commit 71a5ff9c45f5dd58e8683c27e62d8c1d3327f411
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Tue Jul 28 20:09:19 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: rockerritesh/r1-distill-qwen7b-offline
    Source: Original Platform

diff --git a/.gitattributes b/.gitattributes
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diff --git a/README.md b/README.md
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+---
+license: mit
+---
+
+# R1-Distill-Qwen-7B-AWQ — offline inference bundle
+
+AWQ 4-bit reasoning model with a self-contained script.py that runs fully offline (loads from `.`). Reasons step by step, then emits a final JSON array. Fits a 16 GB GPU.
diff --git a/config.json b/config.json
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+{
+  "_name_or_path": "/root/.cache/huggingface/hub/models--deepseek-ai--DeepSeek-R1-Distill-Qwen-7B/snapshots/008b8c2e0b59dac9b7619d58a5ad609f43a5b6b1",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "quantization_config": {
+    "bits": 4,
+    "group_size": 128,
+    "modules_to_not_convert": null,
+    "quant_method": "awq",
+    "version": "gemm",
+    "zero_point": true
+  },
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.47.1",
+  "use_cache": false,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}
diff --git a/generation_config.json b/generation_config.json
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--- /dev/null
+++ b/generation_config.json
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+{
+  "_from_model_config": true,
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": 151643,
+  "transformers_version": "4.47.1",
+  "use_cache": false
+}
diff --git a/harness_cfg.json b/harness_cfg.json
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+{"cot": true, "max_new_cap": 1536}
\ No newline at end of file
diff --git a/iol_harness.py b/iol_harness.py
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+++ b/iol_harness.py
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+"""Pure-Python harness logic for the IOL-AI 2026 submission.
+
+Kept free of torch/transformers so it can be unit-tested locally without a GPU
+or the model. ``script.py`` (which runs on the T4) imports from here; so does
+``test_parsing.py``. Both this file and ``script.py`` are uploaded to the
+submission repo, so ``import iol_harness`` resolves at run time (the repo root is
+the working directory and on sys.path[0]).
+"""
+
+import ast
+import json
+import re
+
+# --------------------------------------------------------------------------- #
+# Item counting
+# --------------------------------------------------------------------------- #
+
+# "17. ", "18) ", ... anywhere after start/space/comma/semicolon, followed by
+# whitespace or end-of-line. Requires whitespace after the . or ) so decimals
+# like "3.14" are NOT matched.
+_NUMBERED = re.compile(r"(?:^|[\s,;])(\d{1,3})[.)](?=\s|$)", re.MULTILINE)
+# "(1)", "(2)" — parenthesised single number. "(1-2)" is NOT matched.
+_PAREN = re.compile(r"\((\d{1,3})\)")
+# Explicit item range in the query, e.g. "(1-9)", "(13–16)", "(1—10)".
+_RANGE = re.compile(r"\((\d{1,3})\s*[-–—]\s*(\d{1,3})\)")
+
+
+def _distinct(rx, text):
+    return sorted(set(int(x) for x in rx.findall(text or "")))
+
+
+def _range_count(query):
+    """Count from an explicit "(a-b)" range in the query, else 0."""
+    best = 0
+    for a, b in _RANGE.findall(query or ""):
+        a, b = int(a), int(b)
+        if b >= a:
+            best = max(best, b - a + 1)
+    return best
+
+
+def _item_lines(query):
+    """Non-empty lines after the first non-empty (instruction) line."""
+    lines = [ln for ln in (query or "").splitlines() if ln.strip()]
+    return max(0, len(lines) - 1)
+
+
+def count_items(context, query, task_type=""):
+    """How many answers the problem expects (validated on real Linguini data).
+
+    Items live in different places by task type: numbered/bare lines in the
+    query, blanks "(N)" or a numbered list in the context, or an explicit
+    "(a-b)" range in the query. Returns an int >= 1. Biased so that when in
+    doubt it does not under-count (the scorer ignores extra predictions but
+    zero-scores any item a short prediction fails to cover).
