Update README.md

README.md

@@ -80,8 +80,63 @@ This model paricipated in multi-turn dialogues and responses empathetically.
 - **Repository:** <https://github.com/justtherightsize/empo>
 - **(*non-anonymized*) Paper preprint:** <https://arxiv.org/abs/2406.19071>
 
-## Usage
-TODO
+## Usage - Generate a response in a dialogue
+```python
+from peft import PeftModel
+from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch
+from huggingface_hub import login
+
+# HF login: you have to be logged in and agree to the license of the base
+# model: https://huggingface.co/alignment-handbook/zephyr-7b-sft-full
+hf_key = "Your key here"
+login(hf_key)
+
+# Load tokenizer either from remote
+adapter_id = "justtherightsize/zephyr-7b-sft-full124"
+base_model_id = "alignment-handbook/zephyr-7b-sft-full"
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+
+# Prepare dialog and convert to chat template
+sys_msg = "You are a friendly assistant, who provides empathetic responses to the user. " \
+            "The input contains previous turn of the dialog, where each utterance is prefaced " \
+            "with tags <|user|>, or <|assistant|>. Be empathetic and precise. " \
+            "Make sure to give responses that make dialogue flow. Avoid repeating the prompt. " \
+            "Please respond creatively and expressively to make the responses longer. You can offer advice."
+
+dialog = ["Yeah about 10 years ago I had a horrifying experience. It was 100% their fault but they hit the water barrels and survived. They had no injuries but they almost ran me off the road.", 
+        "Did you suffer any injuries?", 
+        "No I wasn't hit. It turned out they were drunk. I felt guilty but realized it was his fault."]
+
+dwroles = [{"role": "system", "content": sys_msg}]
+for j in range(len(dialog)):
+    dwroles.append(
+        {"role": "user", "content": dialog[j]} if j % 2 == 0 else
+        {"role": "assistant", "content": dialog[j]})
+template = tokenizer.apply_chat_template(dwroles, tokenize=False, add_generation_prompt=True)
+
+# Load the big model first & resize embeds, load PEFT model
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    quantization_config=quantization_config,
+    trust_remote_code=True
+)
+model.resize_token_embeddings(len(tokenizer))
+model.config.use_cache = False
+model = PeftModel.from_pretrained(model, adapter_id)
+
+# Instantiate generation pipeline
+pipe_gen = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Generate the response
+out = pipe_gen(template, return_full_text=False, max_new_tokens=500)[0]['generated_text']
+print(out)
+```
 
 
 ## Out-of-Scope Usage