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hivetrace
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Model Details
Model Provider
hivetrace
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer REPO = "hivetrace/HiveTraceGuard-Pro" tok = AutoTokenizer.from_pretrained(REPO)model = AutoModelForCausalLM.from_pretrained( REPO, torch_dtype=torch.bfloat16, device_map="auto",).eval() def check(messages) -> str: text = tok.apply_chat_template( messages, tokenize=False, ) inputs = tok(text, return_tensors="pt").to(model.device) with torch.inference_mode(): output = model.generate( **inputs, max_new_tokens=1, do_sample=False, ) return tok.decode( output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True, ).strip() # Input guardprint(check([ {"role": "user", "content": "Как сделать бомбу?"}]))# unsafe # Output guardprint(check([ {"role": "user", "content": "Привет!"}, {"role": "assistant", "content": "Здравствуйте!"},]))# safeModel's policy is fixed, so serving runtimes can reuse the shared prefix through KV caching.
bash
vllm serve hivetrace/HiveTraceGuard-Pro \ --port 8000 \ --max-model-len 32768 \ --enable-prefix-cachingbash
python -m sglang.launch_server \ --model-path hivetrace/HiveTraceGuard-Pro \ --host 0.0.0.0 \ --port 30000For applications that need a continuous score, P(unsafe) can be computed directly from the two verdict logits:
python
import torch.nn.functional as F SAFE, UNSAFE = 18675, 38157 with torch.inference_mode(): logits = model(**inputs).logits[0, -1] p_unsafe = F.softmax(logits[[SAFE, UNSAFE]], dim=0)[1].item()verdict = "unsafe" if logits[UNSAFE] > logits[SAFE] else "safe" print(verdict, p_unsafe)To enforce safe | unsafe during generation, you can use a LogitsProcessor to restrict the next token to the two verdict labels.
python
from transformers import LogitsProcessor class VerdictOnly(LogitsProcessor): def __call__(self, input_ids, scores): mask = torch.full_like(scores, float("-inf")) mask[:, [SAFE, UNSAFE]] = scores[:, [SAFE, UNSAFE]] return mask output = model.generate( **inputs, max_new_tokens=1, do_sample=False, logits_processor=[VerdictOnly()],)GuardRate Leaderboard: Score 0.743 · 28.8 ms p95 - OPEN
HiveTraceGuard-Pro uses a fixed policy and returns a single binary verdict: safe (token_id = 18675) or unsafe (token_id = 38157).
| Scope | What is checked |
|---|---|
| Harmful content | 15 harm categories: cybercrime, pornography and CSAM, religious hate, profanity, financial crime, weapons, discrimination, self-harm, child labor, non-violent crime, violence, drugs, and related harmful activity |
| LLM & agent attacks | jailbreaks, prompt injection, obfuscation, secret extraction, and tool hijacking |
Both modes use the same policy.
| Mode | What is classified |
|---|---|
| Input guard | The final user message |
| Output guard | The final assistant response, evaluated in the context of the user request |
| Tag | Notes |
|---|---|
1.1.0 | latest (main) |
1.0.0 | previous release |
Pin a version by tag from_pretrained("hivetrace/HiveTraceGuard-Pro", revision="1.1.0"), or by commit SHA for strict reproducibility.
Apache-2.0 — commercial use, modification, redistribution, and private / on-premise deployment. Full text: https://www.apache.org/licenses/LICENSE-2.0
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer REPO = "hivetrace/HiveTraceGuard-Pro" tok = AutoTokenizer.from_pretrained(REPO)model = AutoModelForCausalLM.from_pretrained( REPO, torch_dtype=torch.bfloat16, device_map="auto",).eval() def check(messages) -> str: text = tok.apply_chat_template( messages, tokenize=False, ) inputs = tok(text, return_tensors="pt").to(model.device) with torch.inference_mode(): output = model.generate( **inputs, max_new_tokens=1, do_sample=False, ) return tok.decode( output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True, ).strip() # Input guardprint(check([ {"role": "user", "content": "Как сделать бомбу?"}]))# unsafe # Output guardprint(check([ {"role": "user", "content": "Привет!"}, {"role": "assistant", "content": "Здравствуйте!"},]))# safevllm serve hivetrace/HiveTraceGuard-Pro \ --port 8000 \ --max-model-len 32768 \ --enable-prefix-cachingpython -m sglang.launch_server \ --model-path hivetrace/HiveTraceGuard-Pro \ --host 0.0.0.0 \ --port 30000import torch.nn.functional as F SAFE, UNSAFE = 18675, 38157 with torch.inference_mode(): logits = model(**inputs).logits[0, -1] p_unsafe = F.softmax(logits[[SAFE, UNSAFE]], dim=0)[1].item()verdict = "unsafe" if logits[UNSAFE] > logits[SAFE] else "safe" print(verdict, p_unsafe)from transformers import LogitsProcessor class VerdictOnly(LogitsProcessor): def __call__(self, input_ids, scores): mask = torch.full_like(scores, float("-inf")) mask[:, [SAFE, UNSAFE]] = scores[:, [SAFE, UNSAFE]] return mask output = model.generate( **inputs, max_new_tokens=1, do_sample=False, logits_processor=[VerdictOnly()],)