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+  a. Adapted Material means material subject to Copyright and Similar
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+                                 Apache License
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LICENSE-QWEN3

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+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
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+      "Legal Entity" shall mean the union of the acting entity and all
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NOTICE

@@ -0,0 +1,13 @@
+Octen-WebGR
+Copyright 2026 Octen
+
+This model is a full-parameter fine-tune of a Qwen3 base model (Qwen/Qwen3-4B-Base or Qwen/Qwen3-0.6B-Base, see README.md),
+Copyright 2024 Alibaba Cloud, licensed under the Apache License, Version 2.0 (LICENSE-QWEN3).
+
+Modifications by Octen: the vocabulary was extended by 2 x 65,536 semantic-identifier tokens and all parameters were trained to map
+search queries to these identifiers.
+
+Licensing of this repository:
+  - Model weights (*.safetensors) and the SID-to-URL table (sid_table/): CC BY-NC 4.0 (LICENSE).
+  - Inference code (infer_gpu.py, infer_cpu.py): Apache License 2.0 (LICENSE-CODE).
+  - Tokenizer files are those of the Qwen3 base model and remain under its Apache License 2.0 (LICENSE-QWEN3).

README.md

@@ -0,0 +1,139 @@
+---
+license: cc-by-nc-4.0
+license_link: LICENSE
+base_model: Qwen/Qwen3-4B-Base
+library_name: transformers
+tags:
+  - generative-retrieval
+  - semantic-id
+  - navigational-search
+  - web-search
+---
+
+# Octen-WebGR-4B
+
+Octen-WebGR-4B is a generative retriever for navigational web search. Given a query, it generates a two-token semantic identifier
+(SID) that is constrained to a table of 195.9 million real URLs, so the web index lives in the model's parameters: retrieval needs
+one GPU and about 10 GB of memory, instead of hundreds of gigabytes of vectors.
+
+It is described in the technical report *Octen-WebGR: Scaling Generative Retrieval to 200 Million Web Pages* (Octen Team, 2026).
+A smaller model for a 31-million-URL table is available as [Octen-WebGR-0.6B](https://huggingface.co/Octen/Octen-WebGR-0.6B).
+
+## Model
+
+| | |
+|---|---|
+| Base model | Qwen3-4B-Base, full-parameter fine-tuning |
+| Layers / hidden size | 36 / 2,560 |
+| Parameters, total (non-embedding) | 4.36B (3.63B) |
+| Identifier tokens | 2 × 65,536 (vocabulary ids 151,936 to 283,007) |
+| URLs in the training universe | 201,231,792 |
+| URLs in the released table | 195,921,090 (after screening, see below) |
+| Training data | Query–URL click pairs up to August 2026 (proprietary, not released) |
+| Weights | bf16, 8.7 GB |
+
+## Results
+
+Navigational queries from September 2026, the month after the training data, weighted by clicks. A system scores a hit when the
+clicked target page is among its top *k* URLs; targets outside a system's candidate set count as misses.
+
+| System | hit@1 | hit@10 |
+|---|---|---|
+| **Octen-WebGR-4B** | **59.6** | **68.7** |
+| Octen-WebGR-0.6B (31M-URL universe) | 56.5 | 64.0 |
+| Dense retrieval (Octen embedding + adapter trained on the same clicks, exact search) | 24.8 | 55.6 |
+| Dense retrieval + click prior | 46.5 | 64.2 |
+| BM25 over URL texts (URL + training queries) | 9.1 | 25.3 |
+| Lookup in the training click table | 58.9 | 62.5 |
+
+GR is strongest on new ways of asking for familiar pages: on unseen queries for pages with many training queries it ranks the target
+first 30.9% of the time, against 17.2% for dense retrieval (21.4% with the click prior). Dense retrieval remains better on rarely
+visited pages, and a click table cannot answer new queries at all. All numbers use the full 201M universe; restricting decoding to the screened table lowers hit@1 from 59.6 to 58.7,
+because 1.2% of the traffic targets a removed URL. See the report for the full evaluation.
+
+## How it works
+
+1. Normalise the query: collapse whitespace, strip, lowercase. Keep at most the first 32 tokens.
+2. Prompt: `Query: {q}\nURL:` without special tokens.
+3. Generate two SID tokens. Level *t* (0 or 1) uses vocabulary id `151936 + t * 65536 + code`.
+4. Constrained beam search: at each level, only prefixes present in `sid_table/` are allowed and the softmax is taken over the valid
