asmr-qwen3.5-2b-zh-cn-echo-gguf-v0.1

GGUF quantizations of a fine-tuned model for translating Japanese ASMR transcriptions (ASR/Whisper output) into Simplified Chinese.

The model normalizes imperfect audio transcriptions, applies domain-specific glossaries, and translates character dialogue while retaining emotion and nuances.

Echo Mode

The model "echoes" the source Japanese text in an "input" field alongside the target translation. This anchors cross-attention, significantly reducing hallucinations and omitted segments.

Available Quantizations

Prompt Example

将以下日语ASMR逐字稿翻译成简体中文。

音轨：track01_示例音轨
场景说明：主角与青梅竹马在校园下午的对话...

术语表（请严格使用zh栏位的译名）：
{
  "cvs": [],
  "characters": [],
  "terms": [{"ja": "放課後", "zh": "放学后"}]
}

翻译前请静默修正以下Whisper识别错误：
- 重复片语（连续3次以上且无变化）：仅保留一次
- 错字／同音异字：依上下文修正
- 字幕版权行（字幕：／翻訳：／QQ／LINE水印）：text设为null
- 错误专有名词：依术语表修正

翻译规则：
- 呻吟与气息声（あ、ん、はあ）→ 自然对应（啊、嗯、哈、呼）
- 拟声词：日语形式翻译（パンパン→啪啪）；中文形式保留原样
- 保留角色语气与口吻
- text字段只输出译文，不加注释或括号说明
- input字段为ids所对应的原始日文片段

输入：逐字稿JSON数组 — {"id": <n>, "text": "<日文>", "start": <ms>, "end": <ms>}

输出：将连续构成同一句话的片段合并，JSON数组格式：
{"ids": [<n>, ...], "input": "<合并后的原始日文，以空格连接>", "text": "<简体中文>", "start": <最早ms>, "end": <最晚ms>}

字幕版权行：{"ids": [<n>], "input": "<原始日文>", "text": null, "start": <ms>, "end": <ms>}
每个输入id必须恰好出现在一个输出项中。
input字段为ids所对应的原始日文片段以空格连接，text为其简体中文翻译。

逐字稿：
[
  {"id": 1, "text": "ねぇ、放課後、", "start": 3000, "end": 5000},
  {"id": 2, "text": "一緒に帰らない?", "start": 5000, "end": 7000}
]

Example Output:

[{"ids": [1, 2], "input": "ねぇ、放課後、 一緒に帰らない?", "text": "呐，放学后，要不要一起回去？", "start": 3000, "end": 7000}]

Usage

llama-server

llama-server -m asmr-qwen3.5-2b-zh-cn-echo-gguf-v0.1-q4_k_m.gguf -c 4096 --port 8080

llama-cli

llama-cli -m asmr-qwen3.5-2b-zh-cn-echo-gguf-v0.1-q4_k_m.gguf -p "<your prompt>" -n 2048

Structured Decoding (Recommended)

This model outputs JSON arrays. Using structured decoding (e.g. GBNF grammar or JSON schema constraints) avoids wasted computation on malformed output and guarantees valid JSON on every generation.

JSON Schema:

{
  "type": "array",
  "items": {
    "type": "object",
    "properties": {
      "ids": {
        "type": "array",
        "items": {
          "type": "integer"
        },
        "minItems": 1
      },
      "input": {
        "type": "string"
      },
      "text": {
        "anyOf": [
          {
            "type": "string"
          },
          {
            "type": "null"
          }
        ]
      },
      "start": {
        "type": "integer"
      },
      "end": {
        "type": "integer"
      }
    },
    "required": [
      "ids",
      "input",
      "text",
      "start",
      "end"
    ],
    "additionalProperties": false
  },
  "minItems": 1
}

Supported by llama.cpp (--json-schema), vLLM, and outlines.

Training Details

• Base model: unsloth/Qwen3.5-2B
• Method: LoRA (r=16, alpha=16)
• Target modules: k_proj, gate_proj, o_proj, q_proj, down_proj, up_proj, v_proj
• Locale: zh-cn (Simplified Chinese)
• Mode: Echo Mode
• Max sequence length: 4096
• Precision: bf16