feat/add-support-qwen3

validator/modules/llm_judge/__init__.py

@@ -14,8 +14,8 @@
 from typing import List, Dict, Any
 from validator.modules.llm_judge.prompt import get_prompt
 from validator.modules.llm_judge.utils import download_file
+from validator.modules.llm_judge.constant import SUPPORTED_BASE_MODELS
 from validator.exceptions import LLMJudgeException, InvalidModelParametersException
-from validator.modules.llm_judge.template import template_dict
 from peft import PeftModel
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from validator.modules.base import (
@@ -37,7 +37,7 @@
 class LLMJudgeConfig(BaseConfig):
     gen_batch_size: int = 1
     eval_batch_size: int = 16
-    gen_temperature: float = 0.1
+    gen_temperature: float = 0.7
 
 
 class LLMJudgeMetrics(BaseMetrics):
@@ -144,7 +144,6 @@ def _load_model(self, repo_id: str, revision: str = "main", max_params: int = No
         model_kwargs = dict(
             trust_remote_code=True,
             torch_dtype=compute_dtype,
-            use_cache=False,
             device_map="auto",
         )
         if is_lora:
@@ -157,6 +156,18 @@ def _load_model(self, repo_id: str, revision: str = "main", max_params: int = No
             with open("judge/adapter_config.json", "r") as f:
                 adapter_config = json.load(f)
             base_model = adapter_config["base_model_name_or_path"]
+            if base_model in SUPPORTED_BASE_MODELS:
+                logger.info(
+                    f"LoRA's base model '{base_model}' is in SUPPORTED_BASE_MODELS. "
+                    f"Using it for tokenizer."
+                )
+            else:
+                logger.error(
+                    f"LoRA's base model '{base_model}' is not in SUPPORTED_BASE_MODELS. "
+                    f"Marking assignment as failed."
+                )
+                raise
+
             self.hf_tokenizer = AutoTokenizer.from_pretrained(
                 base_model, trust_remote_code=True, use_fast=True, padding_side="left"
             )
@@ -189,88 +200,6 @@ def _load_model(self, repo_id: str, revision: str = "main", max_params: int = No
                 f"Model parameters {total} exceed limit {max_params}"
             )
 
-    def _construct_conversation_template(
-        self,
-        conversation: List[Dict[str, str]],
-        base_model: str,
-    ) -> str:
-        try:
-            if base_model not in template_dict:
-                logger.info(f"Template {base_model} not found, using default")
-                base_model = "default"
-
-            template = template_dict[base_model]
-
-            conversation_parts = []
-
-            # Validate conversation structure
-            if not isinstance(conversation, dict):
-                raise LLMJudgeException(
-                    f"Conversation must be a dict, got {type(conversation)}"
-                )
-
-            if "conversations" not in conversation:
-                raise LLMJudgeException(
-                    f"Conversation dict must have 'conversations' key"
-                )
-
-            if not conversation["conversations"]:
-                raise LLMJudgeException(f"Conversation 'conversations' list is empty")
-
-            # Use provided system_text or fall back to template default
-            if template.system_format:
-                system_prompt = (
-                    conversation["system"] if "system" in conversation else None
-                )
-                system_content = (
-                    system_prompt if system_prompt else "You are a helpful assistant."
-                )
-                if system_content:
-                    formatted_system = template.system_format.format(
-                        content=system_content
-                    )
-                    conversation_parts.append(formatted_system)
-
-            # Multi-turn conversation: format each message according to template
-            for msg in conversation["conversations"]:
-                if (
-                    not isinstance(msg, dict)
-                    or "role" not in msg
-                    or "content" not in msg
-                ):
-                    logger.warning(f"Skipping invalid message: {msg}")
-                    continue
-
-                if msg["role"] == "user":
-                    user_text = template.user_format.format(
-                        content=msg["content"],
-                        stop_token=self.hf_tokenizer.eos_token,
-                    )
-                    conversation_parts.append(user_text)
-                elif msg["role"] == "assistant":
-                    assistant_text = template.assistant_format.format(
-                        content=msg["content"],
-                        stop_token=self.hf_tokenizer.eos_token,
-                    )
-                    conversation_parts.append(assistant_text)
-
-            conversation_format = "".join(conversation_parts)
-
-            if not conversation_format.strip():
-                logger.error(
-                    f"Empty template generated. Template: {base_model}, Conversation: {conversation}, Parts: {conversation_parts}"
-                )
-                raise LLMJudgeException(
-                    f"Generated conversation template is empty after formatting"
-                )
-
-        except Exception as e:
-            raise LLMJudgeException(
-                f"Failed to construct conversation template: {e}"
-            ) from e
-
-        return conversation_format
-
     def _generate_response(
         self,
         context_length: int,
@@ -294,9 +223,20 @@ def _generate_response(
                 # Apply chat template with fallback
                 batch_conversation_templates = []
                 for conversation in batch_conversations:
-                    template = self._construct_conversation_template(
-                        conversation,
-                        base_model=base_model,
+
+                    messages = []
+                    if "system" in conversation:
+                        messages.append({
+                            "role": "system",
+                            "content": conversation["system"]
+                        })
+
+                    messages += conversation["conversations"]
+                    template = self.hf_tokenizer.apply_chat_template(
+                        messages,
+                        tokenize=False,
+                        add_generation_prompt=True,
+                        enable_thinking=False,
                     )
 
