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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,7 @@ def convert_lmi_schema_to_completion_request(
parameters = payload.get("parameters", {})

completion_dict = {
"model": payload.pop("model"),
"prompt": payload.pop("inputs"),
"max_tokens": parameters.pop("max_new_tokens", 30),
"echo": parameters.pop("return_full_text", False),
Expand Down
21 changes: 5 additions & 16 deletions engines/python/setup/djl_python/lmi_vllm/vllm_async_service.py
Original file line number Diff line number Diff line change
Expand Up @@ -165,6 +165,8 @@ def preprocess_request(self, inputs: Input) -> ProcessedRequest:
logging.info(
f"Using LoRA request: {lora_request.lora_name} (ID: {lora_request.lora_int_id})"
)
# Set the model field to the adapter name so vLLM's _maybe_get_adapters() can extract it
decoded_payload["model"] = adapter_name

# completions request
if "prompt" in decoded_payload:
Expand Down Expand Up @@ -238,22 +240,9 @@ async def inference(
"", error=f"Input parsing failed: {str(e)}", code=424)
return output

if processed_request.lora_request:
original_add_request = self.vllm_engine.add_request

async def add_request_with_lora(*args, **kwargs):
kwargs['lora_request'] = processed_request.lora_request
return await original_add_request(*args, **kwargs)

self.vllm_engine.add_request = add_request_with_lora
try:
response = await processed_request.inference_invoker(
processed_request.vllm_request)
finally:
self.vllm_engine.add_request = original_add_request
else:
response = await processed_request.inference_invoker(
processed_request.vllm_request)
# vLLM will extract the adapter from the request object via _maybe_get_adapters()
response = await processed_request.inference_invoker(
processed_request.vllm_request)

if isinstance(response, types.AsyncGeneratorType):
# Apply custom formatter to streaming response
Expand Down
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