Update README.md

README.md

@@ -20,14 +20,43 @@ pipeline_tag: text-generation
 
 # Mistral-Instruct-7B-v0.2-ChatAlpaca
 
+## Model description
+
 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the English [robinsmits/ChatAlpaca-20K](https://www.huggingface.co/datasets/robinsmits/ChatAlpaca-20K) dataset.
 
 It achieves the following results on the evaluation set:
 - Loss: 0.8584
 
-## Model description
+## Model usage
 
-More information needed
+A basic example of how to use the finetuned model. Note this example is a modified version from the base model.
+
+```
+import torch
+from peft import AutoPeftModelForCausalLM
+from transformers import AutoTokenizer
+
+device = "cuda"
+
+model = AutoPeftModelForCausalLM.from_pretrained("robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca", 
+                                                 device_map = "auto", 
+                                                 load_in_4bit = True, 
+                                                 torch_dtype = torch.bfloat16)
+
+tokenizer = AutoTokenizer.from_pretrained("robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca")
+
+messages = [
+    {"role": "user", "content": "What is your favourite condiment?"},
+    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
+    {"role": "user", "content": "Do you have mayonnaise recipes?"}
+]
+
+encodeds = tokenizer.apply_chat_template(messages, return_tensors = "pt")
+
+generated_ids = model.generate(input_ids = encodeds.to(device), max_new_tokens = 512, do_sample = True)
+decoded = tokenizer.batch_decode(generated_ids)
+print(decoded[0])
+```
 
 ## Intended uses & limitations