Add pipeline tag and sample usage

README.md

@@ -1,22 +1,19 @@
 ---
-library_name: transformers
-license: mit
 base_model:
 - Qwen/Qwen3-8B
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
 ---
 
 # Model Card for SubconsciousDev/TIM-8b-preview
 
-<!-- Provide a quick summary of what the model is/does. -->
-
 TIM is a model that reasons on recursive task trees formatted as JSON structures.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
 - **Developed by:** MIT and Subconscious
 - **Model type:** Structural reasoning model
 - **License:** MIT License
@@ -24,9 +21,34 @@ TIM is a model that reasons on recursive task trees formatted as JSON structures
 
 ### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [TIMRUN](https://github.com/subconscious-systems/TIMRUN)
 - **Paper:** [Beyond Context Limits: Subconscious Threads for Long-Horizon Reasoning](https://arxiv.org/pdf/2507.16784)
 - **Demo:** [Subconscious API platform](https://www.subconscious.dev/)
 
+## Sample Usage
+
+You can use this model with the `transformers` library, leveraging `trust_remote_code=True`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer
+model_name = "SubconsciousDev/TIM-8b-preview"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16, # Use torch.float16 for GPUs that don't support bfloat16
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Example: Simple text generation
+prompt_text = "What is the capital of France?"
+input_ids = tokenizer(prompt_text, return_tensors="pt").input_ids.to(model.device)
+
+output_ids = model.generate(input_ids, max_new_tokens=50, do_sample=True, temperature=0.7)
+response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+print(response)
+```