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Model: Kuyash/teptez-ai Source: Original Platform
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*.7z filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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qwen2.5-coder-7b-instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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FROM qwen2.5-coder-7b-instruct.Q4_K_M.gguf
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TEMPLATE """{{- if .Suffix }}<|fim_prefix|>{{ .Prompt }}<|fim_suffix|>{{ .Suffix }}<|fim_middle|>
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{{- else if .Messages }}
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{{- if or .System .Tools }}<|im_start|>system
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{{- if .System }}
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{{ .System }}
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{{- end }}
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{{- if .Tools }}
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# Tools
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||||||
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||||||
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You may call one or more functions to assist with the user query.
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||||||
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||||||
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You are provided with function signatures within <tools></tools>:
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<tools>
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{{- range .Tools }}
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{"type": "function", "function": {{ .Function }}}
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{{- end }}
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</tools>
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For each function call, return a json object with function name and arguments within <tool_call></tool_call> with NO other text. Do not include any backticks or ```json.
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>
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{{- end }}<|im_end|>
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{{ end }}
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{{- range $i, $_ := .Messages }}
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{{- $last := eq (len (slice $.Messages $i)) 1 -}}
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{{- if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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{{ else if eq .Role "assistant" }}<|im_start|>assistant
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{{ if .Content }}{{ .Content }}
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{{- else if .ToolCalls }}<tool_call>
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{{ range .ToolCalls }}{"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}}
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{{ end }}</tool_call>
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{{- end }}{{ if not $last }}<|im_end|>
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{{ end }}
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{{- else if eq .Role "tool" }}<|im_start|>user
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<tool_response>
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{{ .Content }}
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</tool_response><|im_end|>
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{{ end }}
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{{- if and (ne .Role "assistant") $last }}<|im_start|>assistant
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{{ end }}
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{{- end }}
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{{- else }}
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{{- if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ end }}{{ .Response }}{{ if .Response }}<|im_end|>{{ end }}"""
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SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
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335
README.md
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---
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language: en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-7B-Instruct
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tags:
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- security
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- sast
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- code-analysis
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- vulnerability-detection
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- triage
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- gguf
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pipeline_tag: text-generation
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---
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# teptez-ai
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**SAST finding triage model** — fine-tuned on real production security scan data to classify Static Application Security Testing findings as true positives, false positives, or uncertain, with CWE labels, confidence scores, and remediation guidance.
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---
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## Overview
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Rule-based SAST tools generate enormous volumes of findings, a significant portion of which are false positives. Security analysts spend hours triaging noise instead of fixing real vulnerabilities. teptez-ai is a 7B-parameter LLM fine-tuned specifically to automate this triage step.
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Given a SAST finding (title, CWE, severity, code snippet, taint flow), teptez-ai returns a structured JSON verdict:
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- **true_positive** — finding is real, exploit path exists
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- **false_positive** — finding is noise, safe to suppress
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- **uncertain** — insufficient context, escalate to analyst
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Fine-tuned on production findings from the [Teptez](https://teptez.io) security platform — real codebases, real scan data, real analyst labels.
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### Key specs
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| Property | Value |
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|---|---|
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| Base model | Qwen2.5-Coder-7B-Instruct |
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| Quantization | GGUF Q4_K_M |
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| Model size | ~4.7 GB |
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| Inference speed | ~100 tok/s (RTX 3090 24GB) |
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| Context window | 8192 tokens |
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| License | Apache 2.0 |
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---
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||||||
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## Benchmark Results
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Evaluated against **OWASP Benchmark v1.2** — the standard industry benchmark for SAST tools — using the official **Youden's J statistic** (`J = TPR − FPR`).
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> J = 0.0 is random. J = 1.0 is perfect. Open-source SAST tools typically score 0.30–0.45.
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### Head-to-head vs base model
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| Model | TPR | FPR | Youden J | vs base |
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|---|---|---|---|---|
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| **teptez-ai (Q4_K_M)** | **0.68** | **0.57** | **0.109** | **+0.048 (+79%)** |
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| qwen2.5-coder-7b (base) | 0.66 | 0.60 | 0.061 | — |
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Fine-tuning delivers a **79% relative improvement** in Youden's J over the base model, primarily by cutting the false positive rate from 0.60 to 0.57 across the full benchmark.
