library_name, license, base_model, tags, language, pipeline_tag

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

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 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 / Ollama. 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:

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).