Update README.md

README.md

@@ -23,25 +23,15 @@ To run this model for yourself:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-device = "cuda" # the device to load the model onto
-
 model = AutoModelForCausalLM.from_pretrained("TromeroResearch/SciMistral-V1")
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
-
-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?"}
-]
+tokenizer = AutoTokenizer.from_pretrained("TromeroResearch/SciMistral-V1")
 
-encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
+prompt = "This paper seeks to disprove that 1+1=2"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda") 
 
-model_inputs = encodeds.to(device)
-model.to(device)
+output = model.generate(input_ids, max_length=150, num_return_sequences=1, repetition_penalty=1.2, top_k=50, top_p=0.95, temperature=1.0)
 
-generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
-decoded = tokenizer.batch_decode(generated_ids)
-print(decoded[0])
+print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
 
@@ -78,4 +68,11 @@ And it continues. A much better, more useful and relevant response to someone wh
 
 ## Hardware
 
-4 x Nvidia A6000 GPUs
+4 x Nvidia A6000 GPUs
+
+
+## Limitations
+
+The SciMistral model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. 
+It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
+make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.