Update README.md

README.md

@@ -4,17 +4,10 @@ task_categories:
 - text-generation
 language:
 - en
-tags:
-- Reasoning
 size_categories:
 - 1K<n<10K
 ---
 
-## about Eval.py
-> **How to Run Eval:**
-> 1. Prepare a CSV with two columns: `target` (the truth) and `prediction` (the model's full output).
-> 2. Run: `python eval.py --csv results.csv`
-> 3. The script will automatically clean spaces, extract the final 9 digits, and calculate the Exact Match score.
 
 # 🧩 NumPuzzle-Easy: The 1,500-Step Spatial Reasoning Nightmare
 
@@ -68,39 +61,6 @@ We invite all researchers and AI enthusiasts to post their evaluation logs in th
 
 ### 🛠 Evaluation Protocol: The Strict Mandate
 
-This benchmark is not a test of linguistic creativity or "vibes." It is a cold, binary assessment of a model's ability to maintain a deterministic world model. To maintain the integrity of NumPuzzle-Easy, all evaluators must adhere to the following rigorous protocol.
-
-#### 1. The "Exact Match" (EM) Mandate
-We reject the use of fuzzy matching, BLEU, ROUGE, or any other semantic similarity metrics. In the realm of spatial reasoning, "almost correct" is fundamentally "wrong." If a model calculates 14 out of 15 moves correctly, its 15th move and the subsequent 4-chain reaction will be catastrophic. Therefore, the **only valid metric is Exact Match (EM).**
-- **Criteria:** The output must be exactly the 9-digit sequence representing Row 1.
-- **Fail Case:** `086738476` vs `086730476` (Result: **0**).
-
-#### 2. The Leading Zero Trap (The "No Mercy" Policy)
-One of the most common failures in LLMs is the loss of numeric context. In this puzzle, Row 1 can frequently begin with one or more zeros (e.g., `001234567`). 
-- **The Rule:** The model must output exactly **9 digits**. 
-- **The Rationale:** If a model outputs 8 digits (e.g., `12345678`), it indicates that the LLM has collapsed the grid representation into a standard integer, losing the spatial awareness of the 9-column architecture. This is an immediate failure. No post-processing or "fixing" of the output is allowed. 
-
-#### 3. Zero-Shot vs. Reasoning Trace 
-To observe the pure architectural limits, we recommend **Zero-Shot evaluation**. However, for "Reasoning" models (o1, DeepSeek-R1, Qwen 3.6 Plus), the internal "Thinking Process" or "Chain of Thought" (CoT) should be preserved in logs.
-- **What to look for:** Does the model's internal grid representation "drift" over time? Most models will lose the position of the active orb around Move 7 or 8. We encourage evaluators to share these logs in the Community tab to visualize the "moment of collapse."
-
-#### 4. Community-Driven Verification (Transparency Protocol)
-As the creator, I do not provide an "Official Leaderboard" based on my own tests. To prevent "Benchmark Laundering" (where providers cherry-pick successful runs), the truth must be crowdsourced. 
-- **How to Submit:** Please open a thread in the **[Community Tab]** with your model's name. 
-- **Requirements:** 
-    - Full transparency on the prompt used.
-    - If testing a subset (e.g., 100/1500), specify the exact row indices.
-    - Post a screenshot or a JSON dump of the failed responses. 
-- **Verification:** Only results that can be replicated by other community members will be recognized as the definitive score for a given model version.
-
-#### 5. Cost-Effective Testing
-We recognize that 1,500 samples of o1-level reasoning are expensive. 
-- **Sampling:** For quick validation, a random sample of **100 rows** is acceptable. Given the 1% failure rate, a 100-sample test is statistically sufficient to prove a model's inability to solve this benchmark. 
-- **Stop-Loss:** If your model fails the first 50 consecutive puzzles, it is safe to assume an accuracy of <2%. You may stop there and report the result as "Complete Failure."
-
-**"The grid does not care about your reasoning. It only cares about the truth."**
-
----
 
 # 📂 File Structure & Usage
 
@@ -129,6 +89,39 @@ If you are running your own Python script, you can use either file.
 1. **Statistical Power:** With 1,500 problems, any "lucky guess" from an LLM is statistically irrelevant. 
 2. **Cost-Efficiency:** Small enough to be processed via OpenAI/Anthropic APIs within a few hours (overnight), keeping benchmarking costs reasonable while ensuring total failure for the model.
 
+## 📊 Call for Evaluation: Test your model on the NumPuzzle-Easy Benchmark!
+
+We are crowdsourcing evaluation results for **NumPuzzle-Easy**! 
+This benchmark evaluates how accurately an LLM can identify and extract target numbers of varying lengths from complex contexts.
+
+Instead of waiting in long queues on centralized leaderboards, you can evaluate your model locally using Hugging Face's official `Lighteval` tool. By running the command below, the results will be automatically verified and submitted to your model's repository as a Pull Request!
+
+### 🚀 How to evaluate your model
+
+**1. Install Lighteval**
+```bash
+pip install lighteval
+```
+
+**2. Download the task script**
+Make sure you have the `numpuzzle_task.py` script in your working directory.
+
+**3. Run the evaluation**
+Run the following command. Replace `[YOUR_MODEL_ID]` with the Hugging Face ID of the model you want to test (e.g., `meta-llama/Meta-Llama-3-8B-Instruct`).
+
+```bash
+lighteval accelerate \
+    --model_args "pretrained=[YOUR_MODEL_ID]" \
+    --custom_tasks "Evai.py" \
+    --tasks "custom|numpuzzle-easy|0|0" \
+    --push_to_hub \
+    --results_org "[YOUR_HF_USERNAME]"
+```
+
+### 💡 What happens next?
+By using the `--push_to_hub` flag, Lighteval will automatically generate a JSON/YAML report with your score and push it to the Hub. You can then show off your verified NumPuzzle score on your model card!
+
+We look forward to seeing your results! Let's find out which model is the true master of numbers. 🧩🔢
 
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