commit 101159e94df4397848c209badb561a6fdc2b4c21
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Sun Aug 9 20:46:14 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: exploitintel/cve-cwe-qwen3-8b
    Source: Original Platform

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+qwen3-8b.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+datasets:
+  - exploitintel/cve-cwe-consensus
+language:
+  - en
+tags:
+  - cybersecurity
+  - vulnerability
+  - cve
+  - cwe
+  - text-classification
+  - qlora
+  - unsloth
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# CVE → CWE Classifier (Qwen3-8B)
+
+A QLoRA fine-tune of **Qwen3-8B** that maps a free-text **CVE description** to the **CWE weakness
+ID(s)** it corresponds to. The LoRA adapter is merged into the base and released in 16-bit, so it
+loads directly with `transformers`. A higher-quality (larger) variant is at
+[`exploitintel/cve-cwe-qwen3-32b`](https://huggingface.co/exploitintel/cve-cwe-qwen3-32b).
+
+Trained only on labels where **NVD and the CNA agree** after roll-up to **CWE View-1003** — see the
+[`cve-cwe-consensus`](https://huggingface.co/datasets/exploitintel/cve-cwe-consensus) dataset.
+
+## Results (held-out test split, 6,802 rows)
+
+| Metric | Score |
+|---|---|
+| Exact-match | **0.676** |
+| Micro-F1 | **0.702** |
+| Macro-F1 | **0.511** |
+
+By difficulty (does the description *name* the weakness, or must it be inferred?):
+
+| Stratum | n | Exact-match | Micro-F1 |
+|---|---|---|---|
+| Easy (weakness named) | 2,046 | 0.841 | 0.870 |
+| Hard (must infer) | 4,756 | 0.605 | 0.628 |
+
+The macro-F1 reflects a dataset that caps majority CWEs (e.g. CWE-79) so rare weaknesses are
+learned rather than drowned out.
+
+**Reading the numbers:**
+- **Macro-F1 is computed over the union of gold and predicted labels** (125 = 117 gold + ~8 the model predicted outside the gold set). Those out-of-label predictions score ~0 and pull macro *down*, so 0.511 is a **conservative** figure.
+- **Exact-match has an inherent ceiling of ~98.3%:** ~1.74% of the test set (273 groups / 1,205 rows) are identical descriptions mapped to *different* CWEs (e.g. a bare "Windows Kernel Elevation of Privilege Vulnerability"), which a description-only model cannot disambiguate.
+- Scores are on the **capped/balanced** test split (~30% "easy" rows), so they are **not** directly comparable to metrics measured on a different (e.g. natural-distribution) split.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+mid = "exploitintel/cve-cwe-qwen3-8b"
+tok = AutoTokenizer.from_pretrained(mid)
+model = AutoModelForCausalLM.from_pretrained(mid, torch_dtype="auto", device_map="auto")
+
+messages = [
+    {"role": "system", "content": "You are a vulnerability analyst. Given a CVE description, "
+                                   "reply with only the CWE ID(s) it maps to, comma-separated."},
+    {"role": "user", "content": "A SQL injection vulnerability in the login endpoint allows an "
+                                "unauthenticated attacker to execute arbitrary SQL via the username parameter."},
+]
+inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=32, do_sample=False)
+print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+# -> CWE-89
+```
+
+### GGUF / Ollama
+
+A `Q4_K_M` GGUF is included in this repo for local runners:
+
+```bash
+ollama run hf.co/exploitintel/cve-cwe-qwen3-8b:Q4_K_M
+```
+
+Set the same system prompt (`/set system You are a vulnerability analyst...`) so it returns bare CWE IDs.
+
+> **Note:** This Ollama command has not been verified end-to-end. This is a standard `qwen3`
+> model so the embedded template should apply normally — but if `ollama run` ignores the
+> system prompt and produces rambling text instead of a bare CWE ID, supply an explicit
+> ChatML Modelfile `TEMPLATE` as shown in the [Qwen3.5-4B card](https://huggingface.co/exploitintel/cve-cwe-qwen35-4b).