+   children only; 20 beams are kept. The score is the sum of the two log-probabilities.
+5. Look `(sid0, sid1)` up in `sid_table/` to get the URL.
+
+> **Do not use `model.generate()`.** The tokenizer has no entries for the SID tokens, and unconstrained generation returns
+> identifiers that map to no URL. Use the inference code in this repository.
+
+## Usage
+
+```bash
+pip install -r requirements.txt      # torch 2.8.0, transformers 4.57.1; flash-attn optional
+python infer_gpu.py --model Octen/Octen-WebGR-4B --query "youtube" "boeing internship"
+python infer_gpu.py --model Octen/Octen-WebGR-4B --check        # compare against smoke_test.json
+```
+
+```python
+from huggingface_hub import snapshot_download
+import sys
+
+root = snapshot_download("Octen/Octen-WebGR-4B")
+sys.path.insert(0, root)
+from infer_gpu import GR
+
+gr = GR(root)                        # loads the model and the 195.9M-row table
+for url, logp, row in gr.search(["hugging face"], K=10)[0]:
+    print(url, logp)
+```
+
+`infer_gpu.py` is a reference implementation: it recomputes the prompt for every beam at the second level and pads each batch to its
+longest prompt. On an idle H200 it answers a single query in 56 ms (median) and needs 10.1 ms per query in batches of 32.
+`infer_cpu.py` implements the same protocol on CPUs and reuses the prompt's key–value cache.
+
+Memory: about 9.7 GiB of GPU memory (bf16 weights and SID keys). Peak host RAM is about 23 GiB with `infer_cpu.py`, which keeps the weights and the table in memory.
+
+## Files
+
+| Path | Content | License |
+|---|---|---|
+| `*.safetensors`, `config.json`, `generation_config.json` | Model weights (bf16) | CC BY-NC 4.0 |
+| `tokenizer*.json`, `vocab.json`, `merges.txt` | Qwen3 tokenizer, unchanged | Apache 2.0 |
+| `sid_table/part-*.parquet` | One row per URL: `sid0`, `sid1` (int32), `url` (string, without scheme), sorted by `sid0 * 65536 + sid1`, unique | CC BY-NC 4.0 |
+| `infer_gpu.py`, `infer_cpu.py` | Inference code | Apache 2.0 |
+| `smoke_test.json` | 31 hand-picked queries with expected top-20 URLs, for `--check` | – |
+
+## Screening of the table
+
+Before release we removed every URL whose host, or a parent domain of it, is on the adult, malware, phishing, cryptojacking or
+stalkerware lists of the UT1 blacklists; every URL listed by URLhaus; and every URL whose text contains an e-mail address or a
+credential (5,310,702 URLs, 2.6%, mostly adult sites). Removing a row from the table removes the URL from every possible output, because
+decoding is constrained to the table. The tables were not checked against lists of child sexual abuse material, nor screened for
+personal pages; both are left to the takedown process.
+
+URL removal requests: TODO_CONTACT. Removals are published as versioned changes to `sid_table/`.
+
+## Intended use and limitations
+
+- Intended for research on generative retrieval and for navigational search over the URLs in the released table.
+- The model can only return URLs in the table. Pages that appeared after August 2026 are not covered, and adding a page requires
+  training on it.
+- It is weaker than dense retrieval on rarely visited pages, and it is not meant for informational queries.
+- Its knowledge reflects clicks up to August 2026 and the biases of the rankings under which those clicks were made.
+- It was trained on aggregated click data without user identifiers. It must not be used to infer anything about individual users.
+
+## License
+
+The model weights and the SID-to-URL table are released under [CC BY-NC 4.0](LICENSE) (non-commercial use). The inference code is
+released under the [Apache License 2.0](LICENSE-CODE). The model is derived from Qwen3-4B-Base, Copyright 2024 Alibaba Cloud, licensed
+under the Apache License 2.0 ([LICENSE-QWEN3](LICENSE-QWEN3)); see [NOTICE](NOTICE). Unlike the Octen-Embedding models, the WebGR
+weights are not licensed for commercial use; contact Octen for commercial licensing.
+
+## Citation
+
+```bibtex
+@techreport{octen2026webgr,
+  title       = {Octen-WebGR: Scaling Generative Retrieval to 200 Million Web Pages},
+  author      = {{Octen Team}},
+  institution = {Octen},
+  year        = {2026},
+  note        = {TODO_LINK}
+}
+```

