Update README.md

README.md

@@ -19,36 +19,20 @@ language:
 
 This repo includes two types of quantized models: **GGUF** and **AWQ**, for ourOctopus V2 model at [NexaAIDev/Octopus-v2](https://huggingface.co/NexaAIDev/Octopus-v2)
 
+<p align="center" width="100%">
+  <a><img src="Octopus-logo.jpeg" alt="nexa-octopus" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
 
-# GGUF Qauntization
 
-## Run with [Ollama](https://github.com/ollama/ollama)
+# GGUF Qauntization
+Run with [Ollama](https://github.com/ollama/ollama)
 
 ```bash
 ollama run NexaAIDev/octopus-v2-Q4_K_M
 ```
 
-Input example:
-
-```json
-def get_trending_news(category=None, region='US', language='en', max_results=5):
-    """
-    Fetches trending news articles based on category, region, and language.
-
-    Parameters:
-    - category (str, optional): News category to filter by, by default use None for all categories. Optional to provide.
-    - region (str, optional): ISO 3166-1 alpha-2 country code for region-specific news, by default, uses 'US'. Optional to provide.
-    - language (str, optional): ISO 639-1 language code for article language, by default uses 'en'. Optional to provide.
-    - max_results (int, optional): Maximum number of articles to return, by default, uses 5. Optional to provide.
-
-    Returns:
-    - list[str]: A list of strings, each representing an article. Each string contains the article's heading and URL.
-    """
-```
-
 # AWQ Quantization
-
-Input Python example:
+Python example:
 
 ```python
 from awq import AutoAWQForCausalLM
@@ -102,7 +86,7 @@ for prompt in prompts:
 print("avg throughput:", np.mean(avg_throughput))
 ```
 
-## Quantized GGUF & AWQ Models
+# Quantized GGUF & AWQ Models Benchmark
 
 | Name                   | Quant method | Bits | Size     | Response (t/s) | Use Cases                           |
 | ---------------------- | ------------ | ---- | -------- | -------------- | ----------------------------------- |