+    """
+    tt = (task_type or "").strip()
+
+    # 1. An explicit range in the query is the strongest, cleanest signal.
+    r = _range_count(query)
+    if r:
+        return r
+
+    qn, qp = _distinct(_NUMBERED, query), _distinct(_PAREN, query)
+    cn, cp = _distinct(_NUMBERED, context), _distinct(_PAREN, context)
+    il = _item_lines(query)
+
+    if tt in ("text_to_num", "num_to_text"):
+        # numbers/words listed as bare lines after the instruction line
+        return max(il, len(qn), len(qp), 1)
+    if tt == "match_letters":
+        # items numbered in the query if present, else numbered in the context
+        return len(qn) or len(cn) or il or 1
+    if tt == "fill_blanks":
+        return max(len(qp), len(qn), len(cp), il, 1)
+    # translation / default
+    n = max(len(qn), len(qp))
+    if n <= 1:
+        # bare item lines in the query, else blanks enumerated in the context
+        n = il if il >= 1 else max(len(cp), 1)
+    return max(n, 1)
+
+
+# --------------------------------------------------------------------------- #
+# Prompt building (task-type aware)
+# --------------------------------------------------------------------------- #
+
+SYSTEM_BASE = (
+    "You are an expert solver of International Linguistics Olympiad (IOL) "
+    "problems. Every problem is fully self-contained: use ONLY the data, "
+    "examples and hints given to deduce the grammar, vocabulary and rules of "
+    "the language. Never rely on outside knowledge of the language — work the "
+    "pattern out from the given data. First reason it out silently, then answer "
+    "every numbered item.\n"
+    "OUTPUT FORMAT: respond with ONLY a JSON array of strings — one string per "
+    "numbered item, in the SAME order as the query. No keys, no numbering, no "
+    "commentary, nothing outside the array."
+)
+
+_TASK_GUIDANCE = {
+    "translation": (
+        "Each answer is the translation of that item, using the vocabulary and "
+        "grammar you deduced from the data. Answer in the language the query "
+        "names: 'into English' -> English; 'into <language>' -> that language. "
+        "Output only the translated text."
+    ),
+    "fill_blanks": (
+        "Work out the rule from the complete rows, then fill each blank with the "
+        "missing form IN THE LANGUAGE BEING ANALYSED (the non-English / target "
+        "language) — never its English meaning. Output only that form."
+    ),
+    "match_letters": (
+        "For each numbered item, output ONLY the single UPPERCASE LETTER "
+        "(A, B, C, ...) of its correct match. Never output the matched word, its "
+        "translation, or any text other than the letter."
+    ),
+    "text_to_num": (
+        "Work out the number system from the examples, then COMPUTE each value "
+        'and write it in digits (e.g. "285"). Do NOT copy a number from the '
+        "examples — derive the value for each new item. Output only the digits."
+    ),
+    "num_to_text": (
+        "Work out the number system from the examples, then CONSTRUCT each number "
+        "as words in the target (task) language using those rules. Do NOT copy an "
+        "example — build the form for the given number. Output only those words."
+    ),
+}
+_TASK_GUIDANCE_DEFAULT = (
+    "Give exactly what each numbered item asks for, in the form the query "
+    "requests."
+)
+
+
+def task_guidance(task_type):
+    return _TASK_GUIDANCE.get((task_type or "").strip(), _TASK_GUIDANCE_DEFAULT)
+
+
+SYSTEM_COT = (
+    "You are an expert solver of International Linguistics Olympiad (IOL) "
+    "problems. Every problem is self-contained: use ONLY the given data to deduce "
+    "the language's rules — never outside knowledge of the language. Reason "
+    "step by step to work out the pattern, then give your answers.\n"
+    "FINAL LINE: after your reasoning, output ONLY a JSON array of strings — one "
+    "per numbered item, in order — as the very last thing in your reply."
