Create README.md

README.md

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+---
+pipeline_tag: text-generation
+---
+
+# Model Card for Model ID
+
+Small testing version of my first model
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+### Model Description
+
+Test version of my first model
+
+
+
+## Uses
+
+Dosen't work well
+
+
+### Out-of-Scope Use
+
+Better not use for anything
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+Don't work
+
+
+## How to Get Started with the Model
+
+'''import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load pre-trained model tokenizer (v3 compatibility)
+tokenizer = AutoTokenizer.from_pretrained("amusktweewt/checkpoint-72")
+
+# Load pre-trained model (PyTorch Lightning module)
+model = AutoModelForCausalLM.from_pretrained("amusktweewt/checkpoint-72")
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+while True:
+    user_input = input("> ")
+    if user_input.lower() == "quit":
+        break
+
+    inputs = tokenizer(user_input, return_tensors="pt", max_length=512, truncation=True).to(device)
+    outputs = model(**inputs)
+
+    logits = outputs.logits
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    top_prob, top_idx = torch.topk(probs, 3)  # Get the top 3 probabilities
+
+    # Flatten the list of token IDs before decoding
+    top_idx = top_idx[0].view(-1).tolist()
+    top_pred = tokenizer.decode(top_idx, skip_special_tokens=True)
+
+    print(f"You: {user_input}")
+    print(f"Model: {top_pred}")
+
+print("Goodbye!")
+'''