wip deepseek r1

sia/llm_engine/hf_llm_engine.py

@@ -0,0 +1,84 @@
+from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoConfig
+from typing import Iterator, Optional, Callable
+
+from . import LlmEngine
+
+class HfLlmEngine(LlmEngine):
+    """
+    LLM Engine implementation using HuggingFace's InferenceClient.
+    """
+    
+    def __init__(
+        self,
+        model: str,
+        temperature: float,
+        api_token: Optional[str],
+    ):
+        """
+        Initialize the HuggingFace Inference API LLM Engine.
+
+        Args:
+            model: HuggingFace model ID to use
+            temperature: Sampling temperature
+            api_token: HuggingFace API token
+        """
+        self._model = model
+        self._temperature = temperature
+
+        self._tokenizer = AutoTokenizer.from_pretrained(model, token=api_token)
+        self._config = AutoConfig.from_pretrained(model, token=api_token)
+        self._client = InferenceClient(token=api_token)
+                        
+    def infer(self, system_prompt: str, main_context: str, should_stop: Callable[[], bool] = lambda: False) -> Iterator[str]:
+        """
+        Run inference using the system prompt and main context.
+
+        Args:
+            system_prompt: The system prompt string
+            main_context: The main context string after templating
+            should_stop: Callback that returns True when inference should stop
+
+        Returns:
+            Iterator[str]: An iterator that yields the generated text.
+        """
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": main_context}
+        ]
+        
+        stream = self._client.chat_completion(
+            model=self._model,
+            messages=messages,
+            temperature=self._temperature,
+            stream=True
+        )
+        
+        try:
+            for response in stream:
+                if should_stop():
+                    stream.close()
+                    break
+                if content := response.choices[0].delta.content:
+                    yield content
+        finally:
+            stream.close()
+
+    def token_count(self, system_prompt: str, main_context: str) -> int:
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": main_context}
+        ]
+        prompt = self._tokenizer.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+        return len(self._tokenizer.encode(prompt))
+
+    def token_limit(self) -> int:
+        """
+        Get the model's context window size.
+        
+        Returns:
+            int: Maximum number of tokens the model can process
+        """
+        return self._config.max_position_embeddings