config.json

@@ -0,0 +1,68 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.1",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 283008
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.57.1"
+}

infer_cpu.py

@@ -0,0 +1,165 @@
+# Copyright 2026 Octen. Licensed under the Apache License, Version 2.0 (see LICENSE-CODE).
+"""CPU inference for Octen-WebGR. Same protocol as infer_gpu.py (query normalisation, prompt, constrained beam search,
+scoring); it only removes two algorithmic redundancies of the reference implementation:
+
+  1. Level 0 runs the prompt once and keeps its KV cache; the K beams of level 1 reuse it and each feed one token
+     (the reference recomputes "prompt + SID token" K times, roughly 7-8x the compute).
+  2. Level-1 log_softmax is taken over the valid children only: sid_table/ is sorted by key = sid0 * 65536 + sid1,
+     so the valid children of prefix c are the contiguous rows [start[c], start[c+1]).
+
+Results match infer_gpu.py except for occasional swaps between candidates whose scores are nearly tied.
+
+  python infer_cpu.py --model Octen/Octen-WebGR-0.6B --query "youtube" "boeing internship" --threads 16
+  python infer_cpu.py --model /path/to/local/copy --check --threads 16
+"""
+import argparse, glob, json, os, time
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+BASE_V, V, MAX_Q = 151936, 65536, 32
+PAD = 151643                                   # <|endoftext|>, used only for left padding (masked out)
+
+
+def resolve(model):
+    """Local directory, or a Hugging Face repo id that is downloaded once into the HF cache."""
+    if os.path.isdir(model):
+        return model
+    from huggingface_hub import snapshot_download
+    return snapshot_download(model)
+
+
+def norm_query(q):
+    return " ".join(q.split()).lower()
+
+
+class URLs:
+    """URL column kept as one array per table file (no combined copy, so peak memory is one table); rows are global."""
+    def __init__(self, files):
+        self.parts = [pq.read_table(f, columns=["url"]).column("url").combine_chunks() for f in files]
+        self.start = np.cumsum([0] + [len(p) for p in self.parts])
+
+    def take(self, rows):
+        f = np.searchsorted(self.start, rows, side="right") - 1
+        return [self.parts[i][int(r - self.start[i])].as_py() for i, r in zip(f, rows)]
+
+
+class GRCPU:
+    def __init__(self, model, dtype=torch.bfloat16, logits_fp32=False):
+        """dtype: precision of the forward pass (bf16 matches the GPU evaluation; fp32 is slower).
+        logits_fp32: compute the lm_head step with fp32 weights (bf16 logits are spaced 0.125 apart at |logit| >= 16,
+        so nearly tied candidates can swap depending on the kernel)."""
+        self.root = resolve(model)
+        self.tok = AutoTokenizer.from_pretrained(self.root)
+        self.lm = AutoModelForCausalLM.from_pretrained(self.root, dtype=dtype, attn_implementation="sdpa").eval()
+        assert self.lm.config.vocab_size == BASE_V + 2 * V, "unexpected vocabulary size: should be 151936 + 2 x 65536"
+        W = self.lm.lm_head.weight.detach()
+        ldt = torch.float32 if logits_fp32 else dtype
+        self.W0 = W[BASE_V:BASE_V + V].to(ldt).contiguous()                  # the 65,536 rows of level 0
+        self.W1 = W[BASE_V + V:BASE_V + 2 * V].to(ldt).contiguous()          # the 65,536 rows of level 1
+        files = sorted(glob.glob(f"{self.root}/sid_table/part-*.parquet"))
+        t = pa.concat_tables([pq.read_table(f, columns=["sid0", "sid1"]) for f in files])
+        s0 = t.column("sid0").to_numpy().astype(np.int64)
+        s1 = t.column("sid1").to_numpy().astype(np.int64)
+        assert (np.diff(s0 * V + s1) > 0).all(), "sid_table must be sorted by key in strictly ascending order (one URL per SID)"
+        self.start = torch.from_numpy(np.searchsorted(s0, np.arange(V + 1)))  # valid children of prefix c = rows [start[c], start[c+1])
+        self.ok0 = self.start[1:] > self.start[:-1]                            # valid level-0 codes
+        self.sid1 = torch.from_numpy(s1)
+        self.url = URLs(files)
+        self.n = len(s0)
+
+    def prompts(self, qs):
+        qs = [norm_query(q) for q in qs]
+        qi = self.tok(qs, add_special_tokens=False)["input_ids"]
+        qs = [self.tok.decode(x[:MAX_Q]) if len(x) > MAX_Q else q for q, x in zip(qs, qi)]
+        return self.tok([f"Query: {q}\nURL:" for q in qs], add_special_tokens=False)["input_ids"]
+
+    @torch.inference_mode()
+    def search_rows(self, qs, K=20):
+        """-> (scores, rows) per query, sorted by score; rows index sid_table/."""
+        P = self.prompts(qs)
+        B, T = len(P), max(len(p) for p in P)
+        ids = torch.full((B, T), PAD, dtype=torch.long)
+        am = torch.zeros((B, T), dtype=torch.long)
