Update README.md

README.md

@@ -1,46 +1,68 @@
 ---
 tags:
 - autotrain
-- text-generation-inference
 - text-generation
+- text-generation-inference
 - peft
+- llama-3
+- finance
+- crypto
+- agents
+- workflow-automation
+- soul-ai
 library_name: transformers
 base_model: meta-llama/Llama-3.1-8B
-widget:
-  - messages:
-      - role: user
-        content: What is your favorite condiment?
 license: other
+widget:
+  - text: "Ask me something about AI agents or crypto."
+  - text: "What kind of automation can LLMs perform?"
 ---
 
-# Model Trained Using AutoTrain
+# 🧠 $SOUL AI — Llama 3.1 Fine-Tuned for Finance & Autonomous Agents
 
-This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
+**$SOUL AI** is a purpose-tuned LLM based on Meta's Llama 3.1–8B, trained on domain-specific data focused on **financial logic**, **LLM agent workflows**, and **automated task generation**. Designed to power on-chain AI agents, it's part of the broader $SOUL ecosystem for monetized intelligence.
 
-# Usage
+---
 
-```python
+## 📂 Dataset Summary
+
+This model was fine-tuned on over 10,000+ instruction-style samples simulating:
 
+- Financial queries and tokenomics reasoning  
+- LLM-agent interaction patterns  
+- Crypto automation logic  
+- DeFi, trading signals, news interpretation  
+- Smart contract and API-triggered tasks  
+- Natural language prompts for dynamic workflow creation
+
+The format follows a custom instruction-based structure optimized for reasoning tasks and agentic workflows—not just casual conversation.
+
+---
+
+## 💻 Usage (via Transformers)
+
+```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_path = "PATH_TO_THIS_REPO"
+model_path = "YOUR_HF_USERNAME/YOUR_MODEL_NAME"
 
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForCausalLM.from_pretrained(
     model_path,
     device_map="auto",
-    torch_dtype='auto'
+    torch_dtype="auto"
 ).eval()
 
-# Prompt content: "hi"
-messages = [
-    {"role": "user", "content": "hi"}
-]
+messages = [{"role": "user", "content": "How do autonomous LLM agents work?"}]
+
+input_ids = tokenizer.apply_chat_template(
+    conversation=messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+)
 
-input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
-output_ids = model.generate(input_ids.to('cuda'))
+output_ids = model.generate(input_ids.to("cuda"), max_new_tokens=256)
 response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
 
-# Model response: "Hello! How can I assist you today?"
 print(response)
-```