updated readme

README.md

@@ -18,25 +18,80 @@ tags:
 - biology
 - mlx
 datasets:
-- pubmed
+- health_fact
 base_model:
 - BioMistral/BioMistral-7B
 - mistralai/Mistral-7B-Instruct-v0.1
 pipeline_tag: text-generation
 ---
-
 # abhishek-ch/biomistral-7b-synthetic-ehr
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6460910f455531c6be78b2dd/tGtYB0b3eS7A4zbqp1xz0.png)
+
+
 This model was converted to MLX format from [`BioMistral/BioMistral-7B-DARE`]().
 Refer to the [original model card](https://huggingface.co/BioMistral/BioMistral-7B-DARE) for more details on the model.
+
+
 ## Use with mlx
 
 ```bash
 pip install mlx-lm
 ```
 
+The model was LoRA fine-tuned on [health_facts](https://huggingface.co/datasets/health_fact) and 
+Synthetic EHR dataset inspired by MIMIC-IV using the format below, for 1000 steps (~1M tokens) using mlx.
+
 ```python
-from mlx_lm import load, generate
+def format_prompt(prompt:str, question: str) -> str:
+    return """<s>[INST]
+## Instructions
+{}
+## User Question
+{}.
+[/INST]</s> 
+""".format(prompt, question)
+```
+
+Example For Synthetic EHR Diagnosis System Prompt
+```
+You are an expert in provide diagnosis summary based on clinical notes inspired by MIMIC-IV-Note dataset.
+These notes encompass Chief Complaint along with Patient Summary & medical admission details.
+```
+
+Example for Healthfacts Check System Prompt
+```
+You are a Public Health AI Assistant. You can do the fact-checking of public health claims. \nEach answer labelled with true, false, unproven or mixture. \nPlease provide the reason behind the answer
+```
+
+## Loading the model using `mlx`
 
+```python
+from mlx_lm import generate, load
 model, tokenizer = load("abhishek-ch/biomistral-7b-synthetic-ehr")
-response = generate(model, tokenizer, prompt="hello", verbose=True)
+response = generate(
+    fused_model,
+    fused_tokenizer,
+    prompt=format_prompt(prompt, question),
+    verbose=True,  # Set to True to see the prompt and response
+    temp=0.0,
+    max_tokens=512,
+)
+```
+
+## Loading the model using `transformers`
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+repo_id = "abhishek-ch/biomistral-7b-synthetic-ehr"
+tokenizer = AutoTokenizer.from_pretrained(repo_id)
+model = AutoModelForCausalLM.from_pretrained(repo_id)
+model.to("mps")
+input_text = format_prompt(system_prompt, question)
+input_ids = tokenizer(input_text, return_tensors="pt").to("mps")
+outputs = model.generate(
+    **input_ids,
+    max_new_tokens=512,
+)
+print(tokenizer.decode(outputs[0]))
 ```