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pranav2711/ncu-smartllm-fp32
ncu-smartllm-fp32 is a machine learning model from pranav2711. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
[](https://huggingface.co/pranav2711/ncu-smartllm-fp32) [](LICENSE)
Downloads · 30 days
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From the Hugging Face model README
A full-precision, fully fine-tuned version of Mistral-7B on domain-specific data from The NorthCap University (NCU) — fine-tuned to answer academic, administrative, and general university queries in an instructional format.
The model has been trained to:
Fully plug-and-play with Hugging Face Transformers & inference APIs
pip install transformers accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("pranav2711/ncu-smartllm-fp32", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("pranav2711/ncu-smartllm-fp32")
prompt = "### Question:\nHow do I apply for hostel at NCU?\n\n### Answer:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
| Detail | Value |
|---|---|
| Base model | mistralai/Mistral-7B-v0.1 |
| Fine-tuned epochs | 10 |
| Batch size | 2 |
| Tokenizer max len | 512 |
| Output format | Instruction → Response |
| Loss (final epoch) | ~1.405300 |
Each entry follows:
{
"instruction": "How do I apply for a degree certificate?",
"input": "I graduated in 2023.",
"output": "You can apply for the degree certificate through the Registrar's Office. Submit your documents along with..."
}
Formatted as:
### Question:
[instruction + optional input]
### Answer:
[output]
This model is released under the Apache 2.0 license. You are free to use, modify, and distribute it with attribution.