docs: add code-tape model card

README.md

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+---
+license: apache-2.0
+base_model: HuggingFaceTB/SmolLM2-135M-Instruct
+library_name: transformers
+pipeline_tag: text-generation
+language:
+- zh
+- en
+tags:
+- safetensors
+- llama
+- transformers
+- code-tape
+- subtitle-correction
+- chapter-generation
+---
+
+# code-tape subtitle postprocessor merged model
+
+This repository contains the full merged Hugging Face model for code-tape subtitle post-processing. It was produced by applying the project LoRA adapter to `HuggingFaceTB/SmolLM2-135M-Instruct`.
+
+The model is specialized for a narrow post-ASR task:
+
+- fix frontend/code terminology in subtitle text;
+- keep code identifiers, package names, function names, and component names stable;
+- return only changed subtitle segments as a sparse `segments` array;
+- create timestamped playback chapters;
+- output one strict JSON object.
+
+This model is not an audio transcription model. It should receive subtitle segments that already have ids, start/end timestamps, and ASR text.
+
+## Repository role
+
+code-tape publishes the same model family in three forms:
+
+| Repository | Purpose |
+| --- | --- |
+| [`ceilf6/code-tape-subtitle-postprocessor-lora`](https://huggingface.co/ceilf6/code-tape-subtitle-postprocessor-lora) | LoRA adapter for reproducibility and continued fine-tuning. |
+| [`ceilf6/code-tape-subtitle-postprocessor-merged`](https://huggingface.co/ceilf6/code-tape-subtitle-postprocessor-merged) | This full merged model, useful for Python/Transformers inspection or re-export. |
+| [`ceilf6/code-tape-subtitle-postprocessor-onnx`](https://huggingface.co/ceilf6/code-tape-subtitle-postprocessor-onnx) | Transformers.js-compatible ONNX export used by the browser app. |
+
+For browser-local inference in code-tape, use the ONNX repository. Use this repository when you need a standard Transformers checkpoint.
+
+## Intended contract
+
+Input is a chat message containing JSON:
+
+```json
+{
+  "context": {
+    "fileName": "ReplayControls.tsx",
+    "code": "const canSeek = durationMs > 0;",
+    "runtimeOutput": "",
+    "glossary": ["ReplayControls", "canSeek", "durationMs"]
+  },
+  "segments": [
+    { "id": "subtitle-1", "startMs": 0, "endMs": 1400, "text": "这里先判断 can seek 是否可用" }
+  ]
+}
+```
+
+Expected output shape:
+
+```json
+{
+  "segments": [
+    { "id": "subtitle-1", "text": "这里先判断 canSeek 是否可用" }
+  ],
+  "chapters": [
+    { "title": "判断回放是否可 seek", "startMs": 0, "endMs": 1400 }
+  ]
+}
+```
+
+Rules expected by the code-tape application:
+
+- output JSON only, with no Markdown or explanation;
+- `segments` contains only changed segments and may be empty;
+- every returned segment id must exist in the input and must not be duplicated;
+- chapter times must be monotonic, non-overlapping, and inside the subtitle timeline;
+- invalid output is discarded by the application.
+
+## Usage with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "ceilf6/code-tape-subtitle-postprocessor-merged"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are the code-tape subtitle post-processing model.\n"
+            "Only output one JSON object.\n"
+            "Goal: correct ASR subtitle text for frontend/code terms and create playback chapter jump points."
+        ),
+    },
+    {"role": "user", "content": "{\"context\":{},\"segments\":[]}"},
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=384, do_sample=False)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training and conversion
+
+The model was created from the code-tape subtitle post-processing LoRA workflow:
+
+1. prepare seed records with ASR-like subtitles, code context, runtime output, and glossary terms;
+2. distill strict JSON correction/chapter examples;
+3. fine-tune a LoRA adapter on `HuggingFaceTB/SmolLM2-135M-Instruct`;
+4. merge the adapter into a full model;
+5. export the merged model to ONNX for browser use.
+
+The merged checkpoint is mainly an intermediate artifact for reproducibility and export.
+
+## Evaluation
+
+code-tape evaluates this model family with project-specific checks instead of broad language-model benchmarks:
+
+- valid JSON object output;
+- valid sparse segment references;
+- glossary preservation after sparse corrections are applied back to the source subtitles;
+- non-empty, ordered, non-overlapping chapter supervision for training/evaluation records;
+- chapter bounds inside the subtitle timeline.
+
+The model output must always be validated by the caller.
+
+## Limitations
+
+- Narrowly trained for code-tape subtitle correction and chapter generation.
+- Not suitable as a general chat assistant or general summarizer.
+- Not an ASR model and cannot process audio directly.
+- Small local models may produce malformed JSON; callers must keep a fallback path.
+
+## Privacy and security
+
+The intended production path is the ONNX export running in the browser with `@huggingface/transformers`. Public browser loading does not require a Hugging Face token.
+
+Do not put secrets, credentials, private code, or access tokens in prompts unless your inference environment is trusted.
+
+## License
+
+Apache-2.0, following the base model license.