+
+## Training
+
+- **Base:** `Qwen/Qwen3-8B` (trained 4-bit via `unsloth/qwen3-8b-unsloth-bnb-4bit`)
+- **Method:** QLoRA (4-bit) with Unsloth, merged to 16-bit · released checkpoint: **checkpoint-960** (final; eval loss declined monotonically through training)
+- **Dataset:** [`exploitintel/cve-cwe-consensus`](https://huggingface.co/datasets/exploitintel/cve-cwe-consensus) — 69,386 rows (55,810 / 6,774 / 6,802), majority CWEs capped at 2,500
+- **Settings:** 2 epochs · context 512 · LR 2e-4 · AdamW 8-bit · linear schedule · packing on · train-on-completions-only off · seed 3407
+- LoRA fine-tune, adapter merged into the base. Exact per-run **LoRA rank/alpha, batch size, and weight decay were not logged to the repo.**
+
+## Prompt format
+
+ChatML (Qwen3 standard). System prompt fixed; the description is the only user input — never feed the
+label or CVE-ID.
+
+- **system:** `You are a vulnerability analyst. Given a CVE description, reply with only the CWE ID(s) it maps to, comma-separated.`
+- **user:** the CVE description
+- **assistant:** `CWE-79, CWE-80`
+
+## Limitations
+
+- CWEs below the dataset's 50-example floor are not in the label space and won't be predicted.
+- Outputs CWE IDs as text and can occasionally emit a malformed/non-existent ID — validate against
+  the official CWE list.
+- English-only; descriptions only (no code, CVSS, or references).
+- A triage/assist aid, not an authoritative CWE assignment — human-review before acting.
+
+## License
+
+Apache-2.0 (inherited from Qwen3-8B). Dataset derives from public upstreams (NVD, MITRE CVE/CWE).
diff --git a/chat_template.jinja b/chat_template.jinja
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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = message.content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (message.content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = message.content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}
\ No newline at end of file
diff --git a/config.json b/config.json
new file mode 100644
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--- /dev/null
+++ b/config.json
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+{
+    "architectures": [
+        "Qwen3ForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "bfloat16",
+    "eos_token_id": 151645,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "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": 40960,
+    "max_window_layers": 36,
+    "model_type": "qwen3",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 8,
+    "pad_token_id": 151669,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.5.5",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}
\ No newline at end of file
diff --git a/evaluate.py b/evaluate.py
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+++ b/evaluate.py
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+#!/usr/bin/env python3
+"""Evaluate a fine-tuned CVE -> CWE model on the held-out test split.
+
+Reports exact-match accuracy plus micro/macro multi-label F1, stratified into
+"easy" (the weakness is named in the description) vs "hard" (it must be inferred),
+so you see real-world performance instead of one flattered average.
+
+Loads with plain transformers. Newer architectures (e.g. model_type ``gemma4``,
+used by gemma-4-E4B) need **transformers >= 5.5** -- older versions raise
+``KeyError: 'gemma4'``. Note: do NOT load gemma4 through unsloth in a Studio env
+whose transformers was upgraded -- the upgrade pulls ``huggingface_hub`` 1.x,
+which breaks ``unsloth_zoo``'s config lookup. Plain transformers is the clean path.
+
+    python evaluate.py --model "C:\\path\\to\\exported\\merged_model" --limit 500
+    python evaluate.py --model "C:\\path\\to\\exported\\merged_model"
+
+Needs: transformers>=5.5, torch, datasets, accelerate.
+"""
+
+from __future__ import annotations
+
+import argparse
+import re
+
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+CWE_RE = re.compile(r"CWE-\d+")
+
+# A row is "easy" if the description literally names the weakness (the model can
+# keyword-match); "hard" rows require inferring the CWE from the prose.
