Qwen3-Reranker-0.6B Core ML int8, compiled

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-Reranker-0.6B
+base_model_relation: quantized
+library_name: coreml
+pipeline_tag: text-ranking
+language:
+- multilingual
+- en
+- zh
+tags:
+- coreml
+- apple-neural-engine
+- ane
+- int8
+- on-device
+- reranker
+- qwen3
+---
+
+# Qwen3-Reranker-0.6B — Core ML int8 (Apple Neural Engine, compiled)
+
+[English](#english) · [中文](#中文)
+
+<a id="english"></a>
+
+## English
+
+[Qwen/Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) converted to Core ML:
+
+- **Neural Engine.** The model runs on the Apple Neural Engine (2,566 of 2,573 ops; the other 7 are the token-embedding lookup, on the CPU).
+- **int8 weights.** Activations stay fp16.
+- **Precompiled.** It ships as a compiled `.mlmodelc`.
+- **Fixed lengths.** Nine functions have fixed lengths from 128 to 768 tokens and share one set of weights.
+
+It scores a (query, document) pair the way the model card does: the logits of "no" and "yes" at the last position,
+and the relevance score is `sigmoid(yes − no)`.
+
+### Files
+
+| File | What | Size |
+|---|---|---|
+| `Qwen3Reranker.mlmodelc` | int8 weights, functions `L128`, `L160`, `L192`, `L224`, `L256`, `L320`, `L384`, `L512`, `L768` | 606 MB |
+| `tokenizer.json` | The original Qwen3 tokenizer, unchanged | 11 MB |
+| `config.json` | Prompt strings, token ids, functions, input and output layout | |
+| `SHA256SUMS` | Checksums of every file | |
+
+Sizes are in MB = 10⁶ bytes.
+
+### Inputs and output
+
+Every function `L{N}` takes:
+
+| Name | Type | Shape | Content |
+|---|---|---|---|
+| `input_ids` | int32 | [1, N] | Token ids, **left-padded** with 151643 (`<\|endoftext\|>`) |
+| `mask` | fp16 | [1, N] | 0 for padding, 1 for tokens |
+
+It returns `logits`, fp32 [1, 2], in the order **[no, yes]**. Relevance = `1 / (1 + exp(no − yes))`, which is the
+same as the model card's softmax over the "no" and "yes" logits.
+
+### Use
+
+1. Build three strings (`\n` is a newline). The prefix and suffix are fixed and are also in `config.json`:
+
+   ```text
+   prefix: <|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n<|im_start|>user\n
+   body:   <Instruct>: {instruction}\n<Query>: {query}\n<Document>: {document}
+   suffix: <|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n
+   ```
+
+   In the body, replace `{instruction}`, `{query}` and `{document}` with your instruction, query and document.
+   The default instruction is `Given a web search query, retrieve relevant passages that answer the query`.
+2. Tokenize each string with `tokenizer.json`, without adding special tokens. The prefix is 39 tokens and the suffix
+   is 9.
+3. Cut the body's tokens so that prefix + body + suffix ≤ 768. Join the three lists; the result has n tokens.
+4. Pick the smallest N ≥ n from {128, 160, 192, 224, 256, 320, 384, 512, 768}, left-pad to N, and call function
+   `L{N}`.
