Update README.md

README.md

@@ -12,14 +12,18 @@ language:
 - en
 pipeline_tag: text-generation
 ---
-
-Image here
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/630f3e4002ce39336c411048/3gVVmArsXVoogpkXvsBs7.png)
 
 # MoLA-LM: Mixture of LoRA Adapters LLM
 
 MoLA-LM combines multiple LoRA adapters with an intelligent router to automatically select the best adapter for each input prompt. This approach enables specialized performance across different tasks while maintaining efficiency.
 
-Evals are coming...
+[**Click for evals**](https://github.com/alkinun/MoLA/blob/main/README.md)
+
+**Important Note**: *The v0.5 had issues with the lora applying part of the custom lm class and its router was a bit too small with little generalization.
+In v0.6 and future models, all of these issues are/will be resolved.*
+
+**TLDR:** *Dont use v0.5, use v0.6 and above.*
 
 ## Model Details
 
@@ -32,7 +36,6 @@ Evals are coming...
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-
 # Load the model (trust_remote_code=True is required for custom architecture)
 model = AutoModelForCausalLM.from_pretrained(
     "MoLA-LLM/MoLA-v0.6-9x4b", 
@@ -40,7 +43,6 @@ model = AutoModelForCausalLM.from_pretrained(
     device_map="auto"
 )
 tokenizer = AutoTokenizer.from_pretrained("MoLA-LLM/MoLA-v0.6-9x4b", trust_remote_code=True)
-
 # Use like any other language model - adapter selection is automatic
 prompt = "Write a Python function to calculate fibonacci numbers"
 messages = [{"role": "user", "content": prompt}]
@@ -51,10 +53,8 @@ inputs = tokenizer.apply_chat_template(
     return_dict=True,
     return_tensors="pt",
 ).to(model.device)
-
 outputs = model.generate(**inputs, max_new_tokens=8192, temperature=.6, do_sample=True)
 response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
-
 print(f"Selected LoRA: {model.get_current_lora()}")
 print(response)
 ```
@@ -65,7 +65,7 @@ print(response)
 The MoLA-LM architecture consists of:
 
 1. **Base Model**: Qwen/Qwen3-4B-Thinking-2507
-2. **Router Network**: Frozen encoder as Sentence transformer + decoder as one layer MLP for adapter selection  
+2. **Router Network**: Frozen encoder as Sentence transformer + decoder as MLP for adapter selection  
 3. **LoRA Adapters**: 9 task-specific fine-tuned adapters
 4. **Dynamic Switching**: Automatic adapter application based on input