README.md

---
library_name: transformers
base_model: Qwen/Qwen3.5-9B
tags:
  - code
  - lora
  - cuda
  - habbo
  - game-server-emulation
  - flash
  - shockwave
  - continued-pretraining
  - qwen3.5
  - hybrid-attention
  - gated-deltanet
language:
  - en
license: apache-2.0
inference: false
pipeline_tag: text-generation
---

# FuseLLM-9B

A domain-specialist code model for the **Habbo Hotel ecosystem** — server emulators (Java, C#, PHP, Rust) and Flash/Shockwave client tooling (ActionScript, LiveScript, SWF/DIR reverse engineering). Built by continued pretraining of **Qwen3.5-9B**'s language model on a curated corpus of ~85K source files (~211M tokens) drawn from Habbo emulator projects, decompiled client code, and CMS/database dumps.

The base `Qwen/Qwen3.5-9B` is a multimodal Qwen3.5 model (vision + text). This fine-tune targets and ships only its **text language model** (`Qwen3_5ForCausalLM`, 8.95B params) — the vision tower is not included in the released weights.

---

## tl;dr

- **What it is:** an ~9B-parameter model, LoRA-continued-pretrained on Habbo emulator + retro-client source code, merged into a standalone bf16 text causal LM.
- **Base:** `Qwen/Qwen3.5-9B` — Apache-2.0. Hybrid Qwen3.5 architecture: 24 Gated-DeltaNet (linear-attention) layers + 8 full-attention layers (1 full every 4), 32 layers total, hidden 4096, vocab 248320.
- **Trained on:** a single NVIDIA H100 80GB, CUDA 12.6, PyTorch 2.13.0+cu126. bf16 LoRA, no quantization, gradient checkpointing on. Used the `flash-linear-attention` (fla) fast path + `causal-conv1d` for the DeltaNet / short-conv layers.
- **Runs on:** a 9B model needs more than the 4B sibling but is still very consumer-friendly. A Q4_K_M GGUF is ~5 GB — fits in 8 GB VRAM (short context), comfortable at 12 GB. The full bf16 merged weights (~18 GB) need 24 GB. If your card has ≥8 GB and was made in the last ~6 years, it can run FuseLLM-9B at some quantization.

---

## Inference — yes, your GPU can run this

The point of shipping a ~9B model is strong domain capacity while still running on consumer hardware. The training rig was an 80 GB H100; **inference needs far less.**

| Path | Size | Min. VRAM (comfortable) | Notes |
|---|---|---|---|
| **GGUF Q4_K_M (recommended)** | ~5.0 GB | 12 GB (runs on 8 GB, short ctx) | Via Ollama / llama.cpp / LM Studio / KoboldCpp. CPU-only also works — slow but functional. |
| GGUF Q8_0 | ~9.5 GB | 16 GB (12 GB short ctx) | Near-lossless. |
| bf16 merged (full precision) | ~18 GB + KV cache | 24 GB | The "no quantization" path. 40 GB+ leaves room for a long context. |

Concrete examples of cards that run it fine:

- **8 GB:** RTX 3060 / 4060 / 5060, RX 6600 / 7600, Arc A580 — Q4_K_M with short context.
- **12 GB:** RTX 3060 12GB / 4070 / 5070, RX 6700 XT / 7700 XT — Q4_K_M with long context, or Q8_0 short context.
- **16 GB:** RTX 4060 Ti 16GB / 4080 / 5070 Ti, RX 7800 XT / 9070 — Q8_0 comfortably, or bf16 short context.
- **24 GB:** RTX 3090 / 4090 / 5090, RX 7900 XTX/XT — bf16 merged with a healthy context, or Q4 with room to spare.

Linux, Windows, macOS (Metal) all supported through llama.cpp / Ollama. **AMD, NVIDIA, and Intel are all first-class** — the GGUF backend is vendor-agnostic.

### Quick start (Ollama)

```bash
ollama run h4bbo/fusellm-9b          # once uploaded
# or load the local GGUF:
ollama create fusellm-9b -f Modelfile  # FROM ./fusellm-9b-Q4_K_M.gguf
ollama run fusellm-9b
```