+        for i, p in enumerate(P):                                            # left padding: the last position is each sequence's last token
+            ids[i, T - len(p):] = torch.tensor(p)
+            am[i, T - len(p):] = 1
+        out = self.lm.model(input_ids=ids, attention_mask=am, position_ids=(am.cumsum(1) - 1).clamp(min=0), use_cache=True)
+        lg0 = (out.last_hidden_state[:, -1].to(self.W0.dtype) @ self.W0.t()).float().masked_fill(~self.ok0, float("-inf"))
+        k0 = min(K, int(self.ok0.sum()))
+        s0, c0 = lg0.log_softmax(-1).topk(k0, dim=1)                          # [B, k0]
+        # Level 1: copy the KV cache per beam (the k0 beams of query b are adjacent); each beam feeds only its SID token
+        cache = out.past_key_values
+        cache.batch_repeat_interleave(k0)
+        am1 = torch.cat([am.repeat_interleave(k0, 0), torch.ones((B * k0, 1), dtype=torch.long)], 1)
+        pos1 = am.sum(1, keepdim=True).repeat_interleave(k0, 0)               # position of the new token = number of real tokens
+        h1 = self.lm.model(input_ids=(BASE_V + c0).reshape(-1, 1), attention_mask=am1, position_ids=pos1,
+                           past_key_values=cache, use_cache=True).last_hidden_state[:, -1]
+        lg1 = h1.to(self.W1.dtype) @ self.W1.t()                              # [B*k0, V]; only the valid children's columns are used
+        cc = c0.reshape(-1)
+        lo = self.start[cc]
+        n = self.start[cc + 1] - lo                                           # number of valid children per beam
+        r = torch.repeat_interleave(torch.arange(B * k0), n)                  # beam of each child
+        j = torch.arange(int(n.sum())) - torch.repeat_interleave(n.cumsum(0) - n, n) + lo[r]   # table row of each child
+        x = lg1[r, self.sid1[j]].float()
+        mx = torch.full((B * k0,), float("-inf")).scatter_reduce(0, r, x, "amax")
+        lse = mx + torch.zeros(B * k0).index_add_(0, r, (x - mx[r]).exp()).log()
+        sc = s0.reshape(-1)[r] + x - lse[r]                                   # sum of the two log-probabilities
+        off = [0] + n.view(B, k0).sum(1).cumsum(0).tolist()                    # children of one query are adjacent in sc
+        res = []
+        for b in range(B):
+            s, i = sc[off[b]:off[b + 1]].topk(min(K, off[b + 1] - off[b]))
+            res.append((s.numpy(), j[off[b] + i].numpy()))
+        return res
+
+    def search(self, qs, K=20):
+        """-> one list per query of (url, log_prob, table_row), sorted by score (same format as infer_gpu.GR.search)."""
+        return [list(zip(self.url.take(rr), s.tolist(), rr.tolist())) for s, rr in self.search_rows(qs, K)]
+
+
+def check(gr, bs=32):
+    """Compare against smoke_test.json (hand-picked queries; expected results from the released weights and table)."""
+    ref = json.load(open(f"{gr.root}/smoke_test.json"))
+    assert gr.n == ref["table_rows"], f"sid_table has {gr.n:,} rows, expected {ref['table_rows']:,}"
+    qs, want = [x["query"] for x in ref["cases"]], [x["top20"] for x in ref["cases"]]
+    ov, top1, n = [], 0, len(qs)
+    for i in range(0, n, bs):
+        for res, w in zip(gr.search(qs[i:i + bs], 20), want[i:i + bs]):
+            got = [u for u, *_ in res]
+            ov.append(len(set(got) & set(w)) / max(len(w), 1))
+            top1 += bool(got and w and got[0] == w[0])
+    mo, t1 = float(np.mean(ov)), top1 / n
+    print(f"{n} queries: mean top-20 overlap {mo:.4f}, top-1 agreement {t1:.4f}, identical top-20 {np.mean(np.array(ov) == 1):.3f}")
+    ok = mo >= 0.9 and t1 >= 0.9
+    print("PASS" if ok else "FAIL (small differences from kernels are expected, but overlap should be >= 0.9)")
+    return ok
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--model", default=os.path.dirname(os.path.abspath(__file__)), help="local directory or HF repo id")
+    ap.add_argument("--query", nargs="*", default=[])
+    ap.add_argument("--k", type=int, default=20)
+    ap.add_argument("--threads", type=int, default=0, help="0 = torch default")
+    ap.add_argument("--fp32", action="store_true", help="fp32 forward pass (default bf16, as in the GPU evaluation)")
+    ap.add_argument("--check", action="store_true")
+    a = ap.parse_args()
+    if a.threads:
+        torch.set_num_threads(a.threads)
+    t0 = time.time()
+    gr = GRCPU(a.model, dtype=torch.float32 if a.fp32 else torch.bfloat16)
+    print(f"Loaded {gr.n:,} candidate URLs, {torch.get_num_threads()} threads, {time.time() - t0:.0f}s", flush=True)
+    for q in a.query:
+        t = time.perf_counter()
+        res = gr.search([q], a.k)[0]
+        print(f"\n{q}  ({(time.perf_counter() - t) * 1000:.0f} ms)")
+        for i, (u, lp, _) in enumerate(res, 1):
+            print(f"  {i:2d}. {np.exp(lp):.3f}  {u}")
+    if a.check and not check(gr):
+        raise SystemExit(1)
+
+
+if __name__ == "__main__":
+    main()