+
+```python
+import numpy as np, coremltools as ct
+from tokenizers import Tokenizer
+
+tok = Tokenizer.from_file("tokenizer.json")
+enc = lambda s: tok.encode(s, add_special_tokens=False).ids
+PREFIX = ("<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct "
+          "provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n")
+SUFFIX = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
+INSTRUCT = "Given a web search query, retrieve relevant passages that answer the query"
+SIZES = [128, 160, 192, 224, 256, 320, 384, 512, 768]
+models = {}
+
+def score(query, doc):
+    pre, suf = enc(PREFIX), enc(SUFFIX)
+    body = enc(f"<Instruct>: {INSTRUCT}\n<Query>: {query}\n<Document>: {doc}")[:768 - len(pre) - len(suf)]
+    ids = pre + body + suf; n = len(ids); N = next(s for s in SIZES if s >= n)
+    if N not in models:
+        models[N] = ct.models.CompiledMLModel("Qwen3Reranker.mlmodelc",
+                                              compute_units=ct.ComputeUnit.CPU_AND_NE, function_name=f"L{N}")
+    feed = {"input_ids": np.array([[151643] * (N - n) + ids], np.int32),
+            "mask": np.array([[0] * (N - n) + [1] * n], np.float16)}
+    no, yes = models[N].predict(feed)["logits"].reshape(-1)
+    return float(1 / (1 + np.exp(no - yes)))
+
+print(score("周末去哪里爬山", "香山公园秋季红叶最好看，周末游客较多，建议早上出发。"))
+```
+
+```swift
+import CoreML
+
+let config = MLModelConfiguration()
+config.computeUnits = .cpuAndNeuralEngine
+config.functionName = "L256"                        // smallest bucket that fits
+let model = try MLModel(contentsOf: rerankerURL, configuration: config)   // Qwen3Reranker.mlmodelc
+
+let ids = try MLMultiArray(shape: [1, 256], dataType: .int32)     // left-padded with 151643
+let mask = try MLMultiArray(shape: [1, 256], dataType: .float16)  // 0 = padding, 1 = token
+// … fill ids and mask …
+let out = try model.prediction(from: MLDictionaryFeatureProvider(dictionary: ["input_ids": ids, "mask": mask]))
+let logits = out.featureValue(for: "logits")!.multiArrayValue!
+let relevance = 1 / (1 + exp(logits[0].doubleValue - logits[1].doubleValue))   // [no, yes]
+```
+
+Load each function once and keep it. Loading a function is slow; scoring is fast.
+
+### How it was converted
+
+We wrote the Qwen3 forward pass by hand in PyTorch so it can be traced with fixed shapes:
+
+- **Fixed length per function.** Inputs are left-padded and use a causal mask plus a padding mask. The RoPE tables are
+  precomputed.
+- **RMSNorm safe for fp16.** On our test pairs, Qwen3's hidden states reach values above 8,000, and squaring them inside RMSNorm
+  overflows fp16. Each RMSNorm first divides the row by its largest absolute value, which leaves the result
+  mathematically unchanged. (With plain RMSNorm, our conversion of Qwen3-Embedding-0.6B, which has the same backbone,
+  reached only cosine 0.90 to the original on the Neural Engine.)
+- **MLP scaled for fp16.** The MLP's gate × up product is scaled by 1/64 before `down_proj`, and the result is scaled
+  back by 64.
+- **Two-row LM head.** Only the two LM-head rows that are needed ("no" 2152, "yes" 9693) are kept, so each call
+  returns 2 logits instead of 151,669.
+
+Then, for each length:
+
+1. trace with `torch.jit.trace`;
+2. convert with coremltools 9 to an ML Program (fp16 compute, macOS 15 target);
+3. quantize to int8 with `linear_quantize_weights` (linear symmetric, per output channel, weights with more than
+   2048 elements);
+4. merge the nine functions with `ct.utils.save_multifunction`, which stores the weights once;
+5. compile with `ct.utils.compile_model`.
+
+### Validation
+
+Tests ran on one MacBook Pro (M3 Max, macOS 26.7) with the Neural Engine. For each query we took
+[EmbeddingGemma 2](https://huggingface.co/google/embeddinggemma-2)'s top 30 results and reranked them with this model,
+using the default instruction above. The reference is the original model in PyTorch (fp16, GPU), called as in its
+model card.
+
+| Test set | EmbeddingGemma 2 alone | + PyTorch reranker | **+ this model (int8, ANE)** | Rank correlation with PyTorch |
+|---|---|---|---|---|
+| Simulated personal knowledge base, 113 queries | 93.5 | 97.5 | **96.9** | 0.992 |
+| MLQA, Chinese question → English passage, 100 queries | 60.8 | 69.0 | **68.9** | 0.993 |
+| MLQA, English question → Chinese passage, 100 queries | 63.3 | 78.9 | **78.6** | 0.995 |
+
+All scores are nDCG@10. The simulated knowledge base has 2,829 chunks of notes, Wikipedia pages, open-source
+documentation and code, mixed Chinese and English, with queries written by an LLM. Rank correlation is the mean
+Spearman correlation over each query's 30 candidates.
