---

license: cc-by-sa-4.0

base_model: johnbean393/chiboard-1-m0

tags:

• gguf
• chinese
• lfm2
• chiboard
• pinyin
• ime
• sft
• trl

language:

• zh

---

chiboard-1-m0-GGUF

GGUF exports of johnbean393/chiboard-1-m0, a bootstrap 350M SFT model for Chiboard mistake mining.

Prompt format:

<|startoftext|>{committed_context}<|reserved_6|>{raw_pinyin}<|reserved_7|>{display}<|reserved_8|>{target}<|im_end|>

The source model is trained with completion-only loss on {target}<|im_end|>.

Serve with exactly one <|startoftext|> token. Most runtimes add it automatically, so do not also embed it in the prompt string.

This is M0, a data-generation tool for typing-prefix replay; it is not the shipped final model.

Files

• Chiboard-M0-Q6_K.gguf: Q6_K GGUF quantization of johnbean393/chiboard-1-m0.
• Chiboard-M0-Q8_0.gguf: Q8_0 GGUF quantization of johnbean393/chiboard-1-m0.

Training

• Base model: LiquidAI/LFM2.5-350M-Base
• Dataset: johnbean393/chiboard-1-sft
• Source model: johnbean393/chiboard-1-m0
• Hub target: johnbean393/chiboard-1-m0-GGUF
• Training layout: mixed_packed
• Max packed length: 4096
• Effective batch: 10 x 6 = 60 packed rows per optimizer step
• Steps: 14746 / 14746
• Final eval loss: 0.17373667657375336
• Final eval mean token accuracy: 0.9519697650369391
• Train runtime seconds: 3.506e+04
• Packed LFM2 short-conv boundary isolation: seq_idx collator enabled