Code sample added

README.md

@@ -7,24 +7,12 @@ library_name: transformers
 tags:
 - mergekit
 - merge
-
+license: llama2
+language:
+- en
 ---
-# merge
-
-This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
-
-## Merge Details
-### Merge Method
-
-This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Locutusque/llama-3-neural-chat-v1-8b](https://huggingface.co/Locutusque/llama-3-neural-chat-v1-8b) as a base.
 
-### Models Merged
-
-The following models were included in the merge:
-* [Undi95/Llama-3-Unholy-8B](https://huggingface.co/Undi95/Llama-3-Unholy-8B)
-* [ruslanmv/Medical-Llama3-8B-16bit](https://huggingface.co/ruslanmv/Medical-Llama3-8B-16bit)
-
-### Configuration
+### Medichat-Llama3-8B
 
 The following YAML configuration was used to produce this model:
 
@@ -47,3 +35,40 @@ parameters:
 dtype: bfloat16
 
 ```
+
+### Usage:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("sethuiyer/Medichat-Llama3-8B")
+model = AutoModelForCausalLM.from_pretrained("sethuiyer/Medichat-Llama3-8B").to("cuda")
+
+# Function to format and generate response with prompt engineering using a chat template
+def askme(question):
+    sys_message = ''' 
+    You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
+    provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
+    '''
+
+    # Create messages structured for the chat template
+    messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": question}]
+
+    # Applying chat template
+    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True)  # Adjust max_new_tokens for longer responses
+
+    # Extract and return the generated text
+    answer = tokenizer.batch_decode(outputs)[0].strip()
+    return answer
+
+# Example usage
+question = '''
+Symptoms:
+Dizziness, headache and nausea.
+
+What is the differnetial diagnosis?
+'''
+print(askme(question))
+```