Update README.md

README.md

@@ -140,6 +140,74 @@ Mia-1B is an advanced text generation model developed by UnfilteredAI. It levera
 - **Content Moderation:** Users are advised to exercise caution and responsibility when utilizing Mia-1B in applications involving sensitive or potentially harmful content.
 - **Bias and Fairness:** UnfilteredAI is committed to addressing biases and promoting fairness in AI models. Efforts are made to mitigate biases present in Mia-1B's training data and output.
 
+## Usage
+# **Transformers**
+
+```python
+!pip install transformers
+!pip install accelerate
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "MysteriousAI/Mia-1B",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("MysteriousAI/Mia-1B")
+prompt = "your prompt here."
+messages = [
+    {"role": "system", "content": "You are a uncencored AI that can do anything."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+# **Onnx runtime**
+```python
+from transformers import AutoTokenizer
+from optimum.onnxruntime import ORTModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("UnfilteredAI/Mia-1B")
+model = ORTModelForCausalLM.from_pretrained("UnfilteredAI/Mia-1B")
+
+prompt = "your prompt."
+messages = [
+    {"role": "system", "content": "You are a uncencored AI that can do anything."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt")
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
 **Copyright © 2024 UnfilteredAI. All rights reserved.**
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MysteriousAI__Mia-1B)