+
+**Other checks.** An fp16 build of the same conversion matched PyTorch on three more sets:
+
+| Test set | PyTorch reranker | fp16 build (ANE) |
+|---|---|---|
+| T2Retrieval (Chinese) | 94.2 | 94.3 |
+| DuRetrieval (Chinese) | 93.7 | 93.8 |
+| SciFact (English) | 73.0 | 71.1 |
+
+**The instruction matters.** On SciFact, reranking with the default web-search instruction lowered nDCG@10 from 82.2
+to 73.0, because SciFact's queries are scientific claims, not questions. Write an instruction that matches your task.
+
+**Speed** (M3 Max, Neural Engine, one pair per call, median):
+
+| Function | L128 | L160 | L192 | L224 | L256 | L320 | L384 | L512 | L768 |
+|---|---|---|---|---|---|---|---|---|---|
+| ms per pair | 14.5 | 20.2 | 22.7 | 25.8 | 31.1 | 44.9 | 54.0 | 83.5 | 145 |
+
+Reranking the top 10 takes about 0.3–0.4 s per query on these test sets; the top 30 takes 0.8–1.2 s. int8 is barely
+faster than fp16 (at most 6%), but it is half the size. In a test with fp16 weights, scoring 4 pairs as one batch was
+slower per pair than scoring them one at a time.
+
+### Limitations
+
+- **Tested on one machine only:** M3 Max with macOS 26.7. The model needs macOS 15 / iOS 18 or later, because it is a
+  multifunction model.
+- **First load is slow.** The first load on a device compiles each function for the Neural Engine. On the M3 Max this
+  took 13–30 s per function, 2–3 minutes for all nine. Core ML caches the result. Apps should warm the functions up in
+  the background.
+- **768-token limit.** Pairs longer than 768 tokens must be truncated. The original model accepts much longer inputs.
+- **Small test sets.** The test sets are small and some queries are LLM-written.
+
+### License and attribution
+
+- **License.** Apache 2.0, the same as [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B).
+- **Qwen.** The model is by the Qwen team, Alibaba Cloud.
+- **This repo.** We did the Core ML conversion, the int8 quantization and the tests. This repo is not affiliated with
+  the Qwen team.
+
+---
+
+<a id="中文"></a>
+
+## 中文
+
+本仓库是 [Qwen/Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) 的 Core ML 版本：
+
+- **神经网络引擎**：模型在 Apple 神经网络引擎（ANE）上运行（2,573 个算子中有 2,566 个在 ANE 上，剩下 7 个是查词表那一步，在 CPU 上）。
+- **int8 权重**：计算仍用 fp16。
+- **预先编译**：以编译好的 `.mlmodelc` 发布。
+- **固定长度**：有 9 个长度固定的函数，从 128 到 768 个 token，共用一份权重。
+
+打分方式和官方模型卡相同：取最后一个位置上“no”和“yes”两个词的 logit，相关度 = `sigmoid(yes − no)`。
+
+### 文件
+
+| 文件 | 内容 | 大小 |
+|---|---|---|
+| `Qwen3Reranker.mlmodelc` | int8 权重；函数 `L128`、`L160`、`L192`、`L224`、`L256`、`L320`、`L384`、`L512`、`L768` | 606 MB |
+| `tokenizer.json` | Qwen3 原版分词器，未改动 | 11 MB |
+| `config.json` | 提示词、特殊 token、函数列表、输入输出格式 | |
+| `SHA256SUMS` | 所有文件的校验和 | |
+
+本页的 MB 指 10⁶ 字节。
+
+### 输入和输出
+
+每个函数 `L{N}` 的输入：
+
+| 名称 | 类型 | 形状 | 内容 |
+|---|---|---|---|
+| `input_ids` | int32 | [1, N] | token id，**在左侧补齐**，补齐值为 151643（`<\|endoftext\|>`） |