### Quick start (transformers, bf16)

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

tok = AutoTokenizer.from_pretrained("h4bbo/FuseLLM-9B")
model = AutoModelForCausalLM.from_pretrained(
    "h4bbo/FuseLLM-9B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
```

---

## Training details

### Hardware

| | |
|---|---|
| GPU | NVIDIA H100 80GB HBM3 |
| Stack | CUDA 12.6, PyTorch 2.13.0+cu126 |
| Fast path | `flash-linear-attention` 0.5.2 + `causal-conv1d` 1.6.2 (fla kernels for the Gated-DeltaNet layers; `causal-conv1d` for the short conv). `attn_implementation="sdpa"` — FlashAttention-2 does not cover the DeltaNet layers. |

A single 80 GB datacenter-class card. With gradient checkpointing and bf16 LoRA the full 9B fits without quantization. (The 4B sibling of this model was trained on a single 24 GB consumer AMD card via ROCm — see `h4bbo/FuseLLM-Instruct-4B-v1`.)

### Base model

`Qwen/Qwen3.5-9B` — Apache-2.0. Qwen3.5 hybrid architecture: 32 layers = 24 Gated-DeltaNet (linear attention) + 8 full attention (one full layer every four), hidden 4096, 16 attention heads, 4 KV heads (full-attn), head_dim 256, vocab 248320, max_position_embeddings 262144. The released weights are the text LM only (`Qwen3_5ForCausalLM`, 8.95B params, `tie_word_embeddings=False`); the base's vision tower is not part of this release.

### Method — bf16 LoRA continued pretraining

Raw-code continued pretraining (full-sequence causal-LM loss, no instruction pairs).

- **LoRA:** r=32, alpha=64, dropout=0.05, applied to all Qwen3.5 hybrid projections — full attention (`q/k/v/o_proj`, 8/32 layers), Gated-DeltaNet (`in_proj_qkv`, `in_proj_z`, `in_proj_b`, `in_proj_a`, `out_proj`, 24/32 layers), and FFN (`gate/up/down_proj`, all layers). `task_type=CAUSAL_LM`.
- **Optimizer/schedule:** `adamw_torch`, lr=1e-4, cosine, warmup_ratio=0.03, max_grad_norm=1.0, 1 epoch.
- **Batching:** per-device batch 4 × grad-accum 8 = effective batch 32, `max_length=2048` with `packing_strategy="wrapped"`, 4254 steps, ~15.5 h.
- **Precision/memory:** `bf16=True`, `gradient_checkpointing=True` (`use_reentrant=False`), `attn_implementation="sdpa"`, `optim="adamw_torch"` (no bitsandbytes). `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`.
- **Loss:** full-sequence causal-LM on packed raw code (correct for continued pretraining — no instruction/response pairs to mask).
- **Merge:** `merge_and_unload(safe_merge=True)` into the bf16 base → standalone safetensors. `save_peft_format=False` (critical — otherwise the adapter re-attaches on reload).

### Training result

| | |
|---|---|
| Steps | 4,254 (1 epoch) |
| Wall time | ~15.5 h |
| Train loss (final) | 0.4082 |
| Loss curve | 0.7336 (step 10) → 0.3318 (final) |
| Mean token accuracy | 0.8393 (step 10) → 0.9167 (final) |
| Tokens seen | ~2.79e8 |

### Training data

| | |
|---|---|
| Files | 84,925 unique (sha256-deduped; 206,707 duplicates removed; 2.07M minified files skipped) |
| Size | 803.2 MB |
| Tokens | ~211M (est. chars/4) |
| Format | `{"text": <redacted file content>}` — TRL `dataset_text_field="text"` |

By language (files): Java 26,335 · C# 19,048 · PHP 10,969 · LiveScript 7,457 · ActionScript 3,801 · Python 3,023 · JavaScript 2,646 · XML 2,569 · HTML 2,023 · CSS 1,470 · Rust 1,442 · C 1,103 · TypeScript 644 · C++ 517 · SQL 494 · VB 362 · JSON 351 · + Markdown/Gradle/YAML/Scala/Lua.

Sources: 123 Quackster Habbo emulator/tooling repos (incl. private: HorusClient, Kurkku, Aleeda, Icarus variants, cappo-emu, …), `ntuative/RELEASE63…`, `deklol/Shockless`, plus deeply-nested Beta-archive extractions (Debbo, BloodLine, Chocohotel, uberEmu, etc.) — 497 archives / 30 GB unpacked. `.sql` DB dumps (148 MB) are included for now and may be dropped in a later revision.

---

## Intended use & limitations

**Intended:** code completion / Q&A for Habbo server-emulator and retro-client development — packet handling, room/item state, CMS schemas, SWF/DIR reverse engineering, Shockwave Lingo, ActionScript 3 client internals.

**Not intended:** general-purpose chat, math, or non-Habbo code generation. This is a *continued-pretraining* of an instruct model on domain code; it is not a general assistant and was not aligned for safety/RLHF beyond what the base model already had. (Note: because the base here is an **instruct** model and the fine-tune is raw-code continued pretraining with no chat formatting, some of the base's chat alignment is expected to erode — this is a domain code model, not a conversational one.)

**Limitations:**
- ~9B parameters — strong on domain pattern-completion; weaker than larger models on multi-file reasoning.

---

## License & data provenance

- **Base model:** `Qwen/Qwen3.5-9B` — **Apache-2.0**. Fine-tuning and redistribution permitted with attribution. ✅
- **This fine-tune (weights):** released under **Apache-2.0** *conditional on the data licensing below*. The LoRA adapter is small and derivative; the merged model inherits both base and data obligations.
- **Training data:** mixed provenance —
  - Author's own repos (fine).
  - **GPL / various third-party emulators** (PHPRetro/Yifan Lu, uberEmu/Meth0d, Holograph, Icarus, etc.) — GPL-derivative debate applies; a model trained on GPL source is arguably a derivative work.

## Redaction

Secrets are scrubbed from all training content before tokenisation (replaced with `[REDACTED]`). No known credentials enter the weights. The released weights and tokenizer/config files were scanned for tokens, API keys, IPs, hostnames, and absolute paths before upload — none found.

## Citation

If this model is useful, cite the base and this fine-tune:

```bibtex
@misc{fusellm-9b,
  title  = {FuseLLM-9B: a Habbo ecosystem code model},
  note   = {bf16 LoRA continued pretraining of Qwen3.5-9B's text LM},
  year   = {2026},
}
```