Xielu support

LICENSE

@@ -1,287 +1,204 @@
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README.md

@@ -19,15 +19,13 @@ Apertus transformer on FastAPI
 
 A FastAPI-based Python application that provides an API to interface with the Apertus LLM from the Swiss AI Initiative.
 
-For more information go to https://huggingface.co/swiss-ai
+The [OpenAI-compatible API](https://medium.com/data-science/how-to-build-an-openai-compatible-api-87c8edea2f06)
+is inspired by [openai-compatible-fastapi](https://github.com/ritun16/openai-compatible-fastapi).
 
-## TODOs
+The goal is to use the [Apertus Format API](https://github.com/swiss-ai/apertus_format) in a lightweight service with an efficient, paged attention mechanism.
 
-- [Apertus Format API](https://github.com/swiss-ai/apertus_format)
-- [OpenAI-compatible API](https://medium.com/data-science/how-to-build-an-openai-compatible-api-87c8edea2f06)
-- ...?
-- Profit!
+For more information on Apertus, go to https://huggingface.co/swiss-ai
 
-Just kidding, this is an open source project under the [European Public Domain License](LICENSE)
+This is an open source project under the [Apache 2.0 License](LICENSE)
 
 If you have further suggestions please leave them on [Codeberg Issues](https://codeberg.org/loleg/fastapi-apertus/issues)

app.py

@@ -7,12 +7,15 @@ from typing import List, Optional
 from torch import cuda
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
+from hashlib import sha256
 from huggingface_hub import login
 from dotenv import load_dotenv
+
 import os
 import uvicorn
-
+import time
 import logging
+
 # Configure logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -26,12 +29,15 @@ if hf_token is not None:
 # Configurable model identifier
 model_name = os.getenv("HF_MODEL", "swiss-ai/Apertus-8B-Instruct-2509")
 
+# Configure max tokens
+MAX_NEW_TOKENS = 4096
+
 # Keep data in session
 model = None
 tokenizer = None
 
 class TextInput(BaseModel):
-    text: str
+    text: str = ""
     min_length: int = 3
     # Apertus by default supports a context length up to 65,536 tokens.
     max_length: int = 65536
@@ -42,8 +48,8 @@ class ModelResponse(BaseModel):
     processing_time: float
 
 class ChatMessage(BaseModel):
-    role: str
-    content: str
+    role: str = "user"
+    content: str = ""
 
 class Completion(BaseModel):
     model: str = "apertus"
@@ -117,6 +123,10 @@ def get_completion_text(messages_think: List[ChatMessage]):
     return txt
 
 
+def get_message_id(txt: str):
+    return sha256(str(txt).encode()).hexdigest()
+
+
 def get_model_reponse(messages_think: List[ChatMessage]):
     """Process the text content."""
 
@@ -137,13 +147,13 @@ def get_model_reponse(messages_think: List[ChatMessage]):
     # Generate the output
     generated_ids = model.generate(
         **model_inputs,
-        max_new_tokens=512
+        max_new_tokens=MAX_NEW_TOKENS
     )
 
     # Get and decode the output
     output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
     
-    # Return just the text
+    # Decode the text message
     return tokenizer.decode(output_ids, skip_special_tokens=True)
 
 
@@ -158,15 +168,15 @@ async def completion(data: Completion):
         text = get_completion_text(mt)
         result = get_model_reponse(mt)
 
+        # Standard formatted object
         return {
-            "choices": [
-                {
-                    "text": result,
-                    "_index": 0,
-                    "logprobs": None,
-                    "finish_reason": "length"
-                }
-            ],
+            "id": get_message_id(text),
+            "object": "chat.completion",
+            "created": time.time(),
+            "model": data.model,
+            "choices": [{
+                "message": ChatMessage(role="assistant", content=result)
+            }],
             "usage": {
                 "prompt_tokens": len(text),
                 "completion_tokens": len(result),
@@ -185,7 +195,6 @@ async def predict(q: str):
         raise HTTPException(status_code=503, detail="Model not loaded")
 
     try:
-        import time
         start_time = time.time()
 
         input_data = TextInput(text=q)

pyproject.toml

@@ -12,4 +12,9 @@ dependencies = [
   "torch>=2.8.0",
   "transformers>=4.56.0",
   "compressed-tensors>=0.11.0",
+  "numpy>=2.3.2",
+  "xielu",
 ]
+
+[tool.uv.sources]
+xielu = { git = "https://github.com/nickjbrowning/XIELU" }

uv.lock

@@ -139,6 +139,7 @@ dependencies = [
     { name = "accelerate" },
     { name = "compressed-tensors" },
     { name = "fastapi" },
+    { name = "numpy" },
     { name = "pydantic" },
     { name = "torch" },
     { name = "transformers" },
@@ -150,6 +151,7 @@ requires-dist = [
     { name = "accelerate", specifier = ">=1.10" },
     { name = "compressed-tensors", specifier = ">=0.11.0" },
     { name = "fastapi", specifier = ">=0.116" },
+    { name = "numpy", specifier = ">=2.3.2" },
     { name = "pydantic", specifier = ">=2.11" },
     { name = "torch", specifier = ">=2.8.0" },
     { name = "transformers", specifier = ">=4.56.0" },