+| `mask` | fp16 | [1, N] | 补齐的位置为 0，有 token 的位置为 1 |
+
+输出是 `logits`，fp32 [1, 2]，顺序是 **[no, yes]**。相关度 = `1 / (1 + exp(no − yes))`，
+和模型卡里对 no、yes 两个 logit 做 softmax 的结果相同。
+
+### 用法
+
+1. **拼三段文本**（`\n` 表示换行）。前缀和后缀是固定的，`config.json` 里也有：
+
+   ```text
+   prefix: <|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be "yes" or "no".<|im_end|>\n<|im_start|>user\n
+   body:   <Instruct>: {instruction}\n<Query>: {query}\n<Document>: {document}
+   suffix: <|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n
+   ```
+
+   把 body 里的 `{instruction}`、`{query}`、`{document}` 分别换成指令、查询和文档。
+   默认指令是 `Given a web search query, retrieve relevant passages that answer the query`。
+2. **分词。** 三段分别用 `tokenizer.json` 分词，不加特殊 token。前缀 39 个 token，后缀 9 个。
+3. **截断。** 截短中间一段，使前缀 + 中间 + 后缀 ≤ 768 个 token，再把三段拼起来，共 n 个 token。
+4. **运行。** 在 {128, 160, 192, 224, 256, 320, 384, 512, 768} 中选出不小于 n 的最小值 N，在左侧补齐到 N，
+   然后调用函数 `L{N}`。
+
+```python
+import numpy as np, coremltools as ct
+from tokenizers import Tokenizer
+
+tok = Tokenizer.from_file("tokenizer.json")
+enc = lambda s: tok.encode(s, add_special_tokens=False).ids
+PREFIX = ("<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct "
+          "provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n")
+SUFFIX = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
+INSTRUCT = "Given a web search query, retrieve relevant passages that answer the query"
+SIZES = [128, 160, 192, 224, 256, 320, 384, 512, 768]
+models = {}
+
+def score(query, doc):
+    pre, suf = enc(PREFIX), enc(SUFFIX)
+    body = enc(f"<Instruct>: {INSTRUCT}\n<Query>: {query}\n<Document>: {doc}")[:768 - len(pre) - len(suf)]
+    ids = pre + body + suf; n = len(ids); N = next(s for s in SIZES if s >= n)
+    if N not in models:
+        models[N] = ct.models.CompiledMLModel("Qwen3Reranker.mlmodelc",
+                                              compute_units=ct.ComputeUnit.CPU_AND_NE, function_name=f"L{N}")
+    feed = {"input_ids": np.array([[151643] * (N - n) + ids], np.int32),
+            "mask": np.array([[0] * (N - n) + [1] * n], np.float16)}
+    no, yes = models[N].predict(feed)["logits"].reshape(-1)
+    return float(1 / (1 + np.exp(no - yes)))
+
+print(score("周末去哪里爬山", "香山公园秋季红叶最好看，周末游客较多，建议早上出发。"))
+```
+
+```swift
+import CoreML
+
+let config = MLModelConfiguration()
+config.computeUnits = .cpuAndNeuralEngine
+config.functionName = "L256"                        // 能放下这一对的最小长度档
+let model = try MLModel(contentsOf: rerankerURL, configuration: config)   // Qwen3Reranker.mlmodelc
+
+let ids = try MLMultiArray(shape: [1, 256], dataType: .int32)     // 在左侧用 151643 补齐
+let mask = try MLMultiArray(shape: [1, 256], dataType: .float16)  // 0 = 补齐，1 = token
+// …… 填入 ids 和 mask ……
+let out = try model.prediction(from: MLDictionaryFeatureProvider(dictionary: ["input_ids": ids, "mask": mask]))
+let logits = out.featureValue(for: "logits")!.multiArrayValue!