                     # Validate template is not empty
@@ -353,10 +293,10 @@ def _generate_response(
                     outputs = self.hf_model.generate(
                         **model_inputs,
                         max_new_tokens=max_length,
-                        temperature=self.config.gen_temperature,
+                        temperature=self.config.gen_temperature,  # Non thinking-General 0.7 ,Reasoning 1
                         do_sample=True,
-                        top_p=0.95,  # Nucleus sampling for stability
-                        top_k=50,  # Limit vocabulary for stability
+                        top_p=0.8,  # Non thinking-General 0.8 ,Reasoning 0.95
+                        top_k=20,    #
                         pad_token_id=self.hf_tokenizer.eos_token_id,
                         eos_token_id=self.hf_tokenizer.eos_token_id,
                     )
@@ -916,7 +856,10 @@ def validate(self, data: LLMJudgeInputData, **kwargs) -> LLMJudgeMetrics:
             self._load_model(data.hg_repo_id, data.revision, data.max_params)
         except InvalidModelParametersException as e:
             # lowest possible reward for invalid model parameters
-            logger.info(f"Invalid model parameters: {e}")
+            logger.error(f"Invalid model parameters: {e}")
+            return LLMJudgeMetrics(score=LOWEST_POSSIBLE_SCORE)
+        except Exception as e:
+            logger.error(f"Exception when load model: {e}")
             return LLMJudgeMetrics(score=LOWEST_POSSIBLE_SCORE)
 
         # Stage 1: Generate all responses

validator/modules/llm_judge/constant.py

@@ -0,0 +1,12 @@
+SUPPORTED_BASE_MODELS = [
+    # qwen3.5
+    "Qwen/Qwen3.5-0.8B",
+    "Qwen/Qwen3.5-0.8B-Base",
+    "Qwen/Qwen3.5-2B",
+    "Qwen/Qwen3.5-2B-Base",
+    "Qwen/Qwen3.5-4B",
+    "Qwen/Qwen3.5-4B-Base",
+    "Qwen/Qwen3.5-9B",
+    "Qwen/Qwen3.5-9B-Base",
+    "Qwen/Qwen3.5-27B",
+]

validator/modules/llm_judge/environment.yml

@@ -8,11 +8,11 @@ dependencies:
     - openai>=1.0.0  # OpenAI API client
     - httpx  # HTTP client for OpenAI requests
     - pydantic>=2.0.0  # Data validation and parsing
-    - transformers==4.49.0  # HuggingFace transformers library
+    - transformers==5.3.0  # HuggingFace transformers library
     - torch>=1.13.1  # PyTorch for model inference
     - accelerate>=0.27.2  # For efficient model loading
     - loguru>=0.6.0  # Logging library
-    - huggingface-hub==0.29.1
+    - huggingface-hub==1.5.0
     - tenacity
-    - peft>=0.10.0,<0.18.0
+    - peft==0.18.1
     - python-dotenv  # Load environment variables from .env file