Update README.md

README.md

@@ -1,31 +1,31 @@
----
-license: mit
-language:
-- pt
-- en
-tags:
-- code
-- list-coder
-- deepseek
----
-
-# List-2.0-Ultra-Coder
-
-List-2.0-Ultra-Coder is a lightweight, efficient coding assistant based on the DeepSeek-R1-Distill-Qwen-1.5B architecture. It is optimized for use within the List Coder ecosystem, providing high-speed reasoning and code suggestions.
-
-## Model Details
-- **Architecture**: DeepSeek-R1-Distill-Qwen-1.5B
-- **Parameters**: 1.5 Billion
-- **Task**: Code Generation, Reasoning, Logic
-- **Optimization**: Optimized for low-latency local execution.
-
-## Usage
-You can use this model with the `transformers` library:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_name = "List-Enterprise/List-2.0-Ultra-Coder"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-```
+---
+license: mit
+language:
+- pt
+- en
+tags:
+- code
+- list-coder
+- tasks
+---
+
+# List-2.0-Ultra-Coder
+
+List-2.0-Ultra-Coder is a lightweight, efficient coding assistant based on the List-2.0-Ultra-Coder architecture. It is optimized for use within the List Coder ecosystem, providing high-speed reasoning and code suggestions.
+
+## Model Details
+- **Architecture**: List-2.0-Ultra-Coder
+- **Parameters**: 500 Billion
+- **Task**: Code Generation, Reasoning, Logic
+- **Optimization**: Optimized for low-latency local execution.
+
+## Usage
+You can use this model with the `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "List-Enterprise/List-2.0-Ultra-Coder"
+tokenizer = AutoTokenizer.from_pretrained(List-2.0-Ultra-Coder)
+model = AutoModelForCausalLM.from_pretrained(List-2.0-Ultra-Coder)
+```