infer_gpu.py

@@ -0,0 +1,174 @@
+# Copyright 2026 Octen. Licensed under the Apache License, Version 2.0 (see LICENSE-CODE).
+"""Reference GPU inference for Octen-WebGR: query -> top-K URLs by constrained beam search over two semantic-ID tokens.
+
+Inference protocol (identical to the evaluation in the technical report):
+  1. Normalise the query: collapse Unicode whitespace to one space, strip, lowercase.
+  2. Keep at most the first 32 tokens of the query (decoded back to a string before building the prompt).
+  3. Prompt = "Query: {q}\\nURL:", tokenised without special tokens.
+  4. Level t (t = 0, 1) of the SID is vocabulary token 151936 + t * 65536 + code; only those 65,536 rows of
+     lm_head are scored at each level.
+  5. Constrained beam search: only prefixes present in sid_table/ are allowed, and log_softmax is taken over the
+     valid children only. K beams are kept at each level (default 20).
+  6. Score = sum of the two log-probabilities (exp(score) = the model's probability of the URL among valid SIDs).
+     (sid0, sid1) is looked up in sid_table/ to get the URL.
+
+This implementation recomputes the prompt for every beam at level 1 and pads each batch to its longest prompt;
+it favours clarity over speed. infer_cpu.py reuses the prompt's KV cache instead.
+
+  python infer_gpu.py --model Octen/Octen-WebGR-4B --query "youtube" "boeing internship"
+  python infer_gpu.py --model /path/to/local/copy --check      # compare against smoke_test.json
+"""
+import argparse, glob, json, os, time
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+BASE_V, V, L, MAX_Q = 151936, 65536, 2, 32
+PAD = 151643                                   # <|endoftext|>, used only for left padding (masked out)
+
+
+def resolve(model):
+    """Local directory, or a Hugging Face repo id that is downloaded once into the HF cache."""
+    if os.path.isdir(model):
+        return model
+    from huggingface_hub import snapshot_download
+    return snapshot_download(model)
+
+
+def norm_query(q):
+    return " ".join(q.split()).lower()
+
+
+class URLs:
+    """URL column kept as one array per table file (no combined copy, so peak memory is one table); rows are global."""
+    def __init__(self, files):
+        self.parts = [pq.read_table(f, columns=["url"]).column("url").combine_chunks() for f in files]
+        self.start = np.cumsum([0] + [len(p) for p in self.parts])
+
+    def take(self, rows):
+        f = np.searchsorted(self.start, rows, side="right") - 1
+        return [self.parts[i][int(r - self.start[i])].as_py() for i, r in zip(f, rows)]
+
+
+class GR:
+    def __init__(self, model, device="cuda"):
+        self.root = resolve(model)
+        self.dev = device
+        self.dev_type = torch.device(device).type
+        self.tok = AutoTokenizer.from_pretrained(self.root)
+        attn = "sdpa"
+        if self.dev_type == "cuda":
+            try:
+                import flash_attn  # noqa: F401
+                attn = "flash_attention_2"
+            except ImportError:
+                pass
+        self.lm = AutoModelForCausalLM.from_pretrained(self.root, dtype=torch.bfloat16, attn_implementation=attn).to(device).eval()
+        assert self.lm.config.vocab_size == BASE_V + L * V, "unexpected vocabulary size: should be 151936 + 2 x 65536"
+        self.W = self.lm.lm_head.weight
+        files = sorted(glob.glob(f"{self.root}/sid_table/part-*.parquet"))
+        t = pa.concat_tables([pq.read_table(f, columns=["sid0", "sid1"]) for f in files])
+        key = t.column("sid0").to_numpy().astype(np.int64) * V + t.column("sid1").to_numpy().astype(np.int64)
+        assert (np.diff(key) > 0).all(), "sid_table must be sorted by key in strictly ascending order (one URL per SID)"
+        self.keys = torch.from_numpy(key).to(device)                         # row number = position in keys
+        ok0 = np.zeros(V, bool)
+        ok0[np.unique(key // V)] = True
+        self.ok0 = torch.from_numpy(ok0).to(device)                          # valid level-0 codes
+        self.url = URLs(files)
+        self.n = len(key)
+
+    def prompts(self, qs):
+        qs = [norm_query(q) for q in qs]
+        qi = self.tok(qs, add_special_tokens=False)["input_ids"]
+        qs = [self.tok.decode(x[:MAX_Q]) if len(x) > MAX_Q else q for q, x in zip(qs, qi)]
+        return self.tok([f"Query: {q}\nURL:" for q in qs], add_special_tokens=False)["input_ids"]
+
+    def last_hidden(self, seqs):
+        P = max(len(s) for s in seqs)
+        ids = torch.full((len(seqs), P), PAD, dtype=torch.long)
