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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,170 @@
+---
+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},
+}
+```

chat_template.jinja

@@ -0,0 +1,154 @@
+{%- set image_count = namespace(value=0) %}
+{%- set video_count = namespace(value=0) %}
+{%- macro render_content(content, do_vision_count, is_system_content=false) %}
+    {%- if content is string %}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping %}
+        {%- for item in content %}
+            {%- if 'image' in item or 'image_url' in item or item.type == 'image' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain images.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set image_count.value = image_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Picture ' ~ image_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|image_pad|><|vision_end|>' }}
+            {%- elif 'video' in item or item.type == 'video' %}
+                {%- if is_system_content %}
+                    {{- raise_exception('System message cannot contain videos.') }}
+                {%- endif %}
+                {%- if do_vision_count %}
+                    {%- set video_count.value = video_count.value + 1 %}
+                {%- endif %}
+                {%- if add_vision_id %}
+                    {{- 'Video ' ~ video_count.value ~ ': ' }}
+                {%- endif %}
+                {{- '<|vision_start|><|video_pad|><|vision_end|>' }}
+            {%- elif 'text' in item %}
+                {{- item.text }}
+            {%- else %}
+                {{- raise_exception('Unexpected item type in content.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- elif content is none or content is undefined %}
+        {{- '' }}
+    {%- else %}
+        {{- raise_exception('Unexpected content type.') }}
+    {%- endif %}
+{%- endmacro %}
+{%- if not messages %}
+    {{- raise_exception('No messages provided.') }}
+{%- endif %}
+{%- if tools and tools is iterable and tools is not mapping %}
+    {{- '<|im_start|>system\n' }}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {%- if content %}
+            {{- '\n\n' + content }}
+        {%- endif %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- set content = render_content(messages[0].content, false, true)|trim %}
+        {{- '<|im_start|>system\n' + content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" %}
+        {%- set content = render_content(message.content, false)|trim %}
+        {%- if not(content.startswith('<tool_response>') and content.endswith('</tool_response>')) %}
+            {%- set ns.multi_step_tool = false %}
+            {%- set ns.last_query_index = index %}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if ns.multi_step_tool %}
+    {{- raise_exception('No user query found in messages.') }}
+{%- endif %}
+{%- for message in messages %}
+    {%- set content = render_content(message.content, true)|trim %}
+    {%- if message.role == "system" %}
+        {%- if not loop.first %}
+            {{- raise_exception('System message must be at the beginning.') }}
+        {%- endif %}
+    {%- elif message.role == "user" %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- set reasoning_content = reasoning_content|trim %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content + '\n</think>\n\n' + content }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls and message.tool_calls is iterable and message.tool_calls is not mapping %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if tool_call.function is defined %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {%- if loop.first %}
+                    {%- if content|trim %}
+                        {{- '\n\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- else %}
+                        {{- '<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                    {%- endif %}
+                {%- else %}
+                    {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+                {%- endif %}
+                {%- if tool_call.arguments is defined %}
+                    {%- for args_name, args_value in tool_call.arguments|items %}
+                        {{- '<parameter=' + args_name + '>\n' }}
+                        {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                        {{- args_value }}
+                        {{- '\n</parameter>\n' }}
+                    {%- endfor %}
+                {%- endif %}
+                {{- '</function>\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- raise_exception('Unexpected message role.') }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- else %}
+        {{- '<think>\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,83 @@
+{
+  "architectures": [
+    "Qwen3_5ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_output_gate": true,
+  "bos_token_id": null,
+  "dtype": "bfloat16",
+  "eos_token_id": 248044,
+  "full_attention_interval": 4,
+  "head_dim": 256,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention",
+    "linear_attention",
+    "linear_attention",
+    "linear_attention",
+    "full_attention"
+  ],
+  "linear_conv_kernel_dim": 4,
+  "linear_key_head_dim": 128,
+  "linear_num_key_heads": 16,
+  "linear_num_value_heads": 32,
+  "linear_value_head_dim": 128,
+  "mamba_ssm_dtype": "float32",
+  "max_position_embeddings": 262144,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_5_text",
+  "mtp_num_hidden_layers": 1,
+  "mtp_use_dedicated_embeddings": false,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 4,
+  "pad_token_id": null,
+  "partial_rotary_factor": 0.25,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "mrope_interleaved": true,
+    "mrope_section": [
+      11,
+      11,
+      10
+    ],
+    "partial_rotary_factor": 0.25,
+    "rope_theta": 10000000,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.13.1",
+  "use_cache": true,
+  "vocab_size": 248320
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": 248044,
+  "transformers_version": "5.13.1",
+  "use_cache": true
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c53a0ec77d6e0508dabf06b05837a5afb1b936f270755917185a278028ea26d9
+size 17907663008

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
+size 19989325

tokenizer_config.json

@@ -0,0 +1,32 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "image_token": "<|image_pad|>",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 262144,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "pretokenize_regex": "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?[\\p{L}\\p{M}]+|\\p{N}| ?[^\\s\\p{L}\\p{M}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}