+EASY_KW = [
+    "sql injection",
+    "cross-site scripting",
+    "cross site scripting",
+    "xss",
+    "buffer overflow",
+    "use after free",
+    "use-after-free",
+    "path traversal",
+    "command injection",
+    "out-of-bounds",
+    "out of bounds",
+    "race condition",
+    "deserialization",
+    "ssrf",
+    "server-side request forgery",
+    "csrf",
+    "cross-site request forgery",
+    "open redirect",
+    "integer overflow",
+]
+
+
+def parse_cwes(text: str) -> set[str]:
+    return set(CWE_RE.findall(text))
+
+
+def is_easy(description: str) -> bool:
+    return any(k in description.lower() for k in EASY_KW)
+
+
+def prf(tp: int, fp: int, fn: int) -> tuple[float, float, float]:
+    p = tp / (tp + fp) if (tp + fp) else 0.0
+    r = tp / (tp + fn) if (tp + fn) else 0.0
+    f = 2 * p * r / (p + r) if (p + r) else 0.0
+    return p, r, f
+
+
+def build_prompt(tok, messages: list[dict]) -> str:
+    """Prompt = everything up to (but not including) the assistant answer."""
+    convo = messages[:-1]
+    try:
+        return tok.apply_chat_template(convo, tokenize=False, add_generation_prompt=True)
+    except Exception:
+        # Some chat templates (e.g. Gemma) reject a separate "system" role;
+        # fold the system text into the user turn instead.
+        sys_txt = next((m["content"] for m in convo if m["role"] == "system"), "")
+        usr_txt = next((m["content"] for m in convo if m["role"] == "user"), "")
+        folded = [{"role": "user", "content": f"{sys_txt}\n\n{usr_txt}".strip()}]
+        return tok.apply_chat_template(folded, tokenize=False, add_generation_prompt=True)
+
+
+def score(truths: list[set[str]], preds: list[set[str]], easies: list[bool]) -> None:
+    micro = [0, 0, 0]  # tp, fp, fn
+    per_label: dict[str, list[int]] = {}
+    exact = 0
+    strata = {"easy": [0, 0, 0, 0, 0], "hard": [0, 0, 0, 0, 0]}  # tp,fp,fn,exact,n
+
+    for true, pred, easy in zip(truths, preds, easies):
+        tp, fp, fn = len(pred & true), len(pred - true), len(true - pred)
+        micro[0] += tp
+        micro[1] += fp
+        micro[2] += fn
+        ex = int(pred == true)
+        exact += ex
+        for lab in true | pred:
+            d = per_label.setdefault(lab, [0, 0, 0])
+            if lab in true and lab in pred:
+                d[0] += 1
+            elif lab in pred:
+                d[1] += 1
+            else:
+                d[2] += 1
+        s = strata["easy" if easy else "hard"]
+        s[0] += tp
+        s[1] += fp
+        s[2] += fn
+        s[3] += ex
+        s[4] += 1
+
+    n = len(truths)
+    micro_f1 = prf(*micro)[2]
+    macro_f1 = sum(prf(*v)[2] for v in per_label.values()) / len(per_label) if per_label else 0.0
+
+    print("\n=== CVE -> CWE evaluation ===")
+    print(f"examples             : {n}")
+    print(f"exact-match accuracy : {exact / n:.3f}   (predicted CWE set == true set)")
+    print(f"micro-F1             : {micro_f1:.3f}")
+    print(f"macro-F1             : {macro_f1:.3f}   (unweighted mean over {len(per_label)} CWEs)")
+    print("\n-- by difficulty --")
+    for name, label in (("easy", "easy (weakness named)"), ("hard", "hard (must infer) ")):
+        tp, fp, fn, ex, m = strata[name]
+        if m:
+            print(f"  {label:22s} n={m:5d}  exact={ex / m:.3f}  micro-F1={prf(tp, fp, fn)[2]:.3f}")
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="Evaluate a CVE->CWE model on the test split.")
+    ap.add_argument("--model", required=True, help="path or HF id of the fine-tuned (merged) model")
+    ap.add_argument("--dataset", default="exploitintel/cve-cwe-consensus")
+    ap.add_argument("--split", default="test")
+    ap.add_argument(
+        "--limit", type=int, default=None, help="evaluate only the first N rows (quick check)"
+    )
+    ap.add_argument("--batch-size", type=int, default=16)
+    ap.add_argument("--max-new-tokens", type=int, default=32)
+    args = ap.parse_args()
+
+    print(f"loading model: {args.model}")
+    try:
+        tok = AutoTokenizer.from_pretrained(args.model)
+    except (AttributeError, TypeError):
+        # Some Gemma tokenizer configs store `extra_special_tokens` as a list, which
+        # trips a transformers bug ('list' object has no attribute 'keys').
