Update README.md

README.md

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 ---
+language:
+- en
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- medical
 license: cc-by-nc-4.0
 ---
+
+# MedFalcon v2 40b LoRA - Final
+
+![img.png](img.png)
+
+## Model Description
+
+This a model release at `1 epoch`. For evaluation use only! Limitations: 
+* Do not use to treat paitients! Treat AI content as if you wrote it!!!
+
+### Architecture
+`nmitchko/medfalcon-v2-40b-lora` is a large language model LoRa specifically fine-tuned for medical domain tasks.
+It is based on [`Falcon-40b`](https://huggingface.co/tiiuae/falcon-40b) at 40 billion parameters.
+
+The primary goal of this model is to improve question-answering and medical dialogue tasks.
+It was trained using [LoRA](https://arxiv.org/abs/2106.09685), specifically [QLora](https://github.com/artidoro/qlora), to reduce memory footprint. 
+
+See Training Parameters for more info  This Lora supports 4-bit and 8-bit modes.
+
+### Requirements
+
+```
+bitsandbytes>=0.39.0
+peft
+transformers
+```
+
+Steps to load this model:
+1. Load base model using transformers
+2. Apply LoRA using peft
+
+```python
+# 
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+from peft import PeftModel
+
+model = "tiiuae/falcon-40b"
+LoRA = "nmitchko/medfalcon-v2-40b-lora"
+
+# If you want 8 or 4 bit set the appropriate flags
+load_8bit = True
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+
+model = AutoModelForCausalLM.from_pretrained(model,
+    load_in_8bit=load_8bit,
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+)
+
+model = PeftModel.from_pretrained(model, LoRA)
+
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+
+sequences = pipeline(
+   "What does the drug ceftrioxone do?\nDoctor:",
+    max_length=200,
+    do_sample=True,
+    top_k=40,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+)
+
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")
+```
+
+## Training Parameters 
+
+The model was trained for or 1 epoch on a custom, unreleased dataset named `medconcat`. 
+`medconcat` contains only human generated content and weighs in at over 100MiB of raw text.  
+
+
+| Item          | Amount | Units |
+|---------------|--------|-------|
+| LoRA Rank     | 64    | ~     |
+| LoRA Alpha    | 16    | ~     |
+| Learning Rate | 1e-4   | SI    |
+| Dropout       | 5      | %     |