初始化项目，由ModelHub XC社区提供模型

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

README.md

@@ -0,0 +1,100 @@
+---
+library_name: transformers
+license: other  # TODO: set the license you want to release this model under
+language:
+  - code
+tags:
+  - qwen3
+  - causal-lm
+  - code-completion
+  - habbo
+  - from-scratch
+---
+
+# FuseLLM-112M
+
+A small **112M-parameter decoder-only language model trained from scratch** (no base
+checkpoint, no LoRA) on a corpus of Habbo emulator / game-server source code. The
+goal is a tiny, fast model for **code completion** in that Java codebase, not a
+general-purpose or instruction-following model.
+
+## Model details
+
+| | |
+|---|---|
+| Architecture | Qwen3 (decoder-only causal LM) |
+| Parameters | ~112M (tied input/output embeddings) |
+| Hidden size | 512 |
+| Layers | 8 (all full attention) |
+| Attention heads | 8 (8 KV heads) |
+| Vocab size | 151,936 |
+| Max context | 2048 |
+| Precision | float32 (safetensors) |
+| Training | From scratch, 4 epochs, 16,188 steps |
+| Final train loss | ~0.58 |
+
+`tie_word_embeddings: true` — the output `lm_head` shares the input embedding
+matrix, so checkpoints store only one copy. This is expected, not a missing weight.
+
+## Intended use
+
+- **Code completion** for Habbo-style Java server code (raw prompt → continuation).
+- Local experimentation / distillation base.
+
+## What it is NOT
+
+- **Not instruction-tuned / not a chat model.** It was trained only on raw source
+  code, never on chat/instruction data.
+- The Qwen3 ChatML chat template is included (it ships with the tokenizer) for
+  tokenizer/tool compatibility, but the model has **not** learned to follow chat
+  turns. Passing chat-formatted prompts will produce poor, often repetitive output.
+  Use it in **completion mode**, not conversation mode.
+
+## Usage
+
+### transformers (recommended for completion)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+m   = AutoModelForCausalLM.from_pretrained("h4bbo/FuseLLM-112M")
+tok = AutoTokenizer.from_pretrained("h4bbo/FuseLLM-112M")
+
+prompt = "public class Room {\n  public void onEnter(Player p) {\n    "
+ids = tok(prompt, return_tensors="pt").input_ids
+out = m.generate(ids, max_new_tokens=64, do_sample=False,
+                 repetition_penalty=1.1, pad_token_id=tok.eos_token_id)
+print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
+```
+
+### llama.cpp (completion mode)
+
+No GGUF is shipped in this repo. The HF model is **verified** to convert and run in
+`llama.cpp`; generate the GGUF locally:
+
+```bash
+# 1) convert HF -> lossless fp16 GGUF
+python convert_hf_to_gguf.py h4bbo/FuseLLM-112M --outtype f16 \
+  --model-name FuseLLM-112M --outfile FuseLLM-112M.fp16.gguf
+# (optional) 4-bit quantize
+llama-quantize FuseLLM-112M.fp16.gguf FuseLLM-112M.Q4_K_M.gguf Q4_K_M
+
+# 2) completion mode — pass the raw code seed, do NOT use chat/conversation mode.
+llama-cli -m FuseLLM-112M.Q4_K_M.gguf -cnv -st --no-jinja \
+  -f seed.txt -n 64 --temp 0.0 --repeat-penalty 1.1 --no-display-prompt < /dev/null
+```
+
+`--no-jinja` keeps the prompt raw (the embedded chat template exists but the model
+isn't chat-tuned, so conversation mode is not meaningful for this model).
+
+## Files
+
+- `model.safetensors`, `config.json`, `generation_config.json` — HF model
+- `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja` — tokenizer + ChatML template
+
+## Notes
+
+- Small model + limited-domain corpus: expect repetition on long generations; use
+  a repetition penalty and keep continuations short.
+- Trained from scratch, so this is fully independent of any upstream Qwen weights.
+  The Qwen3 architecture/tokenizer are reused for compatibility.