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Model: exploitintel/cve-cwe-qwen3-8b Source: Original Platform
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117
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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datasets:
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- exploitintel/cve-cwe-consensus
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language:
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- en
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tags:
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- cybersecurity
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- vulnerability
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- cve
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- cwe
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- text-classification
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- qlora
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- unsloth
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pipeline_tag: text-generation
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library_name: transformers
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---
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# CVE → CWE Classifier (Qwen3-8B)
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A QLoRA fine-tune of **Qwen3-8B** that maps a free-text **CVE description** to the **CWE weakness
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ID(s)** it corresponds to. The LoRA adapter is merged into the base and released in 16-bit, so it
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loads directly with `transformers`. A higher-quality (larger) variant is at
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[`exploitintel/cve-cwe-qwen3-32b`](https://huggingface.co/exploitintel/cve-cwe-qwen3-32b).
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Trained only on labels where **NVD and the CNA agree** after roll-up to **CWE View-1003** — see the
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[`cve-cwe-consensus`](https://huggingface.co/datasets/exploitintel/cve-cwe-consensus) dataset.
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## Results (held-out test split, 6,802 rows)
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| Metric | Score |
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|---|---|
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| Exact-match | **0.676** |
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| Micro-F1 | **0.702** |
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| Macro-F1 | **0.511** |
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By difficulty (does the description *name* the weakness, or must it be inferred?):
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| Stratum | n | Exact-match | Micro-F1 |
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|---|---|---|---|
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| Easy (weakness named) | 2,046 | 0.841 | 0.870 |
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| Hard (must infer) | 4,756 | 0.605 | 0.628 |
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The macro-F1 reflects a dataset that caps majority CWEs (e.g. CWE-79) so rare weaknesses are
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learned rather than drowned out.
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**Reading the numbers:**
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- **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.
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- **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.
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- 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.
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## Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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mid = "exploitintel/cve-cwe-qwen3-8b"
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tok = AutoTokenizer.from_pretrained(mid)
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model = AutoModelForCausalLM.from_pretrained(mid, torch_dtype="auto", device_map="auto")
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messages = [
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{"role": "system", "content": "You are a vulnerability analyst. Given a CVE description, "
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"reply with only the CWE ID(s) it maps to, comma-separated."},
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{"role": "user", "content": "A SQL injection vulnerability in the login endpoint allows an "
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"unauthenticated attacker to execute arbitrary SQL via the username parameter."},
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]
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inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(inputs, max_new_tokens=32, do_sample=False)
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print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
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# -> CWE-89
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```
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### GGUF / Ollama
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A `Q4_K_M` GGUF is included in this repo for local runners:
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```bash
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ollama run hf.co/exploitintel/cve-cwe-qwen3-8b:Q4_K_M
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```
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Set the same system prompt (`/set system You are a vulnerability analyst...`) so it returns bare CWE IDs.
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> **Note:** This Ollama command has not been verified end-to-end. This is a standard `qwen3`
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> model so the embedded template should apply normally — but if `ollama run` ignores the
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> system prompt and produces rambling text instead of a bare CWE ID, supply an explicit
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> ChatML Modelfile `TEMPLATE` as shown in the [Qwen3.5-4B card](https://huggingface.co/exploitintel/cve-cwe-qwen35-4b).
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## Training
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- **Base:** `Qwen/Qwen3-8B` (trained 4-bit via `unsloth/qwen3-8b-unsloth-bnb-4bit`)
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- **Method:** QLoRA (4-bit) with Unsloth, merged to 16-bit · released checkpoint: **checkpoint-960** (final; eval loss declined monotonically through training)
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- **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
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- **Settings:** 2 epochs · context 512 · LR 2e-4 · AdamW 8-bit · linear schedule · packing on · train-on-completions-only off · seed 3407
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- 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.**
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## Prompt format
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ChatML (Qwen3 standard). System prompt fixed; the description is the only user input — never feed the
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label or CVE-ID.
