Instructions to use FalconX80/NisoriV2llama7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FalconX80/NisoriV2llama7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FalconX80/NisoriV2llama7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FalconX80/NisoriV2llama7b")
model = AutoModelForCausalLM.from_pretrained("FalconX80/NisoriV2llama7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use FalconX80/NisoriV2llama7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FalconX80/NisoriV2llama7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FalconX80/NisoriV2llama7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FalconX80/NisoriV2llama7b

SGLang

How to use FalconX80/NisoriV2llama7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FalconX80/NisoriV2llama7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FalconX80/NisoriV2llama7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FalconX80/NisoriV2llama7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FalconX80/NisoriV2llama7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FalconX80/NisoriV2llama7b with Docker Model Runner:
```
docker model run hf.co/FalconX80/NisoriV2llama7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NisoriV2 LLaMA 7B

A fine-tuned version of Meta’s LLaMA 7B, created by Priyanshu Bhatt.
This model (NisoriV2) has been fine-tuned on Dev Bhoomi Uttarakhand University (DBUU) data for instruction-following and question answering in a conversational style.

🧠 Model Details

Base Model: LLaMA 7B
Fine-Tuned Model: NisoriV2 LLaMA 7B
Fine-Tuned By: Priyanshu Bhatt (FalconX80)
Dataset: Dev Bhoomi Uttarakhand University (DBUU) educational & institutional data
Architecture: Decoder-only Transformer
Libraries: Hugging Face Transformers

🎯 Intended Use

This model is designed for:

Question Answering related to Dev Bhoomi Uttarakhand University
Educational and Institutional Information Retrieval
Conversational AI for DBUU context

📊 Example

Input Prompt

who is the chairman of dev bhoomi uttarakhand university?

Model Output

The Chairman of Dev Bhoomi Uttarakhand University is Shri Sanjay Bansal. 
He is a well-known businessman and philanthropist who has made significant contributions to the field of education. 
Under his leadership, the university has made tremendous progress and has established itself as a premier institution of higher learning in the region.

🚀 How to Use

import re
from transformers import AutoTokenizer, LlamaForCausalLM
import torch

model_name = "FalconX80/NisoriV2llama7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")

def chat(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(inputs.input_ids, max_length=200)
    text = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Cleaning (remove [INST] tokens and repeated prompt)
    if text.lower().startswith(prompt.lower()):
        text = text[len(prompt):].strip()
    text = re.sub(r"\[/?INST\]", "", text).strip()

    return text

print(chat("Tell me about Dev Bhoomi Uttarakhand University"))

🛠️ Training Details

Fine-Tuning Task: Instruction Following & QA
Framework: Hugging Face Transformers + PyTorch
Hardware Used: GPU (Kaggle / Colab / custom environment)
Dataset: Proprietary Dev Bhoomi Uttarakhand University (DBUU) dataset prepared for institutional knowledge and educational Q&A.

⚠️ Limitations & Biases

The model knowledge is limited to DBUU data used during fine-tuning.
May hallucinate facts outside the scope of DBUU.
Not optimized for safety-critical applications.
Responses may vary depending on prompt phrasing.

📬 Contact

Created by Priyanshu Bhatt

📧 Email: priyanshubhatt80@gmail.com

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