CodeLanguage-Qwen3.5-2B-v5

Quick start

python
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from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, json, re

BASE = "Qwen/Qwen3.5-2B"
ADAPTER = "Accuknoxtechnologies/PromptInjection-Qwen3.5-2B-v5"

SYSTEM_MSG = """You are a code language identifier. For the given user prompt, decide whether it contains any embedded source code (program source or recognizable code-like configuration). Output exactly one JSON object and nothing else: {"is_valid": <true|false>, "category": {"<Lang>": true, ...}}.
No preamble. No explanation. No <think> tags. No markdown code fences. No trailing prose.
Rules:
  - is_valid is TRUE when the prompt contains at least one code/config snippet, FALSE when the prompt is plain natural-language only.
  - category contains ONLY the languages that appear, each mapped to true. If no code is present, category is the empty object {}.
  - When multiple languages appear, list every distinct one (still only true).
Allowed language keys (use these exact spellings):
  Python, JavaScript, Java, C, C++, C#, Go, Rust, Kotlin, Swift, Ruby, R, Scala, Perl, Lua, Bash, PowerShell, Batch, SQL, Dockerfile, YAML, Makefile, Terraform, AWK, jq

Examples:

Input: What's the weather forecast today?
Output: {"is_valid": false, "category": {}}

Input: Run this for me: print('hello world')
Output: {"is_valid": true, "category": {"Python": true}}

Input: Compare these — SELECT * FROM users vs the snippet: console.log(users)
Output: {"is_valid": true, "category": {"SQL": true, "JavaScript": true}}"""

tokenizer = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    BASE, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True,
)
model = PeftModel.from_pretrained(model, ADAPTER); model.eval()

def langid(prompt: str) -> dict:
    chat = tokenizer.apply_chat_template(
        [{"role":"system","content":SYSTEM_MSG},
         {"role":"user","content":prompt}],
        tokenize=False, add_generation_prompt=True, enable_thinking=False)
    inputs = tokenizer(chat, return_tensors="pt").to(model.device)
    out = model.generate(**inputs, max_new_tokens=220, do_sample=False)
    text = tokenizer.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True)
    return json.loads(re.search(r'\{.*\}', text, re.DOTALL).group(0))

System prompt

The model was trained with the exact system prompt below. Pass it verbatim at inference time — the output schema depends on this prompt.

text
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You are a code language identifier. For the given user prompt, decide whether it contains any embedded source code (program source or recognizable code-like configuration). Output exactly one JSON object and nothing else: {"is_valid": <true|false>, "category": {"<Lang>": true, ...}}.
No preamble. No explanation. No <think> tags. No markdown code fences. No trailing prose.
Rules:
  - is_valid is TRUE when the prompt contains at least one code/config snippet, FALSE when the prompt is plain natural-language only.
  - category contains ONLY the languages that appear, each mapped to true. If no code is present, category is the empty object {}.
  - When multiple languages appear, list every distinct one (still only true).
Allowed language keys (use these exact spellings):
  Python, JavaScript, Java, C, C++, C#, Go, Rust, Kotlin, Swift, Ruby, R, Scala, Perl, Lua, Bash, PowerShell, Batch, SQL, Dockerfile, YAML, Makefile, Terraform, AWK, jq

Examples:

Input: What's the weather forecast today?
Output: {"is_valid": false, "category": {}}

Input: Run this for me: print('hello world')
Output: {"is_valid": true, "category": {"Python": true}}

Input: Compare these — SELECT * FROM users vs the snippet: console.log(users)
Output: {"is_valid": true, "category": {"SQL": true, "JavaScript": true}}

Evaluation

Evaluated on 200 held-out prompts drawn from test_dataset_langid.csv (same single + multi + benign composition as training).

• Evaluation timestamp: 2026-05-22 00:42 UTC
• GPU: NVIDIA A10G
• Source adapter: Accuknoxtechnologies/PromptInjection-Qwen3.5-2B-v5
• JSON parse errors: 0/200 (0.0%)

Top-level metrics

Confusion matrix — binary is_valid decision

Positive class = the prompt contains code (is_valid=True).

Per-language metrics

Only languages that appear in either the actual or predicted labels are listed.

Inference latency

• Mean: 0.99 s/prompt
• Median: 0.94 s/prompt
• p95: 1.35 s/prompt
• Max: 1.63 s/prompt

Training setup

• Base model: Qwen/Qwen3.5-2B (loaded in full precision (bf16 / fp16, no bitsandbytes quantization))
• LoRA: r=16, alpha=32, dropout=0.05, target modules = {q,k,v,o,gate,up,down}_proj
• Optimizer: adamw_torch, lr=1e-4, cosine schedule, warmup 5%
• Precision: bf16 if available, else fp16
• Effective batch size: 8 (per-device 1 + grad-accum 8), gradient checkpointing on
• Max sequence length: 3200 tokens
• Training data: 10,000 rows (7,000 single-language + 2,000 multi-language + 1,000 benign)
• Languages: 25 (programming + config formats)

Supported languages

The model emits one or more of these keys in the category map of its JSON output:

markdown
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Python, JavaScript, Java, C, C++, C#, Go, Rust, Kotlin, Swift, Ruby, R, Scala, Perl, Lua, Bash, PowerShell, Batch, SQL, Dockerfile, YAML, Makefile, Terraform, AWK, jq

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Model card generated automatically by eval_and_push_card.py on 2026-05-22 00:42 UTC.