+        am = torch.zeros((len(seqs), P), dtype=torch.long)
+        for i, s in enumerate(seqs):                                         # left padding: the last position is each sequence's last token
+            ids[i, P - len(s):] = torch.tensor(s)
+            am[i, P - len(s):] = 1
+        ids, am = ids.to(self.dev), am.to(self.dev)
+        pos = (am.cumsum(1) - 1).clamp(min=0)
+        return self.lm.model(input_ids=ids, attention_mask=am, position_ids=pos).last_hidden_state[:, -1]
+
+    @torch.no_grad()
+    def search(self, qs, K=20):
+        """-> one list per query of (url, log_prob, table_row), sorted by score."""
+        with torch.autocast(self.dev_type, dtype=torch.bfloat16):
+            P = self.prompts(qs)
+            B = len(P)
+            ninf = float("-inf")
+            lg0 = (self.last_hidden(P) @ self.W[BASE_V:BASE_V + V].t()).float()                  # [B, V]
+            lp0 = lg0.masked_fill(~self.ok0, ninf).log_softmax(-1).masked_fill(~self.ok0, ninf)
+            k0 = min(K, int(self.ok0.sum()))
+            s0, c0 = lp0.topk(k0, dim=1)                                                            # [B, k0]
+            # Level 1: one sequence per (query, level-0 candidate) = prompt + that SID token
+            P1 = [p + [BASE_V + c] for p, cs in zip(P, c0.tolist()) for c in cs]
+            lg1 = (self.last_hidden(P1) @ self.W[BASE_V + V:BASE_V + 2 * V].t()).float()          # [B*k0, V]
+            cc = c0.reshape(-1)
+            lo = torch.searchsorted(self.keys, cc * V)
+            n = torch.searchsorted(self.keys, (cc + 1) * V) - lo                                    # number of valid children per prefix
+            r = torch.repeat_interleave(torch.arange(len(cc), device=self.dev), n)
+            j = torch.arange(int(n.sum()), device=self.dev) - torch.repeat_interleave(n.cumsum(0) - n, n) + lo[r]
+            ok1 = torch.zeros_like(lg1, dtype=torch.bool)
+            ok1[r, self.keys[j] - cc[r] * V] = True
+            lp1 = lg1.masked_fill(~ok1, ninf).log_softmax(-1).masked_fill(~ok1, ninf)
+            cand = (s0.reshape(-1, 1) + lp1).view(B, k0 * V)
+            s, flat = cand.topk(min(K, k0 * V), dim=1)
+            key = c0.gather(1, flat // V) * V + flat % V
+            row = torch.searchsorted(self.keys, key)
+        s, row = s.float().cpu().numpy(), row.cpu().numpy()
+        out = []
+        for b in range(B):
+            m = np.isfinite(s[b])
+            rr = row[b][m]
+            out.append(list(zip(self.url.take(rr), s[b][m].tolist(), rr.tolist())))
+        return out
+
+
+def check(gr, bs=32):
+    """Compare against smoke_test.json (hand-picked queries; expected results from the released weights and table)."""
+    ref = json.load(open(f"{gr.root}/smoke_test.json"))
+    assert gr.n == ref["table_rows"], f"sid_table has {gr.n:,} rows, expected {ref['table_rows']:,}"
+    qs, want = [x["query"] for x in ref["cases"]], [x["top20"] for x in ref["cases"]]
+    ov, top1, n = [], 0, len(qs)
+    for i in range(0, n, bs):
+        for res, w in zip(gr.search(qs[i:i + bs], 20), want[i:i + bs]):
+            got = [u for u, *_ in res]
+            ov.append(len(set(got) & set(w)) / max(len(w), 1))
+            top1 += bool(got and w and got[0] == w[0])
+    mo, t1 = float(np.mean(ov)), top1 / n
+    print(f"{n} queries: mean top-20 overlap {mo:.4f}, top-1 agreement {t1:.4f}, identical top-20 {np.mean(np.array(ov) == 1):.3f}")
+    ok = mo >= 0.9 and t1 >= 0.9
+    print("PASS" if ok else "FAIL (small differences from batching and kernels are expected, but overlap should be >= 0.9)")
+    return ok
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--model", default=os.path.dirname(os.path.abspath(__file__)), help="local directory or HF repo id")
+    ap.add_argument("--query", nargs="*", default=[])
+    ap.add_argument("--k", type=int, default=20)
+    ap.add_argument("--device", default="cuda")
+    ap.add_argument("--check", action="store_true")
+    a = ap.parse_args()
+    t0 = time.time()
+    gr = GR(a.model, a.device)
+    print(f"Loaded {gr.n:,} candidate URLs in {time.time() - t0:.0f}s", flush=True)
+    for q in a.query:
+        t = time.perf_counter()
+        res = gr.search([q], a.k)[0]
+        print(f"\n{q}  ({(time.perf_counter() - t) * 1000:.0f} ms)")
+        for i, (u, lp, _) in enumerate(res, 1):
+            print(f"  {i:2d}. {np.exp(lp):.3f}  {u}")
+    if a.check and not check(gr):
+        raise SystemExit(1)
+
+
+if __name__ == "__main__":
+    main()

