Update README.md

README.md

@@ -1,6 +1,131 @@
----
-license: apache-2.0
-tags:
-- trl
-- sft
----
+---
+license: apache-2.0
+base_model: qwen/Qwen2.5-1.5B
+tags:
+- math
+- fine-tuned
+- gsm8k
+- qwen
+- qwen2.5
+- logic
+- zh
+- chinese
+language:
+- zh
+- en
+datasets:
+- meta-math/GSM8K_zh
+metrics:
+- accuracy
+library_name: peft
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-1.5B-GSM8K-zh-Adapter
+
+This model is a fine-tuned version of **Qwen2.5-1.5B** specialized for solving Chinese mathematical word problems. It was trained using Low-Rank Adaptation (LoRA) on the `meta-math/GSM8K_zh` dataset.
+
+## Model Description
+- **Developed by:** [Your Name/Organization]
+- **Model Type:** Causal Language Model (Fine-tuned)
+- **Base Model:** `Qwen/Qwen2.5-1.5B`
+- **Language(s):** Chinese (Primary), English
+- **License:** Apache 2.0
+- **Fine-tuning Method:** LoRA (PEFT)
+
+## Training Details
+The model was optimized for reasoning and step-by-step mathematical solutions in a Chinese context.
+
+- **Training Hardware:** Kaggle (2x T4 GPUs)
+- **Quantization:** 4-bit (bitsandbytes)
+- **Epochs/Steps:** 500 Steps
+- **Batch Size:** 64 (effective)
+- **Learning Rate:** 2e-4
+- **Optimizer:** Paged AdamW 8-bit
+
+## How to Use
+
+To use this adapter, you must load the base Qwen2.5-1.5B model first and then attach the adapter.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+model_id = "Qwen/Qwen2.5-1.5B"
+adapter_id = "your-username/qwen2.5-gsm8k-zh-lora" # Replace with your repo
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+model = PeftModel.from_pretrained(model, adapter_id)
+
+prompt = "<|im_start|>user\n小明有5个苹果，小红给了他3个，他又吃了2个，现在他还有几个？<|im_end|>\n<|im_start|>assistant\n"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+output = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(output[0], skip_special_tokens=True))---
+license: apache-2.0
+base_model: qwen/Qwen2.5-1.5B
+tags:
+- math
+- fine-tuned
+- gsm8k
+- qwen
+- qwen2.5
+- logic
+- zh
+- chinese
+language:
+- zh
+- en
+datasets:
+- meta-math/GSM8K_zh
+metrics:
+- accuracy
+library_name: peft
+pipeline_tag: text-generation
+---
+
+# Qwen2.5-1.5B-GSM8K-zh-Adapter
+
+This model is a fine-tuned version of **Qwen2.5-1.5B** specialized for solving Chinese mathematical word problems. It was trained using Low-Rank Adaptation (LoRA) on the `meta-math/GSM8K_zh` dataset.
+
+## Model Description
+- **Developed by:** [Your Name/Organization]
+- **Model Type:** Causal Language Model (Fine-tuned)
+- **Base Model:** `Qwen/Qwen2.5-1.5B`
+- **Language(s):** Chinese (Primary), English
+- **License:** Apache 2.0
+- **Fine-tuning Method:** LoRA (PEFT)
+
+## Training Details
+The model was optimized for reasoning and step-by-step mathematical solutions in a Chinese context.
+
+- **Training Hardware:** Kaggle (2x T4 GPUs)
+- **Quantization:** 4-bit (bitsandbytes)
+- **Epochs/Steps:** 500 Steps
+- **Batch Size:** 64 (effective)
+- **Learning Rate:** 2e-4
+- **Optimizer:** Paged AdamW 8-bit
+
+## How to Use
+
+To use this adapter, you must load the base Qwen2.5-1.5B model first and then attach the adapter.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+model_id = "Qwen/Qwen2.5-1.5B"
+adapter_id = "your-username/qwen2.5-gsm8k-zh-lora" # Replace with your repo
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+model = PeftModel.from_pretrained(model, adapter_id)
+
+prompt = "<|im_start|>user\n小明有5个苹果，小红给了他3个，他又吃了2个，现在他还有几个？<|im_end|>\n<|im_start|>assistant\n"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+output = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(output[0], skip_special_tokens=True))