Update README.md

README.md

@@ -1,35 +1,37 @@
----
-license: apache-2.0
-base_model: Qwen/Qwen2.5-Coder-14B-Instruct
-tags:
-- rust
-- code-generation
-- lora
-language:
-- en
-- zh
----
-
-# Qwen2.5-Coder-14B-Instruct Rust LoRA
-
-LoRA fine-tuned version of Qwen2.5-Coder-14B-Instruct specifically optimized for Rust code generation. This model significantly improves Rust syntax understanding and generates 100% Rust code compared to the base model which sometimes generates Python/C++ code. Trained with Q-LoRA (4-bit quantization) on RTX 3090, achieving final loss of 0.5738.
-
-## Usage
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-from peft import PeftModel
-import torch
-
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.bfloat16
-)
-
-base_model = "Qwen/Qwen2.5-Coder-14B-Instruct"
-model = AutoModelForCausalLM.from_pretrained(base_model, quantization_config=bnb_config, device_map="auto")
-model = PeftModel.from_pretrained(model, "huaiwuai/Qwen2.5-Coder-14B-Instruct-Rust-LoRA")
-tokenizer = AutoTokenizer.from_pretrained(base_model)
-```
+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-14B-Instruct
+tags:
+- rust
+- code-generation
+- lora
+language:
+- en
+- zh
+datasets:
+- Neloy262/rust_instruction_dataset
+---
+
+# Qwen2.5-Coder-14B-Instruct Rust LoRA
+
+LoRA fine-tuned version of Qwen2.5-Coder-14B-Instruct specifically optimized for Rust code generation. This model significantly improves Rust syntax understanding and generates 100% Rust code compared to the base model which sometimes generates Python/C++ code. Trained with Q-LoRA (4-bit quantization) on RTX 3090, achieving final loss of 0.5738.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+base_model = "Qwen/Qwen2.5-Coder-14B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(base_model, quantization_config=bnb_config, device_map="auto")
+model = PeftModel.from_pretrained(model, "huaiwuai/Qwen2.5-Coder-14B-Instruct-Rust-LoRA")
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+```