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requirements.txt

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+# Versions the release was validated with. infer_cpu.py relies on DynamicCache.batch_repeat_interleave (transformers 4.x).
+torch==2.8.0
+transformers==4.57.1
+pyarrow>=14
+numpy>=1.26
+huggingface_hub>=0.26
+# Optional, GPU only: flash-attn==2.8.3 (infer_gpu.py falls back to SDPA without it)

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smoke_test.json

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+{
+ "model": "Octen-WebGR-4B",
+ "table_rows": 195921090,
+ "note": "Hand-picked queries. Expected results were produced by infer_cpu.py (bf16, batches of 8) on the released weights and table. Any implementation may swap nearly tied candidates (GPU attention kernel, CPU thread count, batch composition), so --check requires mean top-20 overlap and top-1 agreement >= 0.9 rather than identical lists.",
+ "cases": [
+  {
+   "query": "youtube",
+   "top20": [
+    "youtube.com",
+    "en.wikipedia.org/wiki/YouTube",
+    "youtube.com/user/YouTube",
+    "music.youtube.com",
+    "youtube.com/feed/history",
+    "youtube.com/YouTubeJapan",
+    "youtube.com/index",
+    "youtube.com/feed/library",
+    "youtube.com/channel/UCqVDpXKLmKeBU_yyt_QkItQ",
+    "support.google.com/youtube?hl=en",
+    "youtube.com/example",
+    "youtube.com/user/YouTube/videos",
+    "facebook.com/youtube",
+    "es.wikipedia.org/wiki/YouTube",
+    "play.google.com/store/apps/details?hl=en_US&id=com.google.android.youtube",
+    "studio.youtube.com",
+    "youtube.jp",
+    "about.youtube",
+    "it.wikipedia.org/wiki/YouTube",
+    "support.google.com/youtube/answer/155562?hl=pt-BR"
+   ]
+  },
+  {
+   "query": "github",
+   "top20": [
+    "github.com",
+    "en.wikipedia.org/wiki/GitHub",
+    "github.co.jp",
+    "github.com/login",
+    "github.com/signup",
+    "github.com/explore",
+    "youtube.com/watch?v=pJYOG6klqj8",
+    "pages.github.com",
+    "youtube.com/watch?v=h2a3Kw-I_Ec",
+    "github.blog",
+    "github.com/features/copilot",
+    "play.google.com/store/apps/details?id=com.github.android",
+    "youtube.com/watch?v=a9u2yZvsqHA",
+    "git-scm.com",
+    "ja.wikipedia.org/wiki/GitHub",
+    "youtube.com/watch?v=2SgI1kAczfk",
+    "es.wikipedia.org/wiki/GitHub",
+    "play.google.com/store/apps/details?hl=en_IN&id=com.github.android",
+    "desktop.github.com/download",
+    "github.com/github"
+   ]
+  },
+  {
+   "query": "wikipedia",
+   "top20": [
+    "wikipedia.org",
+    "es.wikipedia.org/wiki/Wikipedia:Portada",
+    "it.wikipedia.org/wiki/Pagina_principale",
+    "pt.wikipedia.org/wiki/Wikipédia:Página_principal",
+    "en.wikipedia.org/wiki/Main_Page",
+    "fr.wikipedia.org/wiki/Wikipédia:Accueil_principal",
+    "ja.wikipedia.org/wiki/メインページ",
+    "de.wikipedia.org/wiki/Wikipedia:Hauptseite",
+    "pl.wikipedia.org/wiki/Wikipedia:Strona_główna",
+    "vi.wikipedia.org/wiki/Trang_Chính",
+    "nl.wikipedia.org/wiki/Hoofdpagina",
+    "es.wikipedia.org",
+    "tr.wikipedia.org/wiki/Anasayfa",
+    "id.wikipedia.org/wiki/Wikipedia",
+    "wikipedia.nl",
+    "sv.wikipedia.org/wiki/Portal:Huvudsida",
+    "en.wikipedia.org/wiki/Wiki",
+    "id.wikipedia.org/wiki/Halaman_Utama",
+    "cs.wikipedia.org/wiki/Hlavní_strana",
+    "es.wikipedia.org/wiki/Wikipedia"
+   ]
+  },
+  {
+   "query": "amazon",
+   "top20": [
+    "amazon.com",
+    "amazon.fr",
+    "amazon.co.jp",
+    "amazon.com/amz-books/store",
+    "amazon.co.uk",
+    "amazon.com.br",
+    "amazon.it",
+    "amazon.de",
+    "amazon.in",
+    "amazon.com/gp/video/storefront",
+    "amazon.ca",
+    "amazon.com.au",
+    "amazon.es",
+    "hiring.amazon.com",
+    "amazon.com/-/es",
+    "amazon.com/amazonprime",
+    "en.wikipedia.org/wiki/Amazon_(company)",
+    "amazon.jobs",
+    "amazon.com.mx",
+    "amazon.com.be"
+   ]
+  },
+  {
+   "query": "facebook",
+   "top20": [
+    "m.facebook.com",
+    "facebook.com",
+    "facebook.com?locale=es_LA",
+    "facebook.com?locale=pl_PL",
+    "m.facebook.com/login",
+    "m.facebook.com/reg",
+    "en.wikipedia.org/wiki/Facebook",
+    "facebook.com?locale=it_IT",
+    "facebook.com?locale=fr_FR",
+    "facebook.com?locale=es_ES",
+    "facebook.com?locale=vi_VN",
+    "facebook.com/facebook",
+    "facebook.com?locale=th_TH",
+    "facebook.com?locale=de_DE",
+    "facebook.com?locale=en_GB",
+    "m.facebook.com/watch",
+    "facebook.com/marketplace",
+    "facebook.com?locale=ar_AR",
+    "facebook.com?locale=ja_JP",
+    "facebook.com?locale=hr_HR"
+   ]
+  },
+  {
+   "query": "gmail",
+   "top20": [
+    "mail.google.com/mail/u/0",
+    "google.com/gmail/about/signup_complete.html",
