Fix repetition_penalty default causing 24% TPS drop in GGUF inference (#4634)

The ChatCompletionRequest Pydantic model defaulted repetition_penalty
to 1.1 when clients omitted the field. This silently forced
llama-server to perform per-token repetition scanning, dropping
streaming throughput from ~225 TPS to ~172 TPS (a 24% penalty).

The Studio frontend always sends repetition_penalty=1.0 explicitly,
so UI users were unaffected. But any API client hitting
/v1/chat/completions without setting the field (curl, third-party
integrations, Open WebUI, etc.) would get the slow path.

Benchmarked on Qwen3.5-4B Q4_K_XL, GPU 0:
- repeat_penalty=1.0: 225.2 TPS
- repeat_penalty=1.1: 172.7 TPS (24% slower)
- LM Studio (which applies rp internally): 170.8 TPS

This aligns the Pydantic default with the frontend default (1.0),
generate_chat_completion's function signature default (1.0), and
llama-server's own default (1.0).

studio/backend/models/inference.py

@@ -291,7 +291,7 @@ class ChatCompletionRequest(BaseModel):
         0.01, ge = 0.0, le = 1.0, description = "[x-unsloth] Min-p sampling threshold"
     )
     repetition_penalty: float = Field(
-        1.1, ge = 1.0, le = 2.0, description = "[x-unsloth] Repetition penalty"
+        1.0, ge = 1.0, le = 2.0, description = "[x-unsloth] Repetition penalty"
     )
     image_base64: Optional[str] = Field(
         None, description = "[x-unsloth] Base64-encoded image for vision models"