+let relevance = 1 / (1 + exp(logits[0].doubleValue - logits[1].doubleValue))   // 顺序是 [no, yes]
+```
+
+每个函数加载一次后保留复用：加载慢，打分快。
+
+### 转换方法
+
+我们用 PyTorch 手写了 Qwen3 的前向计算，使它能以固定形状被追踪：
+
+- **每个函数长度固定**：输入在左侧补齐，使用因果遮罩和补齐遮罩；RoPE 表预先算好。
+- **防 fp16 溢出的 RMSNorm**：在我们的测试样本上，Qwen3 的隐藏状态有超过 8,000 的离群值，在 RMSNorm 里平方后会超出 fp16 的范围。
+  每个 RMSNorm 先把这一行除以它的最大绝对值，数学上结果不变。（我们转换同一骨干的 Qwen3-Embedding-0.6B 时，
+  用普通 RMSNorm 在神经网络引擎上和原版的余弦相似度只有 0.90。）
+- **防溢出的 MLP**：MLP 中 gate × up 的乘积先乘以 1/64，经过 `down_proj` 之后再乘回 64。
+- **只保留两行输出层**：只保留需要的两行（“no” 2152、“yes” 9693），每次调用只输出 2 个 logit，而不是 151,669 个。
+
+然后对每个长度：
+
+1. 用 `torch.jit.trace` 追踪；
+2. 用 coremltools 9 转成 ML Program（fp16 计算，最低 macOS 15）；
+3. 用 `linear_quantize_weights` 压成 int8（线性对称，按输出通道；只压缩元素数超过 2048 的权重）；
+4. 用 `ct.utils.save_multifunction` 把 9 个函数合并成一个模型，权重只存一份；
+5. 用 `ct.utils.compile_model` 编译。
+
+### 验证
+
+在一台 MacBook Pro（M3 Max，macOS 26.7）上测试，模型跑在神经网络引擎上。对每条查询，先取
+[EmbeddingGemma 2](https://huggingface.co/google/embeddinggemma-2) 检索出的前 30 条结果，再用本模型重排，指令用上面的默认指令。
+参照是 PyTorch 原版模型（fp16，GPU），调用方式和模型卡相同。
+
+| 测试集 | 只用 EmbeddingGemma 2 | + PyTorch 原版重排 | **+ 本模型（int8，ANE）** | 和 PyTorch 排序的相关系数 |
+|---|---|---|---|---|
+| 模拟个人知识库，113 条查询 | 93.5 | 97.5 | **96.9** | 0.992 |
+| MLQA，中文问题找英文段落，100 条查询 | 60.8 | 69.0 | **68.9** | 0.993 |
+| MLQA，英文问题找中文段落，100 条查询 | 63.3 | 78.9 | **78.6** | 0.995 |
+
+- **指标**：都是 nDCG@10。
+- **模拟个人知识库**：2,829 个文本块，内容包括笔记、维基百科页面、开源文档和代码，中英混合；查询由大模型编写。
+- **相关系数**：对每条查询的 30 个候选计算 Spearman 相关系数，再取平均。
+
+**其他检查**：同一转换方法的 fp16 版本在另外三个测试集上也和 PyTorch 一致：
+
+| 测试集 | PyTorch 原版重排 | fp16 版本（ANE） |
+|---|---|---|
+| T2Retrieval（中文） | 94.2 | 94.3 |
+| DuRetrieval（中文） | 93.7 | 93.8 |
+| SciFact（英文） | 73.0 | 71.1 |
+
+**指令很重要**：在 SciFact 上，用默认的网页搜索指令重排后，nDCG@10 从 82.2 降到了 73.0。
+原因是 SciFact 的查询是科学论断，不是问题。请按自己的任务写指令。
+
+**速度**（M3 Max，神经网络引擎，每次调用打一对分，取中位数）：
+
+| 函数 | L128 | L160 | L192 | L224 | L256 | L320 | L384 | L512 | L768 |
+|---|---|---|---|---|---|---|---|---|---|
+| 每对耗时（ms） | 14.5 | 20.2 | 22.7 | 25.8 | 31.1 | 44.9 | 54.0 | 83.5 | 145 |
+
+- **每条查询的耗时**：在这些测试集上，重排前 10 条约 0.3–0.4 秒，前 30 条约 0.8–1.2 秒。
+- **int8 和 fp16 对比**：int8 只比 fp16 快一点（最多 6%），但体积减半。
+- **批量打分**：在 fp16 版本上测过，把 4 对放在一个批次里打分，平均每对反而比逐对打分更慢。
+
+### 局限
+
+- **只在一台机器上测过**：M3 Max，macOS 26.7。模型是多函数模型，需要 macOS 15 / iOS 18 或更高版本。
+- **第一次加载很慢**：在每台设备上第一次加载时，系统要把每个函数编译给神经网络引擎。M3 Max 上每个函数需要 13–30 秒，
+  9 个函数共 2–3 分钟。Core ML 会缓存编译结果。建议 App 在后台预热各个函数。
+- **最长 768 个 token**：超过 768 个 token 的输入必须截断，而原版模型支持长得多的输入。
+- **测试集很小**：部分查询由大模型编写。
+
+### 许可证和来源
+
+- **许可证**：Apache 2.0，和 [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) 相同。
+- **Qwen**：模型由阿里云通义千问团队发布。
+- **本仓库**：我们做了 Core ML 转换、int8 量化和测试。本仓库与通义千问团队没有关联。

SHA256SUMS

@@ -0,0 +1,7 @@