+    "workspace.google.com/intl/es-419/gmail",
+    "workspace.google.com/intl/it/gmail",
+    "workspace.google.com/intl/fr/gmail",
+    "workspace.google.com/intl/es/gmail",
+    "mail.google.com",
+    "support.google.com/mail/answer/56256?hl=en",
+    "workspace.google.com/gmail",
+    "google.com/gmail",
+    "accounts.google.com/login?hl=tr&service=mail",
+    "accounts.google.com/ServiceLogin?hl=es&service=mail",
+    "workspace.google.com/intl/es-419_mx/gmail",
+    "workspace.google.com/intl/de/gmail",
+    "accounts.google.com/v3/signin/identifier?dsh=S1812573153:1655944654029516&flowEntry=ServiceLogin&flowName=WebLiteSignIn&ifkv=AX3vH39E0iYVTmn-NoMNM_C35EPrno8LWsRx2Qhr0HApkVLZ-Zc_Vql8ouaSQOiXzEmthrpOPAV5",
+    "workspace.google.com/intl/pt-BR/gmail",
+    "accounts.google.com/ServiceLogin?hl=fr&service=mail",
+    "workspace.google.com/intl/pl/gmail",
+    "mail.google.com/mail",
+    "workspace.google.com/intl/ru/gmail"
+   ]
+  },
+  {
+   "query": "google maps",
+   "top20": [
+    "maps.google.com",
+    "google.com/maps",
+    "earth.google.com/web",
+    "accounts.google.com/login?hl=en-GB&lp=1&service=local",
+    "developers.google.com/maps",
+    "en.wikipedia.org/wiki/Google_Maps",
+    "maps.google.com/maps/dir",
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+    "play.google.com/store/apps/details?hl=ja&id=jp.co.yahoo.android.yjtop",
+    "shopping.yahoo.co.jp",
+    "play.google.com/store/apps/details?id=jp.co.yahoo.android.yjtop",
+    "apps.apple.com/jp/app/yahoo-japan/id299147843",
+    "m.yahoo.co.jp/tools",
+    "ja.wikipedia.org/wiki/Yahoo!_JAPAN",
+    "facebook.com/yahoojapan",
+    "apps.apple.com/jp/app/yahoo-乗換案内/id291676451",
+    "search.yahoo.co.jp",
+    "youtube.com/@YahooJAPANPR",
+    "finance.yahoo.co.jp",
+    "support.yahoo-net.jp/PccLogin/s",
+    "services.yahoo.co.jp",
+    "play.google.com/store/apps/details?hl=ja&id=jp.co.yahoo.android.finance",
+    "en.wikipedia.org/wiki/Yahoo_Japan",
+    "mail.yahoo.co.jp/promo"
+   ]
+  },
+  {
+   "query": "mercado libre",
+   "top20": [
+    "mercadolibre.com.ar",
+    "mercadolibre.com.mx",
+    "mercadolibre.com.co",
+    "mercadolibre.com.pe",
+    "mercadolibre.com.ve",
+    "es.wikipedia.org/wiki/MercadoLibre",
+    "mercadolibre.cl",
+    "mercadolibre.com.mx/ofertas",
+    "es.wikipedia.org/wiki/Mercado_Libre",
+    "mercadolibre.com.uy",
+    "instagram.com/mercadolibre.arg?hl=es-la",
+    "mercadolibre.com.ec",
+    "instagram.com/mercadolibre.mex?hl=es",
+    "careers-meli.mercadolibre.com",
+    "mercadolibre.com.ar/l/emprendedores",
+    "mercadolibre.com",
+    "listado.mercadolibre.com.mx/tepotzotlan",
+    "en.wikipedia.org/wiki/MercadoLibre",
+    "mercadolibre.com.ar/c/autos-motos-y-otros",
+    "facebook.com/mex.mercadolibre?locale=es_LA"
+   ]
+  },
+  {
+   "query": "boeing internship",
+   "top20": [
+    "jobs.boeing.com/internships",
+    "jobs.boeing.com/finance-internships",
+    "au.prosple.com/graduate-employers/boeing-australia/jobs-internships",
+    "jobs.boeing.com/software-engineering-internships",
+    "jobs.boeing.com/employment/australia-internship-jobs/185-18469/9287/2077456/2",
+    "jobs.boeing.com/category/internship-jobs/185/9287/1",
+    "boehringer-ingelheim.com/us/careers/discover-our-career-areas/summer-internship-program",
+    "reddit.com/r/boeing/comments/1rv3udi/applied_to_boeing_summer_2026_internship_still",
+    "careers.bankofamerica.com/en-us/students/programs/sustainable-finance-research",
+    "reddit.com/r/boeing/comments/1cvdzmj/advice_on_getting_an_internship",
+    "jobs.boeing.com/entry-level",
+    "reddit.com/r/boeing/comments/1kmvgq3/maximizing_chance_to_obtain_a_fulltime_offer",
+    "aci.aero/wp-content/uploads/2022/04/Aiport-Internship-Summer-2022-Job-Announcement_Final.pdf",
+    "au.prosple.com/graduate-employers/boeing-australia/jobs-internships/engineering-university-summer-internship-2",
+    "jobs.boeing.com/BTAP",
+    "jobs.boeing.com/location/india-country-jobs/185/1269750/2",
+    "search-careers.gm.com/en/gm-apprenticeship-program",
+    "extern.com/post/boeing-internship-guide",
+    "boq.com.au/about-us/careers/internship-program",
+    "jobs.baesystems.com/global/en/studentsandgraduates"
+   ]
+  }
+ ]
+}

tokenizer_config.json

@@ -0,0 +1,239 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# 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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set content = message.content %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in message.content %}\n                {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n                {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}