readme updated

README.md

@@ -1,3 +1,84 @@
 ---
 license: mit
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- medical
+- clinical
 ---
+# Model Card for Model ID
+
+## Model Details
+
+### Model Description
+
+This model is a fine-tuned version of [TheBloke/samantha-falcon-7B-GPTQ](https://huggingface.co/TheBloke/samantha-falcon-7B-GPTQ) for text generation tasks in the medical domain.
+
+- **Developed by:** Pradhaph
+- **Model type:** Fine-tuned samantha-falcon-7B-GPTQ based model
+- **Language(s) (NLP):** English
+- **License:** MIT
+
+### Model Sources
+
+- **Repository:** [👉Click here👈](https://huggingface.co/pradhaph/medical-falcon-7b)
+- **Demo:** Available soon
+
+## Uses
+
+### Direct Use
+
+This model can be used for text generation tasks in the medical domain, such as generating medical reports, answering medical queries, etc.
+
+### Downstream Use
+
+This model can be fine-tuned for specific medical text generation tasks or integrated into larger healthcare systems.
+
+### Out-of-Scope Use
+
+This model may not perform well on tasks outside the medical domain.
+
+## Bias, Risks, and Limitations
+
+This model will requires more than 7.00GB GPU vram and 12.00GB CPU ram 
+
+## How to Get Started with the Model
+
+```python
+# Install dependencies
+!pip install transformers==4.31.0 sentence_transformers==2.2.2
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 1. Load the model
+loaded_model_path = r"path_to_downloaded_model"
+model = AutoModelForCausalLM.from_pretrained(loaded_model_path)
+
+# 2. Initialize the tokenizer
+tokenizer = AutoTokenizer.from_pretrained(loaded_model_path)
+
+# 3. Prepare input
+context = "The context you want to provide to the model."
+question = "The question you want to ask the model."
+input_text = f"{context}\nQuestion: {question}\n"
+
+# 4. Tokenize input
+inputs = tokenizer(input_text, return_tensors="pt")
+
+# 5. Model inference
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_length=512,  # Adjust max_length as per your need
+        temperature=0.7,  # Adjust temperature for randomness in sampling
+        top_p=0.9,  # Adjust top_p for nucleus sampling
+        num_return_sequences=1  # Number of sequences to generate
+    )
+
+# 6. Decode and print the output
+generated_texts = [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]
+print("Generated Texts:")
+for text in generated_texts:
+    print(text)
+