Update README.md

README.md

@@ -1,3 +1,56 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+
+
+# Pico Mini V1
+
+Pico v1 is a work in progress model. Based off Qwen 2.5 .5b model, it has been fine tuned for automatic COT and self reflection. 
+
+When making a output, Pico will create three sections, a reasoning section, a self-reflection section and a output section.
+
+Pico Mini v1 struggles with non-question related tasks (Small talk, roleplay, etc).
+
+Pico Mini v1 can struggle with staying on topic at times.
+
+Here is a example of how you can use it:
+
+```from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load the model and tokenizer from the Hugging Face Model Hub (test/test repository)
+output_dir = "test/test"
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+print("Loading the model and tokenizer from the Hugging Face Hub...")
+model = AutoModelForCausalLM.from_pretrained(output_dir).to(device)  # Ensure model is on the same device
+tokenizer = AutoTokenizer.from_pretrained(output_dir)
+
+# Define the testing prompt
+prompt = "What color is the sky?"
+print(f"Testing prompt: {prompt}")
+
+# Tokenize input and move to the same device as the model
+inputs = tokenizer(prompt, return_tensors="pt").to(device)  # Ensure inputs are on the same device
+
+# Generate response
+print("Generating response...")
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1550,  # Adjust the max tokens if needed
+    temperature=0.5,      # Adjust for response randomness
+    top_k=50,             # Adjust for top-k sampling
+    top_p=0.9             # Adjust for nucleus sampling
+)
+
+# Decode and print the response
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Generated response:")
+print(response)
+
+```