+c71d239df91726fc519c6eb72d318ec65820627232b2f796219e87dcf35d0ab4  LICENSE
+28c83e3735a90ab9d4f38cf31a31c5eb9b078ec9700443aba000fb8988a9ddf9  Qwen3Reranker.mlmodelc/analytics/coremldata.bin
+27758036bde2510df206f4343146f90509785307dc8a607665e7fa96a0cabf2b  Qwen3Reranker.mlmodelc/coremldata.bin
+8df1324548aa3585be89f00627f887571f3ac40b6b816ab8f9439842e32ad9b8  Qwen3Reranker.mlmodelc/model.mil
+55a35f46fdda6129daf0f346a35fb1f3a1e49e4c8405c6ab65f2c9f3b13e9776  Qwen3Reranker.mlmodelc/weights/weight.bin
+184530324f224da2898a7ee094e6b2e68a137d263e4fc936582865536eb828f6  config.json
+aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4  tokenizer.json

config.json

@@ -0,0 +1,92 @@
+{
+  "base_model": "Qwen/Qwen3-Reranker-0.6B",
+  "format": "coreml-compiled",
+  "model": "Qwen3Reranker.mlmodelc",
+  "compute_units": "cpuAndNeuralEngine",
+  "precision": {
+    "weights": "int8 (linear symmetric, per output channel)",
+    "activations": "fp16"
+  },
+  "minimum_os": {
+    "macOS": "15.0",
+    "iOS": "18.0"
+  },
+  "tokenizer": "tokenizer.json",
+  "add_special_tokens": false,
+  "functions": {
+    "L128": {
+      "tokens": 128
+    },
+    "L160": {
+      "tokens": 160
+    },
+    "L192": {
+      "tokens": 192
+    },
+    "L224": {
+      "tokens": 224
+    },
+    "L256": {
+      "tokens": 256
+    },
+    "L320": {
+      "tokens": 320
+    },
+    "L384": {
+      "tokens": 384
+    },
+    "L512": {
+      "tokens": 512
+    },
+    "L768": {
+      "tokens": 768
+    }
+  },
+  "inputs": {
+    "input_ids": {
+      "dtype": "int32",
+      "shape": [
+        1,
+        "L"
+      ],
+      "padding": "left",
+      "pad_token_id": 151643
+    },
+    "mask": {
+      "dtype": "float16",
+      "shape": [
+        1,
+        "L"
+      ],
+      "values": "0 for padding, 1 for tokens"
+    }
+  },
+  "output": {
+    "logits": {
+      "dtype": "float32",
+      "shape": [
+        1,
+        2
+      ],
+      "order": [
+        "no",
+        "yes"
+      ],
+      "score": "sigmoid(yes - no) = probability that the document is relevant"
+    }
+  },
+  "token_ids": {
+    "yes": 9693,
+    "no": 2152,
+    "pad": 151643
+  },
+  "prompt": {
+    "prefix": "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n",
+    "body": "<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {document}",
+    "suffix": "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n",
+    "default_instruction": "Given a web search query, retrieve relevant passages that answer the query",
+    "prefix_tokens": 39,
+    "suffix_tokens": 9,
+    "truncate": "truncate the body tokens so prefix + body + suffix <= 768"
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654