112M 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 h4bbo/FuseLLM-112M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for h4bbo/FuseLLM-112M to start chatting

How to use h4bbo/FuseLLM-112M with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h4bbo/FuseLLM-112M:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "h4bbo/FuseLLM-112M:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use h4bbo/FuseLLM-112M with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h4bbo/FuseLLM-112M:BF16

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 h4bbo/FuseLLM-112M:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use h4bbo/FuseLLM-112M with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf h4bbo/FuseLLM-112M:BF16

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 "h4bbo/FuseLLM-112M:BF16" \
  --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"

Docker Model Runner
How to use h4bbo/FuseLLM-112M with Docker Model Runner:
```
docker model run hf.co/h4bbo/FuseLLM-112M:BF16
```

Lemonade

How to use h4bbo/FuseLLM-112M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h4bbo/FuseLLM-112M:BF16

Run and chat with the model

lemonade run user.FuseLLM-112M-BF16

List all available models

lemonade list

FuseLLM-112M / README.md

h4bbo

README: document the non-quantized bf16 GGUF variant

9b01f61 verified about 24 hours ago

preview code

Raw

History Blame Contribute Delete

6.45 kB

	---
	library_name: transformers
	license: apache-2.0
	base_model: h4bbo/FuseLLM-112M-Completion
	tags:
	- habbo
	- code
	- chatml
	- sft
	language:
	- en
	pipeline_tag: text-generation
	---

	# FuseLLM-112M (Chat)

	This is the ChatML chat variant of FuseLLM-112M — a 112M-parameter, from-scratch
	Qwen3-architecture decoder-only model supervised-fine-tuned (SFT) on on-domain Habbo instruction
	pairs derived from the Habbo source corpus itself (no external/teacher data).

	The base model, [h4bbo/FuseLLM-112M-Completion](https://huggingface.co/h4bbo/FuseLLM-112M-Completion),
	was trained only on raw Habbo code in completion mode. Its tokenizer already shipped a Qwen3
	ChatML template, but the weights had never seen a chat turn, so chat-formatted prompts produced
	poor, repetitive output. This checkpoint teaches the weights to follow ChatML turns and to **emit
	`<\|im_end\|>`** at the end of an assistant answer (the turn terminator — conveniently the same token,
	151645, the base model was trained to use as a document boundary), which is what stops the runaway
	repetition.

	## Use

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "h4bbo/FuseLLM-112M"
	tok = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="sdpa")

