Add temperature, top_p, and smart max_tokens defaults

- Set temperature=1.0 and top_p=1.0 as defaults (OpenAI recommended)
- Add --temperature and --top-p flags for customization
- Auto-scale max_new_tokens based on reasoning_effort:
- low: 512 tokens
- medium: 1024 tokens
- high: 2048 tokens (prevents truncation)
- Document OpenAI's sampling recommendations in README
- Users can still override with explicit --max-new-tokens

README.md

@@ -28,8 +28,15 @@ hf jobs uv run --flavor l4x4 --secrets HF_TOKEN=hf_*** \
 | `--prompt-column` | Column containing prompts | `prompt` |
 | `--model-id` | Model to use | `openai/gpt-oss-20b` |
 | `--max-samples` | Limit samples to process | None (all) |
-| `--max-new-tokens` | Max tokens to generate | `1024` |
+| `--max-new-tokens` | Max tokens to generate | Auto-scales: 512/1024/2048 |
 | `--reasoning-effort` | Reasoning depth: low/medium/high | `medium` |
+| `--temperature` | Sampling temperature | `1.0` |
+| `--top-p` | Top-p sampling | `1.0` |
+
+**Note**: `max-new-tokens` auto-scales based on `reasoning-effort` if not set:
+- `low`: 512 tokens
+- `medium`: 1024 tokens  
+- `high`: 2048 tokens (prevents truncation of detailed reasoning)
 
 ## 💡 What You Get
 
@@ -102,6 +109,12 @@ The `reasoning_effort` parameter controls how much chain-of-thought reasoning th
 - `medium`: Balanced reasoning (default)
 - `high`: Detailed step-by-step reasoning
 
+### Sampling Parameters
+OpenAI recommends `temperature=1.0` and `top_p=1.0` as defaults for GPT OSS models:
+- These settings provide good diversity without compromising quality
+- The model was trained to work well with these parameters
+- Adjust only if you need specific behavior (e.g., lower temperature for more deterministic output)
+
 ## 📚 Resources
 
 - [Model: openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b)

gpt_oss_minimal.py

@@ -60,15 +60,37 @@ def main():
     parser.add_argument("--model-id", default="openai/gpt-oss-20b", help="Model to use")
     parser.add_argument("--max-samples", type=int, help="Limit number of samples")
     parser.add_argument(
-        "--max-new-tokens", type=int, default=1024, help="Max tokens to generate"
+        "--max-new-tokens", type=int, help="Max tokens to generate (auto-scales with reasoning effort if not set)"
     )
     parser.add_argument(
-        "--reasoning-effort", 
-        choices=["low", "medium", "high"], 
+        "--reasoning-effort",
+        choices=["low", "medium", "high"],
         default="medium",
-        help="Reasoning effort level (default: medium)"
+        help="Reasoning effort level (default: medium)",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=1.0,
+        help="Sampling temperature (default: 1.0)",
+    )
+    parser.add_argument(
+        "--top-p",
+        type=float,
+        default=1.0,
+        help="Top-p sampling (default: 1.0)",
     )
     args = parser.parse_args()
+    
+    # Auto-scale max_new_tokens based on reasoning effort if not explicitly set
+    if args.max_new_tokens is None:
+        if args.reasoning_effort == "high":
+            args.max_new_tokens = 2048  # More tokens for detailed reasoning
+        elif args.reasoning_effort == "medium":
+            args.max_new_tokens = 1024
+        else:  # low
+            args.max_new_tokens = 512
+        print(f"Auto-set max_new_tokens={args.max_new_tokens} based on reasoning_effort={args.reasoning_effort}")
 
     # Check GPU availability
     if not torch.cuda.is_available():
@@ -136,20 +158,21 @@ def main():
 
         # Apply chat template with reasoning_effort parameter
         inputs = tokenizer.apply_chat_template(
-            messages, 
-            add_generation_prompt=True, 
-            return_tensors="pt", 
+            messages,
+            add_generation_prompt=True,
+            return_tensors="pt",
             return_dict=True,
-            reasoning_effort=args.reasoning_effort  # "low", "medium", or "high"
+            reasoning_effort=args.reasoning_effort,  # "low", "medium", or "high"
         ).to(model.device)
 
-        # Generate
+        # Generate with user-specified or default parameters
         with torch.no_grad():
             generated = model.generate(
                 **inputs,
                 max_new_tokens=args.max_new_tokens,
                 do_sample=True,
-                temperature=0.7,
+                temperature=args.temperature,
+                top_p=args.top_p,
             )
 
         # Decode only the generated part (excluding input)