Update README.md

README.md

@@ -1,9 +1,11 @@
 ---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/model-cards
 tags:
 - autotrain
 - text-generation
 - peft
-- chain-of-though
+- chain-of-thought
 - finetuned
 library_name: transformers
 base_model: tiiuae/Falcon3-3B-Instruct
@@ -12,37 +14,78 @@ widget:
   - role: user
     content: What is your favorite condiment?
 ---
+# Model Card for FalconMind3B
 
-# This is a model trained to think in chain of thought. It was trained to think step ny step about problems.
-you can find the video explaining how this works, and more details below.
+This is a fine-tuned open-source model trained to excel in **chain-of-thought reasoning**. The model is designed to process tasks step by step, providing logical and structured responses for a wide range of applications.
 
-Model Trained Using AutoTrain
-This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
+## Model Details
 
-# Usage
+### Model Description
 
-```python
+FalconMind3B is a fine-tuned variant of the tiiuae/Falcon3-3B-Instruct model. It leverages **chain-of-thought reasoning** techniques to handle complex tasks requiring step-by-step thinking. The fine-tuning process was conducted using PEFT/LoRA on the Hugging Face AutoTrain platform.
+
+- **Developed by:** Faris Allafi
+- **Model type:** Text-generation (causal language modeling)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** tiiuae/Falcon3-3B-Instruct
+
+### Model Sources [optional]
+
+- **Demo [optional]:** [Demo video link](#)
+
+## Uses
+
+### Direct Use
+
+This model is designed for text generation tasks that require logical reasoning, including problem-solving, code explanations, and general Q&A applications.
+
+### Downstream Use [optional]
+
+FalconMind3B can be fine-tuned further for specific tasks in education, programming, or other domains requiring detailed step-by-step reasoning.
 
+### Out-of-Scope Use
+
+This model is not suitable for tasks requiring real-time interaction or applications that rely on languages other than English.
+
+## Bias, Risks, and Limitations
+
+FalconMind3B is fine-tuned using synthetic datasets, which may introduce biases or limitations in generalization. It is recommended to test the model on your specific use cases to ensure reliability.
+
+### Recommendations
+
+Users should be aware of potential biases and limitations when applying the model in high-stakes or sensitive scenarios.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model:
+
+```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_path = "PATH_TO_THIS_REPO"
+model_path = "CoolCreator/FalconMind3b"
 
+# Load tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained(model_path)
 model = AutoModelForCausalLM.from_pretrained(
     model_path,
     device_map="auto",
-    torch_dtype='auto'
+    torch_dtype="auto"
 ).eval()
 
-# Prompt content: "hi"
+# Define chat messages
 messages = [
     {"role": "user", "content": "hi"}
 ]
 
-input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
-output_ids = model.generate(input_ids.to('cuda'))
+# Generate response
+input_ids = tokenizer.apply_chat_template(
+    conversation=messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+)
+output_ids = model.generate(input_ids.to("cuda"))
 response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
 
-# Model response: "Hello! How can I assist you today?"
-print(response)
-```
+print(response)  # Model response: "Hello! How can I assist you today?"