Update README with accurate stats (86 rows, no empty/missing data)

README.md

@@ -67,47 +67,96 @@ dataset_info:
       dtype: string
   splits:
     - name: train
-      num_examples: 611
+      num_examples: 86
 ---
 
 # Pass@k GPU Benchmark Results
 
 GPU benchmark results for agent-generated optimization patches from [ISO-Bench](https://huggingface.co/datasets/Lossfunk/ISO-Bench).
 
-Patches sourced from [Inferencebench/pass-at-k-samples](https://huggingface.co/datasets/Inferencebench/pass-at-k-samples), benchmarked on NVIDIA H100 GPU using Docker-containerized vLLM.
+Patches sourced from [Inferencebench/pass-at-k-samples](https://huggingface.co/datasets/Inferencebench/pass-at-k-samples), benchmarked on NVIDIA H100 80GB GPU using Docker-containerized vLLM.
 
-## Schema
+## Summary
 
-| Column | Description |
-|--------|-------------|
-| `item_id` | Task identifier (e.g., `vllm_core-0000`) |
-| `sample_index` | Pass@k sample index (0-7) |
-| `agent_name` | Agent that generated the patch (`claude_code`, `codex_cli`) |
-| `model_name` | Model used by agent (`sonnet`, `claude_model-claude-sonnet-4-5`, `gpt-5`) |
-| `status` | `success`, `empty_patch`, `image_not_found`, `benchmark_failed` |
-| `benchmark_mode` | `serving`, `standalone`, `prefix_caching` |
-| `llm_model` | LLM model used in benchmark (e.g., `meta-llama/Meta-Llama-3-8B-Instruct`) |
-| `ttft_mean_ms` | Mean Time To First Token (serving) |
-| `tpot_mean_ms` | Mean Time Per Output Token (serving) |
-| `itl_mean_ms` | Mean Inter-Token Latency (serving) |
-| `request_throughput_req_s` | Request throughput (serving) |
-| `output_token_throughput_tok_s` | Output token throughput (serving) |
-| `throughput_tok_s` | Token throughput (standalone/latency) |
-| `latency_avg_ms` | Average latency (latency benchmarks) |
+- **86 total rows** (79 successful benchmarks, 7 benchmark failures)
+- **12 tasks** benchmarked across **3 agent/model** configurations
+- Benchmark types: serving, standalone (latency/throughput), prefix caching
 
 ## Status Breakdown
 
 | Status | Count | Description |
 |--------|-------|-------------|
 | `success` | 79 | Benchmark completed, metrics captured |
-| `empty_patch` | 457 | Agent produced no patch (error during collection) |
-| `image_not_found` | 68 | Baseline Docker image not available |
-| `benchmark_failed` | 7 | Patch applied but benchmark errored |
+| `benchmark_failed` | 7 | Patch applied but benchmark errored (server crash, metric parse failure) |
 
 ## Agents
 
-| Agent | Model | Successes |
-|-------|-------|-----------|
+| Agent | Model | Successful Benchmarks |
+|-------|-------|-----------------------|
 | claude_code | claude_model-claude-sonnet-4-5 | 69 |
 | claude_code | sonnet | 9 |
 | codex_cli | gpt-5 | 1 |
+
+## Metrics
+
+### Serving Benchmarks
+| Column | Description |
+|--------|-------------|
+| `ttft_mean_ms` | Mean Time To First Token |
+| `ttft_median_ms` | Median Time To First Token |
+| `ttft_p99_ms` | P99 Time To First Token |
+| `tpot_mean_ms` | Mean Time Per Output Token |
+| `tpot_median_ms` | Median Time Per Output Token |
+| `tpot_p99_ms` | P99 Time Per Output Token |
+| `itl_mean_ms` | Mean Inter-Token Latency |
+| `itl_median_ms` | Median Inter-Token Latency |
+| `itl_p99_ms` | P99 Inter-Token Latency |
+| `request_throughput_req_s` | Request throughput (req/s) |
+| `output_token_throughput_tok_s` | Output token throughput (tok/s) |
+| `total_token_throughput_tok_s` | Total token throughput (tok/s) |
+
+### Latency Benchmarks
+| Column | Description |
+|--------|-------------|
+| `latency_avg_ms` | Average latency (ms) |
+| `latency_p50_ms` | P50 latency (ms) |
+| `latency_p99_ms` | P99 latency (ms) |
+| `throughput_tok_s` | Token throughput (tok/s) |
+
+### Prefix Caching Benchmarks
+| Column | Description |
+|--------|-------------|
+| `input_throughput_tok_s` | Input throughput (tok/s) |
+| `throughput_tok_s` | Output throughput (tok/s) |
+| `elapsed_time_s` | Total elapsed time (s) |
+
+## Per-Task Results
+
+| Task | Samples | Benchmark Mode | LLM Model | Avg Throughput |
+|------|---------|---------------|-----------|----------------|
+| vllm_core-0000 | 5 | serving | Qwen/Qwen2.5-7B-Instruct | 3176.4 tok/s |
+| vllm_core-0003 | 7 | serving | deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct | 2124.8 tok/s |
+| vllm_core-0004 | 8 | serving | meta-llama/Meta-Llama-3-8B-Instruct | 2891.3 tok/s |
+| vllm_core-0005 | 8 | serving | meta-llama/Meta-Llama-3-8B-Instruct | 2885.0 tok/s |
+| vllm_core-0006 | 8 | standalone | unknown | 1177.4 tok/s |
+| vllm_core-0007 | 8 | standalone | meta-llama/Meta-Llama-3-8B-Instruct | 8160.0 tok/s |
+| vllm_core-0008 | 8 | serving | meta-llama/Meta-Llama-3-8B-Instruct | 2889.1 tok/s |
+| vllm_core-0009 | 10 | serving | Qwen/Qwen2.5-1.5B-Instruct | 6268.7 tok/s |
+| vllm_core-0010 | 4 | prefix_caching | RedHatAI/Meta-Llama-3-8B-Instruct-FP8 | 5447.6 tok/s |
+| vllm_core-0011 | 3 | prefix_caching | RedHatAI/Meta-Llama-3-8B-Instruct-FP8 | 5380.7 tok/s |
+| vllm_core-0012 | 8 | serving | meta-llama/Meta-Llama-3-8B-Instruct | 2039.1 tok/s |
+| vllm_core-0013 | 2 | serving | meta-llama/Meta-Llama-3-8B-Instruct | 2909.3 tok/s |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Inferencebench/pass-at-k-benchmark-results", split="train")
+
+# Filter to successful benchmarks
+success = ds.filter(lambda x: x["status"] == "success")
+
+# Get results for a specific task
+task_results = success.filter(lambda x: x["item_id"] == "vllm_core-0004")
+```