	messages = [
	{"role": "system", "content": "You are a Habbo Hotel emulator code assistant. Reply with concise, correct code or a brief explanation."},
	{"role": "user", "content": "Implement this Java method:\n```java\npublic static void sendRoomPacket(Session s, int header) { }\n```"},
	]
	text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	ids = tok(text, return_tensors="pt").to(model.device)
	out = model.generate(**ids, max_new_tokens=256, eos_token_id=151645, pad_token_id=151643,
	do_sample=True, temperature=0.7, top_p=0.9, repetition_penalty=1.05)
	print(tok.decode(out[0][ids["input_ids"].shape[1]:], skip_special_tokens=True))
	```

	It is designed for Habbo-coding instructions (method/class completion, code continuation,
	doc→code). It is not a general assistant — outside its narrow domain it will produce poor or
	repetitive output.

	## Architecture

	`Qwen3ForCausalLM`, hidden 512, 8 layers (full attention), 8 heads / 8 KV heads, intermediate
	1408, vocab 151,936, max context 2048, `tie_word_embeddings=true`. Trained in bf16.

	## Training data

	10,000 ChatML conversations derived deterministically from the Habbo corpus (the same corpus the
	base was trained on — 84,925 unique files, ~210M est. tokens; top languages Java 26,335 /
	C# 19,048 / PHP 10,969 / ActionScript 3,801). No external data, no teacher model. Templates:

	- Method completion — given a method signature with empty body, return the real body.
	- Code continuation — given a file prefix, return the real suffix.
	- Doc → method — given a Javadoc/PHPDoc/`///` summary, return the real method.

	Distribution in this build: Java ~4,936 · C# ~3,752 · PHP ~989 · ActionScript ~323. Decompiler
	noise (JD-Core `/* N:M /` line markers and `/ Location: … */` footers) is stripped. Secrets are
	scrubbed to `[REDACTED]` (currently `xX!elgps`) before extraction; the training file is verified to
	contain 0 secret occurrences.

	## Training config

	- Full fine-tune (no LoRA — 112M is small enough to train every weight on 24 GB).
	- TRL `SFTTrainer` + `SFTConfig`, `messages` format auto-detected, ChatML applied by the base
	tokenizer's chat template. `packing=False`, `max_length=2048` (model's native max position; only
	11/10,000 conversations exceed it). Full-sequence causal-LM loss (the Qwen3 chat template has no
	`{% generation %}` markers, so `assistant_only_loss` is unset — at inference we prompt through
	`<\|im_start\|>assistant\n` and stop at `<\|im_end\|>`).
	- bf16, sdpa attention, `adamw_torch`, cosine schedule, 3 epochs, LR 2e-5, warmup 0.03,
	effective batch 16 (BS 4 × GA 4), `max_grad_norm=1.0`, seed 42. 1,875 steps.
	- `generation_config.json` is written with `eos_token_id=151645` (`<\|im_end\|>`),
	`pad_token_id=151643`, `temperature=0.7`, `top_p=0.9`, `repetition_penalty=1.05`.

	## Training result

	Loss dropped from ~1.83 (step 10) to ~0.45 by the end of epoch 1 and held in the ~0.4–0.5 range
	through epochs 2–3. Final: `train_loss 0.521`, `mean_token_accuracy 0.9144`, 1,875 steps, ~16.5 min
	on a single RX 7900 XTX (ROCm). Verified behavior: method-completion and code-continuation prompts
	produce coherent on-domain Habbo code, close the fenced block, and stop at `<\|im_end\|>`;
	doc→method and free-form explanation prompts tend to ramble (see Limitations).

	## Redaction

	Secrets (currently `xX!elgps`) are scrubbed to `[REDACTED]` in all training content before
	tokenisation. The output training file is checked to contain 0 occurrences. No known credentials
	enter the weights.

	## License

	Released under Apache-2.0. See the base model
	[h4bbo/FuseLLM-112M-Completion](https://huggingface.co/h4bbo/FuseLLM-112M-Completion) for its
	license terms.

	## GGUF

	A non-quantized bf16 GGUF — `FuseLLM-112M.bf16.gguf` (~220 MB, a bit-exact copy of the bf16
	safetensors weights, so truly lossless) — is included in this repo for use with
	[llama.cpp](https://github.com/ggml-org/llama.cpp) / [Ollama](https://ollama.com). The ChatML chat
	template and the EOS token (`<\|im_end\|>`, 151645) are embedded as GGUF metadata, so the model loads
	in chat mode automatically. No quantized (Q4/Q5/Q8) variant is shipped here.

	Example with llama.cpp:

	```bash
	llama-cli -m FuseLLM-112M.bf16.gguf -cnv \
	--temp 0.7 --top-p 0.9 --repeat-penalty 1.05 -n 256 \
	-p "Implement this Java method:\n```java\npublic static void sendRoomPacket(Session s, int h) { }\n```"
	```

	## Intended use

	Domain-specialist code assistant for the Habbo Hotel emulator ecosystem (server/client tooling).
	Not affiliated with or endorsed by Sulake/Habbo.

	## Limitations

	- 112M parameters — narrow capacity; expect errors and repetition on long or off-domain prompts.
	- Trained only on on-domain code-instruction pairs; not a general chat / instruction model.
	- Doc→method and free-form explanation prompts often ramble past `<\|im_end\|>` despite
	`repetition_penalty`; keep `max_new_tokens` modest and prefer the SFT templates
	(method completion / code continuation).