+        tok = AutoTokenizer.from_pretrained(args.model, extra_special_tokens={})
+    tok.padding_side = "left"  # decoder-only batched generation needs left padding
+    if tok.pad_token is None:
+        tok.pad_token = tok.eos_token
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    try:
+        model = AutoModelForCausalLM.from_pretrained(args.model, dtype="auto").to(device)
+    except TypeError:
+        # `dtype` is the transformers 5.x name; older releases use `torch_dtype`.
+        model = AutoModelForCausalLM.from_pretrained(args.model, torch_dtype="auto").to(device)
+    model.eval()
+
+    ds = load_dataset(args.dataset, split=args.split)
+    if args.limit:
+        ds = ds.select(range(min(args.limit, len(ds))))
+
+    prompts, truths, easies = [], [], []
+    for ex in ds:
+        msgs = ex["messages"]
+        prompts.append(build_prompt(tok, msgs))
+        truths.append(parse_cwes(msgs[-1]["content"]))
+        usr = next((m["content"] for m in msgs if m["role"] == "user"), "")
+        easies.append(is_easy(usr))
+
+    preds: list[set[str]] = []
+    for i in range(0, len(prompts), args.batch_size):
+        batch = prompts[i : i + args.batch_size]
+        enc = tok(batch, return_tensors="pt", padding=True, truncation=True, max_length=1024).to(
+            device
+        )
+        with torch.no_grad():
+            out = model.generate(
+                **enc,
+                max_new_tokens=args.max_new_tokens,
+                do_sample=False,  # greedy = deterministic
+                pad_token_id=tok.pad_token_id,
+            )
+        new_tokens = out[:, enc["input_ids"].shape[1] :]  # drop the prompt, keep the answer
+        for row in new_tokens:
+            preds.append(parse_cwes(tok.decode(row, skip_special_tokens=True)))
+        print(f"  {min(i + args.batch_size, len(prompts))}/{len(prompts)}", end="\r")
+    print()
+
+    score(truths, preds, easies)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/export_metadata.json b/export_metadata.json
new file mode 100644
index 0000000..56c09be
--- /dev/null
+++ b/export_metadata.json
@@ -0,0 +1,3 @@
+{
+  "base_model": "unsloth/qwen3-8b-unsloth-bnb-4bit"
+}
\ No newline at end of file
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..d1e84cc
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,11 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 40960,
+  "pad_token_id": 151669,
+  "transformers_version": "5.9.0"
+}
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new file mode 100644
index 0000000..70c8130
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new file mode 100644
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new file mode 100644
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new file mode 100644
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+oid sha256:d7430e9138b76e93fb6f93462394d236b411111aef53cb421ba97d2691040cca
+size 11423114
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000..f94bc23
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,235 @@
+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": false,
+  "local_files_only": false,
+  "model_max_length": 40960,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": false
+    },
+    "151669": {
+      "content": "<|PAD_TOKEN|>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  },
+  "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 forward_message in messages %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- set message = messages[index] %}\n    {%- set tool_start = '<tool_response>' %}\n    {%- set tool_start_length = tool_start|length %}\n    {%- set start_of_message = message.content[:tool_start_length] %}\n    {%- set tool_end = '</tool_response>' %}\n    {%- set tool_end_length = tool_end|length %}\n    {%- set start_pos = (message.content|length) - tool_end_length %}\n    {%- if start_pos < 0 %}\n        {%- set start_pos = 0 %}\n    {%- endif %}\n    {%- set end_of_message = message.content[start_pos:] %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}\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>')|last).lstrip('\\n') %}\n                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\\n') %}\n                {%- set reasoning_content = (reasoning_content.split('<think>')|last).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 %}"
+}
\ No newline at end of file