Update README.md

README.md

@@ -24,50 +24,58 @@ Helion-V1 is a conversational AI model designed to be helpful, harmless, and hon
 - **Language(s):** English
 - **License:** Apache 2.0
 - **Finetuned from:** Troviku-1.1
+- **Model Size:** 7B parameters
+- **Context Length:** 4096 tokens
 
-## Intended Use
+## Model Capabilities
 
-Helion-V1 is designed for:
-- General conversational assistance
-- Question answering
-- Creative writing support
-- Educational purposes
-- Coding assistance
+-  Natural conversation and dialogue
+-  Knowledge synthesis and explanation
+-  Code generation and debugging
+-  Creative writing and content creation
+-  Problem solving and reasoning
+-  Safe and ethical responses
 
-### Direct Use
+## Installation
 
-The model can be used directly for chat-based applications where safety and helpfulness are priorities.
-
-### Out-of-Scope Use
-
-This model should NOT be used for:
-- Generating harmful, illegal, or unethical content
-- Medical, legal, or financial advice without proper disclaimers
-- Impersonating individuals or organizations
-- Creating misleading or false information
-
-## Safeguards
-
-Helion-V1 includes safety mechanisms to:
-- Refuse harmful requests
-- Avoid generating dangerous content
-- Maintain respectful and helpful interactions
-- Protect user privacy and safety
-
-## Usage
-
-```python
+```bash
+pip install transformers torch accelerate
 from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
 
 model_name = "DeepXR/Helion-V1"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-messages = [
-    {"role": "user", "content": "Hello! Can you help me with a question?"}
-]
-
-input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
-output = model.generate(input_ids, max_length=512)
-response = tokenizer.decode(output[0], skip_special_tokens=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+def chat_with_helion(prompt, max_length=512, temperature=0.7):
+    messages = [
+        {"role": "user", "content": prompt}
+    ]
+    
+    input_ids = tokenizer.apply_chat_template(
+        messages, 
+        return_tensors="pt"
+    ).to(model.device)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids,
+            max_length=max_length,
+            temperature=temperature,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+            top_p=0.9,
+            repetition_penalty=1.1
+        )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Example usage
+prompt = "Explain the concept of machine learning in simple terms."
+response = chat_with_helion(prompt)
 print(response)