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- **system:** `You are a vulnerability analyst. Given a CVE description, reply with only the CWE ID(s) it maps to, comma-separated.`
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- **user:** the CVE description
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- **assistant:** `CWE-79, CWE-80`
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## Limitations
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- CWEs below the dataset's 50-example floor are not in the label space and won't be predicted.
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- Outputs CWE IDs as text and can occasionally emit a malformed/non-existent ID — validate against
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the official CWE list.
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- English-only; descriptions only (no code, CVSS, or references).
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- A triage/assist aid, not an authoritative CWE assignment — human-review before acting.
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## License
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Apache-2.0 (inherited from Qwen3-8B). Dataset derives from public upstreams (NVD, MITRE CVE/CWE).
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97
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# 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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for forward_message in messages %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- set message = messages[index] %}
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{%- set tool_start = '<tool_response>' %}
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{%- set tool_start_length = tool_start|length %}
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{%- set start_of_message = message.content[:tool_start_length] %}
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{%- set tool_end = '</tool_response>' %}
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{%- set tool_end_length = tool_end|length %}
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{%- set start_pos = (message.content|length) - tool_end_length %}
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{%- if start_pos < 0 %}
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{%- set start_pos = 0 %}
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{%- endif %}
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{%- set end_of_message = message.content[start_pos:] %}
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{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set content = message.content %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in message.content %}
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{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
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{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
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{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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72
config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"torch_dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"full_attention",
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||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
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"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,
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||||
"max_window_layers": 36,
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||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
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"pad_token_id": 151669,
|
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"rms_norm_eps": 1e-06,
|
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"rope_parameters": {
|
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"rope_theta": 1000000,
|
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"rope_type": "default"
|
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},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
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"unsloth_fixed": true,
|
||||
"unsloth_version": "2026.5.5",
|
||||
"use_cache": true,
|
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"use_sliding_window": false,
|
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"vocab_size": 151936
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}
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194
evaluate.py
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evaluate.py
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#!/usr/bin/env python3
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"""Evaluate a fine-tuned CVE -> CWE model on the held-out test split.
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||||
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||||
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),
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so you see real-world performance instead of one flattered average.
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||||
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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.
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||||
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||||
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.
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||||
"""
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||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import re
|
||||
|
||||
import torch
|
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from datasets import load_dataset
|
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from transformers import AutoModelForCausalLM, AutoTokenizer
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CWE_RE = re.compile(r"CWE-\d+")
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||||
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||||
# 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.
|
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EASY_KW = [
|
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"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",
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||||
"race condition",
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||||
"deserialization",
|
||||
"ssrf",
|
||||
"server-side request forgery",
|
||||
"csrf",
|
||||
"cross-site request forgery",
|
||||
"open redirect",
|
||||
"integer overflow",
|
||||
]
|
||||
|
||||
|
||||
def parse_cwes(text: str) -> set[str]:
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||||
return set(CWE_RE.findall(text))
|
||||
|
||||
|
||||
def is_easy(description: str) -> bool:
|
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return any(k in description.lower() for k in EASY_KW)
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||||
|
||||
|
||||
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()
|
||||
3
export_metadata.json
Normal file
3
export_metadata.json
Normal file
@@ -0,0 +1,3 @@
|
||||
{
|
||||
"base_model": "unsloth/qwen3-8b-unsloth-bnb-4bit"
|
||||
}
|
||||
11
generation_config.json
Normal file
11
generation_config.json
Normal file
@@ -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"
|
||||
}
|
||||
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
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||||
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|
||||
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model-00002-of-00004.safetensors
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4915960368
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model-00003-of-00004.safetensors
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3
model-00003-of-00004.safetensors
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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3
model-00004-of-00004.safetensors
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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|
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406
model.safetensors.index.json
Normal file
406
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Normal file
@@ -0,0 +1,406 @@
|
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{
|
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|
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|
||||
"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 %}"
|
||||
}
|
||||
Reference in New Issue
Block a user