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### Per-category breakdown
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| CWE Category | teptez-ai J | base J | Delta |
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|---|---|---|---|
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| Command Injection (CWE-78) | **0.40** | 0.22 | +0.18 |
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| SQL Injection (CWE-89) | **0.33** | 0.18 | +0.15 |
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| XSS (CWE-79) | **0.28** | 0.12 | +0.16 |
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| Path Traversal (CWE-22) | **0.22** | 0.09 | +0.13 |
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| Weak Randomness (CWE-330) | **0.13** | 0.05 | +0.08 |
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| Crypto/Hash (CWE-327/328) | 0.00 | 0.01 | -0.01 |
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| Auth/Authz (CWE-862/639) | 0.02 | 0.01 | +0.01 |
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| Timing (CWE-208) | 0.05 | 0.04 | +0.01 |
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| **Secure Cookie (CWE-614)** | **-0.08** | **0.52** | **-0.60 ⚠️** |
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||||||
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**Strong on injection classes.** teptez-ai significantly outperforms the base model across all injection-type CWEs (cmdi/sqli/xss/path/weakrand). These are the highest-volume SAST categories in real codebases.
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||||||
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**Securecookie regression.** CWE-614 (missing HttpOnly/Secure flags) shows a severe regression vs the base model. **Do not use teptez-ai to triage cookie security findings.** This is a known training artifact being fixed in the next round.
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||||||
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||||||
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**Dead categories.** Crypto, authz, and timing categories have near-zero Youden J on both models — 7B parameters are insufficient for these without full class context. Escalate to frontier models or human analysts.
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### Production run
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On **369 real production SAST findings** from live codebases:
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- **17% rejected as false positive** (~63 findings suppressed)
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- Injection-class findings: majority of suppressions, generally accurate
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- Authz/crypto findings: some wrong suppressions (do not enable for these categories)
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||||||
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---
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||||||
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||||||
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## Usage
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### Recommended architecture
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||||||
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Use teptez-ai as a **gated FP suppressor**, not a confirmer:
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||||||
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```
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Rule-engine finding
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│
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▼
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Is CWE in injection classes? ──No──▶ Keep finding (don't run model)
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│ Yes
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▼
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Run teptez-ai with full function + taint context
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│
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├── verdict: false_positive, confidence > 0.75 ──▶ Suppress finding
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├── verdict: true_positive ──▶ Keep finding
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└── verdict: uncertain / confidence < 0.75 ──▶ Escalate to frontier model / analyst
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```
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**Only suppress on `false_positive`** — never on `true_positive`. The model is biased toward flagging (FPR 0.57), so a `false_positive` verdict is rare and higher-precision.
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**Injection-class CWEs only** (where Youden J ≥ 0.13):
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- CWE-78 Command Injection
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- CWE-79 Cross-Site Scripting
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- CWE-89 SQL Injection
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- CWE-22 Path Traversal
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- CWE-330 Weak Randomness
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**Never auto-suppress**:
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- CWE-614 Secure Cookie (regression — model worse than random)
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- CWE-327/328 Weak Crypto/Hash (near-zero J)
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- CWE-862/639 Auth/Authz/IDOR (near-zero J)
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- CWE-208 Timing Attacks (near-zero J)
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### Running with llama.cpp / Ollama
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```bash
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# Pull via Ollama
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ollama pull hf.co/Kuyash/teptez-ai:Q4_K_M
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# Or run directly with llama.cpp
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./llama-cli -m teptez-ai-Q4_K_M.gguf \
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--temp 0.1 \
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--top-p 0.9 \
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-n 512 \
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-p "<prompt>"
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```
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### Python integration
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```python
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import json
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import requests
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def triage_finding(finding: dict) -> dict:
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prompt = f"""<|im_start|>system
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You are a SAST triage expert. Analyze this finding and return JSON with keys:
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verdict (true_positive|false_positive|uncertain), confidence (0.0-1.0),
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cwe (string), explanation (string), remediation (string).
