ExLlamaV3: Attach AdaptiveP, fix speculative decoding parameter, add seed

modules/exllamav3.py

@@ -9,6 +9,7 @@ from exllamav3.cache import CacheLayer_fp16, CacheLayer_quant
 from exllamav3.generator import Job
 from exllamav3.generator.sampler import (
     CustomSampler,
+    SS_AdaptiveP,
     SS_Argmax,
     SS_MinP,
     SS_PresFreqP,
@@ -158,7 +159,7 @@ class Exllamav3Model:
             tokenizer=tokenizer,
             draft_model=draft_model,
             draft_cache=draft_cache,
-            num_speculative_tokens=shared.args.draft_max if draft_model is not None else 0,
+            num_draft_tokens=shared.args.draft_max if draft_model is not None else 0,
         )
 
         result = cls()
@@ -302,7 +303,11 @@ class Exllamav3Model:
             ordered_samplers = sorted(unordered_samplers, key=custom_sort_key)
 
             # 5. Add the final sampling stage and build the sampler
-            ordered_samplers.append(SS_Sample())
+            if state.get('adaptive_target', 0) > 0:
+                ordered_samplers.append(SS_AdaptiveP(state['adaptive_target'], state['adaptive_decay']))
+            else:
+                ordered_samplers.append(SS_Sample())
+
             sampler = CustomSampler(ordered_samplers)
 
         # Encode prompt with embeddings (ExLlamaV3-specific)
@@ -329,12 +334,14 @@ class Exllamav3Model:
             if hasattr(self.tokenizer, 'eos_token_id') and self.tokenizer.eos_token_id is not None:
                 stop_conditions.append(self.tokenizer.eos_token_id)
 
+        seed = state.get('seed', -1)
         job = Job(
             input_ids=input_ids,
             max_new_tokens=max_new_tokens,
             decode_special_tokens=not state['skip_special_tokens'],
             embeddings=image_embeddings if image_embeddings else None,
             sampler=sampler,
+            seed=seed if seed >= 0 else None,
             stop_conditions=stop_conditions if stop_conditions else None,
         )
 

modules/loaders.py

@@ -276,6 +276,8 @@ loaders_samplers = {
         'min_p',
         'top_p',
         'top_k',
+        'adaptive_target',
+        'adaptive_decay',
         'repetition_penalty',
         'frequency_penalty',
         'presence_penalty',