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h4bbo
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Model Details
Model Provider
h4bbo
Model Tree
h4bbo/FuseLLM-112M-Completion
Input Modalities
Output Modalities
Supported Functionality
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchmodel_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.
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.
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:
/// 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.
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|>).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.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).
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.
Released under Apache-2.0. See the base model h4bbo/FuseLLM-112M-Completion for its license terms.
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:
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```"
Domain-specialist code assistant for the Habbo Hotel emulator ecosystem (server/client tooling). Not affiliated with or endorsed by Sulake/Habbo.
<|im_end|> despite
repetition_penalty; keep max_new_tokens modest and prefer the SFT templates
(method completion / code continuation).generation_config.jsoneos_token_id=151645<|im_end|>pad_token_id=151643temperature=0.7top_p=0.9repetition_penalty=1.05