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<|im_end|>
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<|im_start|>user
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||||||
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Finding: {finding['title']}
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CWE: {finding.get('cwe_id', 'unknown')}
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Severity: {finding.get('severity', 'MEDIUM')}
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Code:
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{finding.get('code_snippet', '')}
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Taint flow: {finding.get('data_flow', 'not available')}
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<|im_end|>
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<|im_start|>assistant
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"""
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response = requests.post("http://localhost:11434/api/generate", json={
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"model": "teptez-ai",
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"prompt": prompt,
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"stream": False,
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"options": {"temperature": 0.1}
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})
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text = response.json()["response"].strip()
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# Strip markdown fences if present
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if text.startswith("```"):
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text = text.split("```")[1]
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if text.startswith("json"):
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text = text[4:]
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return json.loads(text)
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||||||
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# Gate: only run on injection CWEs
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INJECTION_CWES = {"CWE-78", "CWE-79", "CWE-89", "CWE-22", "CWE-330"}
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||||||
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def should_suppress(finding: dict) -> bool:
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cwe = finding.get("cwe_id", "")
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if cwe not in INJECTION_CWES:
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return False # don't touch non-injection
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result = triage_finding(finding)
|
||||||
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return (
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result.get("verdict") == "false_positive"
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and result.get("confidence", 0) >= 0.75
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)
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```
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||||||
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---
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||||||
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||||||
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## Input / Output Format
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||||||
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|
||||||
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### Prompt template
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||||||
|
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||||||
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```
|
||||||
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<|im_start|>system
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||||||
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You are a SAST triage expert. Analyze this finding and return JSON.
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||||||
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<|im_end|>
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||||||
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<|im_start|>user
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||||||
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Finding: {title}
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||||||
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CWE: {cwe_id}
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||||||
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Severity: {severity}
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Code:
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||||||
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{code_snippet}
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||||||
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||||||
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Taint flow: {data_flow}
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||||||
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<|im_end|>
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||||||
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<|im_start|>assistant
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||||||
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```
|
||||||
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||||||
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**Tips for best results:**
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||||||
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- Provide the **full function**, not just the flagged line — avoids "insufficient context" errors
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||||||
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- Include taint flow when available (source → sink path from your SAST tool)
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||||||
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- Keep code under 2048 tokens; truncate from the bottom if needed
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||||||
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||||||
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### Output schema
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||||||
|
|
||||||
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```json
|
||||||
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{
|
||||||
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"verdict": "true_positive" | "false_positive" | "uncertain",
|
||||||
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"confidence": 0.85,
|
||||||
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"cwe": "CWE-89",
|
||||||
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"explanation": "User input from request.getParameter() flows directly into a string-concatenated SQL query with no parameterization or escaping.",
|
||||||
|
"remediation": "Replace string concatenation with a PreparedStatement: `conn.prepareStatement(\"SELECT * FROM users WHERE id = ?\")` and bind the parameter with `stmt.setString(1, userId)`."
|
||||||
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}
|
||||||
|
```
|
||||||
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|
||||||
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| Field | Type | Description |
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||||||
|
|---|---|---|
|
||||||
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| `verdict` | string | `true_positive`, `false_positive`, or `uncertain` |
|
||||||
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| `confidence` | float | 0.0–1.0; scores < 0.75 should be treated as uncertain |
|
||||||
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| `cwe` | string | Classified CWE identifier |
|
||||||
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| `explanation` | string | Why the model reached this verdict |
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||||||
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| `remediation` | string | Concrete fix recommendation |
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||||||
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|
||||||
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---
|
||||||
|
|
||||||
|
## Limitations
|
||||||
|
|
||||||
|
### Known issues (as of current release)
|
||||||
|
|
||||||
|
**CWE-614 Secure Cookie — severe regression.**
|
||||||
|
teptez-ai scores Youden J = −0.08 on secure cookie findings, compared to 0.52 for the base model. This is a catastrophic regression caused by training data imbalance. Do not use teptez-ai for HttpOnly/Secure flag findings until this is fixed.
|
||||||
|
|
||||||
|
**High overall FPR (0.57).**
|
||||||
|
The model over-flags — it sees vulnerability in safe code, especially in crypto, auth, and cookie-related code patterns. A `false_positive` verdict is more reliable than a `true_positive` verdict because it swims against the model's bias.
