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Model: h4bbo/FuseLLM-112M
Source: Original Platform

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+FuseLLM-112M.bf16.gguf filter=lfs diff=lfs merge=lfs -text

FuseLLM-112M.bf16.gguf

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README.md

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+---
+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).

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 1408,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 2048,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 8,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": 151645,
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.9,
+  "transformers_version": "5.13.0"
+}

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tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 1010000,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}