---
# 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-thought
- finetuned
library_name: transformers
base_model: tiiuae/Falcon3-3B-Instruct
widget:
- messages:
  - role: user
    content: What is your favorite condiment?
---
# Model Card for FalconMind3B

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 Details

### Model Description

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 = "CoolCreator/FalconMind3b"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype="auto"
).eval()

# Define chat messages
messages = [
    {"role": "user", "content": "hi"}
]

# 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)

print(response)  # Model response: "Hello! How can I assist you today?"