|
||||||
|
|
||||||
|
**Dead categories (crypto/authz/timing).**
|
||||||
|
CWE-327/328/614/862/639/208 have near-zero Youden J. The model lacks sufficient training signal for these categories. Use a frontier model (Claude, GPT-4o, Gemini) or a human analyst for these.
|
||||||
|
|
||||||
|
**7B parameter ceiling.**
|
||||||
|
Subtle IDOR, broken access control, and privilege escalation patterns require understanding class hierarchy, authentication flow, and business logic across multiple files. A 7B model with single-function context cannot reliably detect these.
|
||||||
|
|
||||||
|
**GGUF Q4_K_M quantization.**
|
||||||
|
~4-bit quantization introduces slight accuracy loss vs fp16. For maximum accuracy, use the Q8_0 variant (if available) or the full fp16 model.
|
||||||
|
|
||||||
|
**Snippet-only inputs fail.**
|
||||||
|
If you pass only the flagged 1–3 lines without the surrounding function, the model frequently returns `uncertain` with "insufficient context." Always include the full function body.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Reproducing the Benchmark
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# 1. Clone OWASP Benchmark
|
||||||
|
git clone https://github.com/OWASP-Benchmark/BenchmarkJava
|
||||||
|
cd BenchmarkJava && mvn package -DskipTests
|
||||||
|
|
||||||
|
# 2. Run evaluation (requires teptez-ai running on Ollama)
|
||||||
|
python salad/eval/owasp_eval.py # stratified sample → results.jsonl
|
||||||
|
python salad/eval/owasp_score.py # Youden J + per-category table
|
||||||
|
python salad/eval/owasp_compare.py # head-to-head vs base model
|
||||||
|
```
|
||||||
|
|
||||||
|
The eval script uses a stratified sample of OWASP BenchmarkJava test cases, covering all CWE categories proportionally. Scoring follows the official OWASP methodology (Youden's J = TPR − FPR).
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Roadmap
|
||||||
|
|
||||||
|
The following improvements are planned for the next fine-tuning round:
|
||||||
|
|
||||||
|
### Round 2 targets
|
||||||
|
|
||||||
|
| Improvement | Target metric |
|
||||||
|
|---|---|
|
||||||
|
| Flood training with safe-code negatives (50/50 balance) | FPR < 0.30 |
|
||||||
|
| Fix securecookie regression (restore CWE-614 training data) | J(CWE-614) > 0.40 |
|
||||||
|
| Add dead-category examples (crypto/authz/timing) | J(CWE-327/328) > 0.10 |
|
||||||
|
| Calibrate confidence score (train explicit `uncertain` label) | Confidence Brier score < 0.15 |
|
||||||
|
| Full-function context at train AND inference | Reduce "uncertain" on short snippets |
|
||||||
|
|
||||||
|
**Root cause of FPR problem:** Current training set is vuln-heavy (more vulnerable examples than safe ones). The model learned to flag aggressively. Rebalancing to 50/50 with explicit safe variants (parameterized SQL, escaped HTML, validated paths, compare_digest timing-safe comparisons, role-checked endpoints, strong ciphers) is the single highest-leverage fix.
|
||||||
|
|
||||||
|
**Securecookie fix:** Restore the original training examples for CWE-614 that were accidentally dropped. Mix with new negative examples. Lower learning rate for this category to avoid forgetting again.
|
||||||
|
|
||||||
|
**No catastrophic forgetting:** Round 2 will mix old injection data with new negatives and use a lower learning rate on the balanced set, following standard continual learning practice.
|
||||||
|
|
||||||
|
### Round 3 vision
|
||||||
|
|
||||||
|
- Full-function + cross-file taint context (requires longer context fine-tune)
|
||||||
|
- Multi-label output (multiple CWEs per finding)
|
||||||
|
- Confidence calibration verified against held-out production labels
|
||||||
|
- Youden J > 0.25 across all injection categories
|
||||||
|
- FPR < 0.30 overall
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
|
||||||
|
If you use teptez-ai in your research or tooling, please cite:
|
||||||
|
|
||||||
|
```bibtex
|
||||||
|
@misc{teptez-ai-2026,
|
||||||
|
title = {teptez-ai: A Fine-Tuned LLM for SAST Finding Triage},
|
||||||
|
author = {Teptez Security},
|
||||||
|
year = {2026},
|
||||||
|
publisher = {HuggingFace},
|
||||||
|
url = {https://huggingface.co/Kuyash/teptez-ai}
|
||||||
|
}
|
||||||
|
```
|
||||||
|
|
||||||
|
Evaluated against [OWASP Benchmark v1.2](https://owasp.org/www-project-benchmark/) using the official Youden's J scoring methodology.