+)
+
+
+def build_messages(context, query, task_type, n, cot=False):
+    """Return chat messages. ``n`` = required answer count. ``cot`` = let the
+    model reason before emitting the final JSON array (for reasoning models)."""
+    base = SYSTEM_COT if cot else SYSTEM_BASE
+    system = (
+        f"{base}\n{task_guidance(task_type)}\n"
+        f"The JSON array must have EXACTLY {n} string{'s' if n != 1 else ''}."
+    )
+    tail = (
+        f"Answer all {n} item{'s' if n != 1 else ''} as a JSON array of "
+        f"{n} string{'s' if n != 1 else ''}, in order"
+        + (", after your step-by-step reasoning." if cot else ".")
+    )
+    user = f"{(context or '').strip()}\n\n{(query or '').strip()}\n\n{tail}"
+    return [
+        {"role": "system", "content": system},
+        {"role": "user", "content": user},
+    ]
+
+
+def max_new_tokens_for(task_type, n, cot=False, cap=1024):
+    """Heuristic generation budget. ``cot`` adds room for reasoning (bounded by
+    ``cap`` to protect the 30-min limit on a T4)."""
+    per_item = 96 if (task_type or "") == "translation" else 40
+    answer_room = 160 + per_item * max(1, n)
+    if cot:
+        return int(min(cap, answer_room + 640))
+    return int(min(cap, answer_room))
+
+
+# --------------------------------------------------------------------------- #
+# Answer parsing (robust, multi-layer)
+# --------------------------------------------------------------------------- #
+
+_FENCE = re.compile(r"```(?:json|python)?\s*(.*?)```", re.DOTALL | re.IGNORECASE)
+_LINE_NUM = re.compile(r"^\s*[\(\[]?(\d{1,3})[\).\]:]\s*(.*\S)?\s*$")
+_PREFIX = re.compile(r"^\s*(?:answers?|output|result)\s*[:\-]\s*", re.IGNORECASE)
+
+
+def _to_str(x):
+    if x is None:
+        return ""
+    if isinstance(x, (list, tuple)):
+        # A nested item (e.g. multiple accepted forms) — join readably.
+        return " ".join(_to_str(e) for e in x)
+    return str(x)
+
+
+def _strip_quotes(s):
+    s = s.strip()
+    if len(s) >= 2 and s[0] == s[-1] and s[0] in "\"'`":
+        s = s[1:-1].strip()
+    return s
+
+
+def _clean(s):
+    return _strip_quotes(_to_str(s).strip())
+
+
+def _strip_fences(text):
+    m = _FENCE.search(text or "")
+    return m.group(1) if m else (text or "")
+
+
+def _balanced_arrays(text):
+    """Yield every top-level [...] substring (handles nesting)."""
+    depth, start = 0, -1
+    for i, ch in enumerate(text):
+        if ch == "[":
+            if depth == 0:
+                start = i
+            depth += 1
+        elif ch == "]" and depth > 0:
+            depth -= 1
+            if depth == 0 and start >= 0:
+                yield text[start : i + 1]
+
+
+def extract_json_array(text):
+    """Return the LAST bracketed list that parses, as a list of strings, else None.
+
+    'Last' matters for reasoning models: they emit <think>...</think> (often
+    containing brackets) and then the final answer array — we want that final one.
+    """
+    if not text:
+        return None
+    for frag in reversed(list(_balanced_arrays(text))):
+        for parser in (json.loads, ast.literal_eval):
+            try:
+                value = parser(frag)
+            except Exception:
+                continue
+            if isinstance(value, list):
+                return [_clean(x) for x in value]
+    return None
+
+
+def parse_numbered_lines(text):
+    """Parse 'N. answer' / 'N) answer' / '(N) answer' lines -> ordered answers."""
