Fix README.md (#4882)

* Revise example prompt and answer to English

Updated example prompt and answer in README with English content.

* Fix typo issue and add link to generative ai readme.md

Author: raywang1021

generative_ai/README.md

@@ -6,7 +6,8 @@ These examples showcases Amazon SageMaker's capabilities in the exciting field o
 
 - [Fine-tuning and deploying a Hugging Face summarization model on SageMaker with your own scripts and dataset](sm-finetuning_huggingface_with_your_own_scripts_and_data/sm-finetuning_huggingface_with_your_own_scripts_and_data.ipynb)
 - [Fine-tuning and deploying the Mixtral 8x7B LLM In SageMaker with Hugging Face, using QLoRA Parameter-Efficient Fine-Tuning](sm-mixtral_8x7b_fine_tune_and_deploy/sm-mixtral_8x7b_fine_tune_and_deploy.ipynb)
-- [Serve large models on SageMaker with DeepSpeed Container](sm-djl_deepspeed_bloom_176b_deploy.ipynb)
+- [Qwen 8B LLM Fine-tuning with SFT and GRPO, and Deployment on AWS SageMaker](sm-qwen3_8b_fine_tune_and_deploy/README.md)
+- [Serve large models on SageMaker with DeepSpeed Container](sm-mixtral_8x7b_fine_tune_and_deploy/sm-mixtral_8x7b_fine_tune_and_deploy.ipynb)(sm-djl_deepspeed_bloom_176b_deploy.ipynb)
 - [Accelerate SageMaker-PyTorch FSDP Training of Llama-v2 (or GPT-NeoX) with FP8 on P5 instances](sm-fsdp_training_of_llama_v2_with_fp8_on_p5.ipynb)
 - [Fine-tune Code Llama, Deploy and Evaluate the Fine-tuning with Human-eval Repository](sm-jumpstart_foundation_code_llama_fine_tuning_human_eval.ipynb)
 - [SageMaker JumpStart Foundation Models - Fine-tuning text generation GPT-J 6B model on domain specific dataset](sm-jumpstart_foundation_finetuning_gpt_j_6b_domain_adaptation.ipynb)

…-qwen3_8b_fine_tune_and_deploy/README.md …-qwen3_8b_fine_tune_and_deploy/README.mdgenerative_ai/sm-qwen3_8b_fine_tune_and_deploy/README.md renamed to generative_ai/sm-qwen3_8b_fine_tune_and_deploy/README.md generative_ai/sm-qwen3_8b_fine_tune_and_deploy/README.md renamed to generative_ai/sm-qwen3_8b_fine_tune_and_deploy/README.md

@@ -626,21 +626,21 @@ The reward function in `reward_function/math.py` computes a multi-metric score f
 
 | Metric | Weight | Description |
 |--------|--------|-------------|
-| `recall` | 35% | Proportion of ground truth tags found |
-| `accuracy` | 35% | Classification accuracy of matched tags |
-| `precision` | 20% | Penalizes extra predicted tags |
-| `match_quality` | 5% | Fuzzy matching quality score |
+| `recall` | 30% | Proportion of ground truth tags found |
+| `accuracy` | 30% | Classification accuracy of matched tags |
+| `precision` | 25% | Penalizes extra predicted tags |
+| `match_quality` | 10% | Fuzzy matching quality score |
 | `formatting` | 5% | Correct output format (9 categories) |
 
 To customize the reward function, edit `2_trainning_grpo/docker/reward_function/math.py`:
 
 ```python
 def compute_score(
     reward_inputs: list[dict[str, Any]],
-    recall_weight: float = 0.35,
-    precision_weight: float = 0.2,
-    accuracy_weight: float = 0.35,
-    match_quality_weight: float = 0.05,
+    recall_weight: float = 0.30,
+    precision_weight: float = 0.25,
+    accuracy_weight: float = 0.30,
+    match_quality_weight: float = 0.10,
     formatting_weight: float = 0.05
 ) -> list[dict[str, float]]:
     """
@@ -683,6 +683,7 @@ GRPO training uses **Parquet format** with prompt-answer pairs. Upload your data
 | `answer` | string | The expected ground truth response (used by reward function) |
 
 **Example row:**
+
 | Column | Content |
 |--------|---------|
 | `problem` | You are a professional and rigorous product tagging expert, responsible for automatically generating and classifying tags based on the provided product information...<br><br>Product Name: MI Amazon Usb Type-C Cable Smartphone Charging (Black) \|Connectivity: Usb 2.0 (Sync And Charging)\| Universal For All Type-C Devices (Grey)... |