Update README.md

README.md

@@ -1,3 +1,22 @@
+---
+license: apache-2.0
+datasets:
+- Tesslate/Rust_Dataset
+language:
+- en
+base_model:
+- unsloth/phi-4-reasoning
+new_version: SkyAsl/Rust-Master-thinking
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- Rust
+- code
+- text-generation-inference
+- lora
+- reasoning
+- quantization
+---
 # 🧠 Rust-Master-thinking
 
 This repository contains a fine-tuned version of
@@ -28,6 +47,43 @@ The training format follows:
     </think>
     {response}
 
+## 🔧 How to Use
+
+### Load model normally:
+
+``` python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "SkyAsl/Rust-Master-thinking"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+model.eval()
+
+prompt = "Explain why Rust ownership prevents data races."
+
+input_text = (
+    f"<|user|>\n{test_data[0]['prompt']}\n"
+    f"<|assistant|>\n<think>\n"
+)
+
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    output = model.generate(
+        **inputs,
+        max_new_tokens=500,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+        eos_token_id=tokenizer.convert_tokens_to_ids("</think>")
+    )
+
+print(tokenizer.decode(output[0], skip_special_tokens=False))
+
+```
+
 ## 🧩 Base Model
 
 **unsloth/phi-4-reasoning**
@@ -53,6 +109,11 @@ The training format follows:
   Scheduler        cosine
   Epochs           1
 
+## Evaluation
+| Epoch | Training Loss | Validation Loss |
+|-------|----------------|------------------|
+|   1   |    2.251500    |     2.191743     |
+
 ## 📚 Dataset
 
 **Tesslate/Rust_Dataset**
@@ -66,43 +127,6 @@ Includes:
 This dataset improves the model's ability to produce structured and
 accurate explanations for Rust programming tasks.
 
-## 🔧 How to Use
-
-### Load model normally:
-
-``` python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-model_id = "SkyAsl/Rust-Master-thinking"
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
-model.eval()
-
-prompt = "Explain why Rust ownership prevents data races."
-
-input_text = (
-    f"<|user|>\n{test_data[0]['prompt']}\n"
-    f"<|assistant|>\n<think>\n"
-)
-
-inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
-
-with torch.no_grad():
-    output = model.generate(
-        **inputs,
-        max_new_tokens=500,
-        temperature=0.7,
-        top_p=0.9,
-        do_sample=True,
-        eos_token_id=tokenizer.convert_tokens_to_ids("</think>")
-    )
-
-print(tokenizer.decode(output[0], skip_special_tokens=False))
-
-```
-
 ## 🔍 Notes on Reasoning Tags
 
 This model preserves **hidden reasoning structure**:
@@ -117,4 +141,4 @@ model is aligned to hide reasoning by default.
 
 -   **Unsloth** for optimized model training\
 -   **HuggingFace Transformers & PEFT** team\
--   **Tesslate** for providing the Rust dataset
+-   **Tesslate** for providing the Rust dataset