"""
Q2 Part B: reasoning_effort Parameter Analysis

Problem:
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When users include a reasoning_effort parameter in requests to this model, it has
no observable effect on the generated output.

Root Cause:
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• The model (Qwen3ForCausalLM) is a standard causal language model, not a reasoning-capable
  model. Only specialized models like OpenAI's o1 or o3 series support reasoning_effort.

• The inference engine doesn't recognize or process the reasoning_effort parameter. It's
  either ignored or passed through without any logic to actually use it.

• There's no code that maps the reasoning_effort value to any model generation parameters.

• The model cannot generate intermediate reasoning tokens. Since it generates output tokens
  directly without a reasoning phase, there's nothing for the parameter to control.

Requirements to Make reasoning_effort Functional:
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• Use a reasoning-capable model architecture:
  - Replace with o1, o3, or similar reasoning-capable models
  - Or fine-tune existing model to support reasoning token generation
  - Model must have built-in capability to generate reasoning tokens

• Update the inference engine:
  - Add code to parse reasoning_effort parameter from API requests
  - Implement logic to generate reasoning tokens
  - Add token classification to distinguish reasoning tokens from output tokens
  - Map effort levels (low/medium/high) to reasoning parameters
  - Pass reasoning_effort to model's forward pass

• Modify the API server:
  - Add reasoning_effort to request schema validation
  - Accept parameter from client requests
  - Pass parameter through to inference engine
  - Handle reasoning tokens separately in response formatting
  - Optionally return reasoning tokens in API response

• Update model configuration:
  - Add reasoning_config section to config.json
  - Configure max_reasoning_tokens based on effort level
  - Specify reasoning token IDs
  - Define reasoning_effort_levels supported

• Implement architectural changes:
  - Create token routing pipeline to separate reasoning and output tokens
  - Add logic to control reasoning depth/length based on effort level
  - Update token stream processing to handle reasoning vs output distinction

Summary:
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This isn't a simple configuration fix. It requires:
• Architectural changes (reasoning-capable model + engine support)
• Implementation changes (API server + token processing)
• Configuration updates (model config + reasoning settings)

The current model simply doesn't have the capability to support this feature.

"""