+    found = {}
+    for line in (text or "").splitlines():
+        m = _LINE_NUM.match(line)
+        if m and m.group(2):
+            found[int(m.group(1))] = _clean(m.group(2))
+    if not found:
+        return []
+    return [found[k] for k in sorted(found)]
+
+
+def _fit(values, n):
+    """Force ``values`` to exactly ``n`` entries (truncate / pad with '')."""
+    values = list(values)[:n]
+    values += [""] * (n - len(values))
+    return values
+
+
+def parse_answers(text, n):
+    """Turn raw model output into exactly ``n`` cleaned answer strings.
+
+    Layers: JSON array -> numbered lines -> plain non-empty lines. Always
+    returns a list of length ``n``; extras are dropped, shortfalls padded so no
+    item is silently missing (the scorer aligns predictions by position).
+    """
+    n = max(1, int(n))
+    inner = _strip_fences(text)
+    # Reasoning models wrap their scratch-work in <think>...</think>; the answer
+    # follows the closing tag. Keep only what comes after it.
+    if "</think>" in inner:
+        inner = inner.rsplit("</think>", 1)[1]
+
+    arr = extract_json_array(inner)
+    if arr:
+        return _fit(arr, n)
+
+    numbered = parse_numbered_lines(inner)
+    if numbered:
+        return _fit(numbered, n)
+
+    lines = [_clean(_PREFIX.sub("", ln)) for ln in inner.splitlines()]
+    lines = [ln for ln in lines if ln]
+    if lines:
+        return _fit(lines, n)
+
+    single = _clean(inner)
+    return _fit([single] if single else [], n)
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+    "model.layers.27.self_attn.k_proj.qzeros": "model-00001-of-00002.safetensors",
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+    "model.layers.27.self_attn.v_proj.qweight": "model-00001-of-00002.safetensors",
+    "model.layers.27.self_attn.v_proj.qzeros": "model-00001-of-00002.safetensors",
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+    "model.layers.27.mlp.gate_proj.scales": "model-00001-of-00002.safetensors",
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+    "model.layers.27.mlp.up_proj.qzeros": "model-00001-of-00002.safetensors",
+    "model.layers.27.mlp.up_proj.scales": "model-00001-of-00002.safetensors",
+    "model.layers.27.mlp.down_proj.qweight": "model-00001-of-00002.safetensors",
+    "model.layers.27.mlp.down_proj.qzeros": "model-00001-of-00002.safetensors",
+    "model.layers.27.mlp.down_proj.scales": "model-00001-of-00002.safetensors",
+    "model.layers.27.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.27.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.norm.weight": "model-00001-of-00002.safetensors",
+    "lm_head.weight": "model-00002-of-00002.safetensors"
+  }
+}
\ No newline at end of file
diff --git a/script.py b/script.py
new file mode 100644
index 0000000..36a9895
--- /dev/null
+++ b/script.py
@@ -0,0 +1,117 @@
+"""IOL-AI 2026 submission entrypoint.
+
+Runs on the platform's T4 (16 GB), offline, within a 30-minute limit. Reads the
+hidden test set from /tmp/data/test.csv and writes submission.csv (id,pred) to
+the working directory. Model weights ship inside this repo and load from ".".
+
+Baseline model: Qwen/Qwen2.5-1.5B-Instruct. The score lever here is the harness
+(task-aware prompts + robust parsing) in iol_harness.py, not the model size.
+"""
+
+import os
+import time
+
+# Must be set BEFORE importing transformers: no network at run time.
+os.environ["HF_HUB_OFFLINE"] = "1"
+os.environ["TRANSFORMERS_OFFLINE"] = "1"
+os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
+
+import json
+
+import pandas as pd
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+import iol_harness as H
+
+MODEL_ID = "."  # weights are shipped in this repo (offline)
+TEST_CSV = "/tmp/data/test.csv"
+OUT_CSV = "submission.csv"
+
+TIME_LIMIT_S = 30 * 60
+SAFETY_S = 26 * 60  # stop generating past this; still write what we have
+_START = time.monotonic()
+
+# Optional per-repo harness config (reasoning-model repos ship one to turn on CoT).
