Update README.md

README.md

@@ -1,37 +1,71 @@
 ---
 base_model: Qwen/Qwen2.5-1.5B-Instruct
 library_name: transformers
-model_name: Qwen-1.5B-GRPO
+model_name: Qwen2.5-1.5B-Thinking-v1.1
 tags:
 - generated_from_trainer
 - trl
 - grpo
 licence: license
+datasets:
+- microsoft/orca-math-word-problems-200k
+# model-index:
+#   - name: Qwen2.5-1.5B-Thinking-v1.1
+#     results:
+#       - task:
+#           type: text-generation
+#         dataset:
+#           name: openai/gsm8k
+#           type: GradeSchoolMath8K
+#         metrics:
+#           - name: GSM8k (0-Shot)
+#             type: GSM8k (0-Shot)
+#             value: 14.4%
+#           - name: GSM8k (Few-Shot)
+#             type: GSM8k (Few-Shot)
+#             value: 63.31%
+co2_eq_emissions:
+  emissions: 7100
+  source: "https://mlco2.github.io/impact#compute"
+  training_type: "GRPO"
+  geographical_location: "East US2"
+  hardware_used: "1 x H100 96GB"
+
 ---
 
-# Model Card for Qwen-1.5B-GRPO
+# Model Card for Qwen2.5-1.5B-Thinking-v1.1
 
 This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct).
 It has been trained using [TRL](https://github.com/huggingface/trl).
 
-## Quick start
 
-```python
-from transformers import pipeline
+<!-- ## Evals
 
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="justinj92/Qwen-1.5B-GRPO", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
+| Model                                    | GSM8k 0-Shot | GSM8k Few-Shot |
+|------------------------------------------|------------------|-------------------|
+| Mistral-7B-v0.1                          | 10             | 41              |
+| Qwen2.5-1.5B-Thinking             | 14.4             | 63.31                 |
+ -->
 
 ## Training procedure
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/justinjoy-5/huggingface/runs/fj1ij9cn) 
+<img src="https://raw.githubusercontent.com/wandb/wandb/fc186783c86c33980e5c73f13363c13b2c5508b1/assets/logo-dark.svg" alt="Weights & Biases Logged" width="150" height="24"/>
+
+<img src="https://huggingface.co/justinj92/Qwen2.5-1.5B-Thinking/resolve/main/w%26b_qwen_r1.png" width="1200" height="900"/>
 
+Trained on 1xH100 96GB via Azure Cloud (East US2).
 
 This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
 
+### Usage Recommendations
+
+**Recommend adhering to the following configurations when utilizing the models, including benchmarking, to achieve the expected performance:**
+
+1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.
+2. **For mathematical problems, it is advisable to include a directive in your prompt such as: "Please reason step by step, and put your final answer within \boxed{}."**
+3. When evaluating model performance, it is recommended to conduct multiple tests and average the results.
+4. This model is not enhanced for other domains apart from Maths.
+
 ### Framework versions
 
 - TRL: 0.15.0.dev0