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
## Related
|
||||||
|
|
||||||
|
- [OWASP Benchmark](https://owasp.org/www-project-benchmark/) — the benchmark used for evaluation
|
||||||
|
- [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) — the base model
|
||||||
|
|
||||||
|
---
|
||||||
|
|
||||||
|
*teptez-ai is a security research model. Results may vary across codebases and languages. Always have a human analyst review suppressed findings in critical security contexts.*
|
||||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0]['role'] == 'system' %}
|
||||||
|
{{- messages[0]['content'] }}
|
||||||
|
{%- else %}
|
||||||
|
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "\n\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>" }}
|
||||||
|
{%- 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' }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{{- '<|im_start|>' + message.role }}
|
||||||
|
{%- if message.content %}
|
||||||
|
{{- '\n' + message.content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if tool_call.function is defined %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if (loop.index0 == 0) 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' }}
|
||||||
|
{%- endif %}
|
||||||
62
config.json
Normal file
62
config.json
Normal file
@@ -0,0 +1,62 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen2ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 3584,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 18944,
|
||||||
|
"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"
|
||||||
|
],
|
||||||
|
"max_position_embeddings": 32768,
|
||||||
|
"max_window_layers": 28,
|
||||||
|
"model_type": "qwen2",
|
||||||
|
"num_attention_heads": 28,
|
||||||
|
"num_hidden_layers": 28,
|
||||||
|
"num_key_value_heads": 4,
|
||||||
|
"pad_token_id": 151665,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_parameters": {
|
||||||
|
"rope_theta": 1000000.0,
|
||||||
|
"rope_type": "default"
|
||||||
|
},
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": false,
|
||||||
|
"unsloth_fixed": true,
|
||||||
|
"unsloth_version": "2026.6.1",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 152064
|
||||||
|
}
|
||||||
15
generation_config.json
Normal file
15
generation_config.json
Normal file
@@ -0,0 +1,15 @@
|
|||||||
|
{
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
151645,
|
||||||
|
151643
|
||||||
|
],
|
||||||
|
"max_length": 32768,
|
||||||
|
"pad_token_id": 151665,
|
||||||
|
"repetition_penalty": 1.1,
|
||||||
|
"temperature": 0.7,
|
||||||
|
"top_k": 20,
|
||||||
|
"top_p": 0.8,
|
||||||
|
"transformers_version": "5.5.0"
|
||||||
|
}
|
||||||
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:0c36d62ecd322df7a08b438c0fcf87549724b051a5edba035aacf0e4962868ad
|
||||||
|
size 4877660776
|
||||||
3
model-00002-of-00004.safetensors
Normal file
3
model-00002-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:5b6b9754cd4351a4e813cf46be0e049637cec9001714a3b0e1054511efb6e329
|
||||||
|
size 4932751008
|
||||||
3
model-00003-of-00004.safetensors
Normal file
3
model-00003-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:410afb0930849b7ff7a9e5961e2577122d0e85ceaf0701b96b7e27cad5f655e8
|
||||||
|
size 4330865200
|
||||||
3
model-00004-of-00004.safetensors
Normal file
3
model-00004-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:5aa6e5cbe642377fd441fb4e60e83cca96b2bcd9820e245b9ea06d94653f17f2
|
||||||
|
size 1089994880
|
||||||
346
model.safetensors.index.json
Normal file
346
model.safetensors.index.json
Normal file
@@ -0,0 +1,346 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 15231233024
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"lm_head.weight": "model-00004-of-00004.safetensors",
|
||||||
|
"model.embed_tokens.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.bias": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||||
|
"model.layers.10.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.bias": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.bias": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.self_attn.k_proj.bias": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||||
|
"model.layers.11.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
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|
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203
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Normal file
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||||||
|
"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": "<|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 {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\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 {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) 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{%- endif %}\n"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user