+CFG = {}
+if os.path.exists("harness_cfg.json"):
+    try:
+        CFG = json.load(open("harness_cfg.json"))
+    except Exception:
+        CFG = {}
+COT = bool(CFG.get("cot", False))
+MAX_CAP = int(CFG.get("max_new_cap", 1024))
+
+
+def elapsed():
+    return time.monotonic() - _START
+
+
+def load_model():
+    tok = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.float16,  # T4 has no bfloat16
+        device_map="auto",
+        trust_remote_code=True,
+    ).eval()
+    if tok.pad_token_id is None:
+        tok.pad_token = tok.eos_token
+    return tok, model
+
+
+def generate(tok, model, messages, max_new_tokens):
+    inputs = tok.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    ).to(model.device)
+    with torch.no_grad():
+        out = model.generate(
+            inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=False,  # greedy => reproducible
+            num_beams=1,
+            pad_token_id=tok.pad_token_id,
+        )
+    return tok.decode(out[0][inputs.shape[-1] :], skip_special_tokens=True).strip()
+
+
+def main():
+    df = pd.read_csv(TEST_CSV, dtype=str).fillna("")
+    total = len(df)
+    print(f"loaded {total} problems from {TEST_CSV}", flush=True)
+
+    tok, model = load_model()
+    print(f"model loaded at {elapsed():.0f}s", flush=True)
+
+    records = []
+    for i, row in df.iterrows():
+        rid = row["id"]
+        context = row.get("context", "")
+        query = row.get("query", "")
+        task_type = row.get("task_type", "")
+        n = H.count_items(context, query, task_type)
+
+        preds = [""] * n  # safe default so every item has a slot
+        if elapsed() < SAFETY_S:
+            try:
+                messages = H.build_messages(context, query, task_type, n, cot=COT)
+                budget = H.max_new_tokens_for(task_type, n, cot=COT, cap=MAX_CAP)
+                text = generate(tok, model, messages, budget)
+                preds = H.parse_answers(text, n)
+            except Exception as exc:  # never let one row sink the whole run
+                print(f"[warn] id={rid} failed: {exc!r}", flush=True)
+        else:
+            print(f"[warn] time budget hit; blanking id={rid}", flush=True)
+
+        records.append({"id": rid, "pred": json.dumps(preds, ensure_ascii=False)})
+        if (i + 1) % 5 == 0 or (i + 1) == total:
+            print(f"{i + 1}/{total} done  ({elapsed():.0f}s)", flush=True)
+
+    pd.DataFrame(records, columns=["id", "pred"]).to_csv(OUT_CSV, index=False)
+    print(f"wrote {OUT_CSV} ({len(records)} rows) at {elapsed():.0f}s", flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000..1d385d6
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000..7d3b8f8
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:749a967cd78e972e9b7f20adaa0a14459c4f738b69cff9d600d9556f9b6b53f6
+size 5212541
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000..b068ffc
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,195 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<｜end▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<｜User｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151645": {
+      "content": "<｜Assistant｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151646": {
+      "content": "<｜begin▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|EOT|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151648": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151649": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "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
+    }
+  },
+  "bos_token": "<｜begin▁of▁sentence｜>",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{%- set ns.is_first = true -%}{%- else %}{{'\\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\\n' + '```json' + '\\n' + tool['function']['arguments'] + '\\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- set ns.is_output_first = false %}{%- else %}{{'\\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{'<｜tool▁outputs▁end｜>'}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{'<｜Assistant｜>'}}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<｜end▁of▁sentence｜>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 16384,
+  "pad_token": "<｜end▁of▁sentence｜>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": null,
+  "use_default_system_prompt": false
+}