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Model: h4bbo/FuseLLM-112M-Completion Source: Original Platform
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README.md
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---
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library_name: transformers
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license: other # TODO: set the license you want to release this model under
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language:
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- code
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tags:
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- qwen3
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- causal-lm
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- code-completion
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- habbo
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- from-scratch
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---
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# FuseLLM-112M
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A small **112M-parameter decoder-only language model trained from scratch** (no base
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checkpoint, no LoRA) on a corpus of Habbo emulator / game-server source code. The
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goal is a tiny, fast model for **code completion** in that Java codebase, not a
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general-purpose or instruction-following model.
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## Model details
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| | |
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|---|---|
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| Architecture | Qwen3 (decoder-only causal LM) |
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| Parameters | ~112M (tied input/output embeddings) |
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| Hidden size | 512 |
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| Layers | 8 (all full attention) |
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| Attention heads | 8 (8 KV heads) |
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| Vocab size | 151,936 |
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| Max context | 2048 |
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| Precision | float32 (safetensors) |
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| Training | From scratch, 4 epochs, 16,188 steps |
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| Final train loss | ~0.58 |
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`tie_word_embeddings: true` — the output `lm_head` shares the input embedding
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matrix, so checkpoints store only one copy. This is expected, not a missing weight.
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## Intended use
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- **Code completion** for Habbo-style Java server code (raw prompt → continuation).
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- Local experimentation / distillation base.
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## What it is NOT
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- **Not instruction-tuned / not a chat model.** It was trained only on raw source
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code, never on chat/instruction data.
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- The Qwen3 ChatML chat template is included (it ships with the tokenizer) for
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tokenizer/tool compatibility, but the model has **not** learned to follow chat
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turns. Passing chat-formatted prompts will produce poor, often repetitive output.
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Use it in **completion mode**, not conversation mode.
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## Usage
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### transformers (recommended for completion)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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m = AutoModelForCausalLM.from_pretrained("h4bbo/FuseLLM-112M")
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tok = AutoTokenizer.from_pretrained("h4bbo/FuseLLM-112M")
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prompt = "public class Room {\n public void onEnter(Player p) {\n "
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ids = tok(prompt, return_tensors="pt").input_ids
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out = m.generate(ids, max_new_tokens=64, do_sample=False,
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repetition_penalty=1.1, pad_token_id=tok.eos_token_id)
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print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
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```
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### llama.cpp (completion mode)
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No GGUF is shipped in this repo. The HF model is **verified** to convert and run in
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`llama.cpp`; generate the GGUF locally:
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```bash
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# 1) convert HF -> lossless fp16 GGUF
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python convert_hf_to_gguf.py h4bbo/FuseLLM-112M --outtype f16 \
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--model-name FuseLLM-112M --outfile FuseLLM-112M.fp16.gguf
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# (optional) 4-bit quantize
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llama-quantize FuseLLM-112M.fp16.gguf FuseLLM-112M.Q4_K_M.gguf Q4_K_M
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# 2) completion mode — pass the raw code seed, do NOT use chat/conversation mode.
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llama-cli -m FuseLLM-112M.Q4_K_M.gguf -cnv -st --no-jinja \
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-f seed.txt -n 64 --temp 0.0 --repeat-penalty 1.1 --no-display-prompt < /dev/null
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```
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`--no-jinja` keeps the prompt raw (the embedded chat template exists but the model
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isn't chat-tuned, so conversation mode is not meaningful for this model).
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## Files
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- `model.safetensors`, `config.json`, `generation_config.json` — HF model
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- `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja` — tokenizer + ChatML template
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## Notes
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- Small model + limited-domain corpus: expect repetition on long generations; use
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a repetition penalty and keep continuations short.
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- Trained from scratch, so this is fully independent of any upstream Qwen weights.
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The Qwen3 architecture/tokenizer are reused for compatibility.
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61
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "float32",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 512,
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"initializer_range": 0.02,
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"intermediate_size": 1408,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 2048,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 8,
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"num_hidden_layers": 8,
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"num_key_value_heads": 8,
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"pad_token_id": 151643,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.13.0",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936,
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"_name_or_path": "FuseLLM-112M"
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}
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generation_config.json
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{
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"_from_model_config": true,
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"eos_token_id": [
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151645
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],
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"output_attentions": false,
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"output_hidden_states": false,
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"pad_token_id": 151643,
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"transformers_version": "5.13.0",
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"use_cache": false
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}
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model.safetensors
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3
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d3ebdb08b1e9f44ccf2bf768c7425bbb3971ecfd4f08fc59b5083b718a0d63fb
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size 447532792
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
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size 11422650
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tokenizer_config.json
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
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"<|video_pad|>"
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],
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"is_local": false,
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"local_files_only": false,
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"model_max_length": 1010000,
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"pad_token": "<|endoftext|>",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null
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}
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