Update README.md

README.md

@@ -40,29 +40,14 @@ Model 4-bit Mistral-7B-Instruct-v0.2 finetuned with QLoRA on multiple medical da
 
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+The model is finetuned on medical data and is intended for research. However, it should not be used as a substitute for professional medical advice, diagnosis, or treatment.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model's predictions are based on the information available in the finetuned medical dataset. It may not generalize well to all medical conditions or diverse patient populations.
 
-[More Information Needed]
+Sensitivity to variations in input data and potential biases present in the training data may impact the model's performance.
 
 ### Recommendations
 
@@ -74,14 +59,32 @@ Users (both direct and downstream) should be made aware of the risks, biases and
 
 Use the code below to get started with the model.
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("adriata/med_mistral")
+model = AutoModelForCausalLM.from_pretrained("adriata/med_mistral")
+
+prompt_template = """<s>[INST] {prompt} [/INST]"""
+
+prompt = "What is influenza?"
+
+model_inputs = tokenizer.encode(prompt_template.format(prompt=prompt),
+                                return_tensors="pt").to("cuda")
+
+generated_ids = model.generate(model_inputs, max_new_tokens=512, do_sample=True)
+decoded = tokenizer.batch_decode(generated_ids)
+print(decoded[0])
+```
 
 ## Training Details
+~13h - 20k examples x 1 epoch 
+GPU: OVH - 1 × NVIDIA TESLA V100S (32 GiB RAM) 
 
 ### Training Data
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-Training data included 15k examples randomly selected from datasets:
+Training data included 20k examples randomly selected from datasets:
 - pubmed
 - bigbio/czi_drsm
 - bigbio/bc5cdr