Instructions to use vedantdeore/VedantDev-V0.1-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use vedantdeore/VedantDev-V0.1-3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="vedantdeore/VedantDev-V0.1-3B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vedantdeore/VedantDev-V0.1-3B-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("vedantdeore/VedantDev-V0.1-3B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vedantdeore/VedantDev-V0.1-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vedantdeore/VedantDev-V0.1-3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vedantdeore/VedantDev-V0.1-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/vedantdeore/VedantDev-V0.1-3B-Instruct

SGLang

How to use vedantdeore/VedantDev-V0.1-3B-Instruct 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 "vedantdeore/VedantDev-V0.1-3B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vedantdeore/VedantDev-V0.1-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "vedantdeore/VedantDev-V0.1-3B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vedantdeore/VedantDev-V0.1-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use vedantdeore/VedantDev-V0.1-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/vedantdeore/VedantDev-V0.1-3B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

VedantDev-V0.1-3B-Instruct

Introduction

VedantDev-V0.1 is a fine-tuned large language model built by Vedant Deore. It is based on Qwen2.5-3B-Instruct and fine-tuned using QLoRA (Quantized Low-Rank Adaptation) with custom domain-specific data.

This is the first release in the VedantDev model series -- a line of open-source, domain-adapted language models designed for knowledge-intensive tasks.

Feature	Details
Base Model	Qwen2.5-3B-Instruct
Parameters	3B
Fine-Tuning Method	QLoRA (4-bit, r=16, alpha=32)
Context Length	2048 tokens
License	Apache 2.0
Author	Vedant Deore

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "vedantdeore/VedantDev-V0.1-3B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are VedantDev, an AI assistant created by Vedant Deore."},
    {"role": "user", "content": "Who are you?"},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
response = tokenizer.batch_decode(outputs[:, inputs.input_ids.shape[-1]:], skip_special_tokens=True)[0]
print(response)

Training Details

Method

VedantDev-V0.1 was fine-tuned using Unsloth + QLoRA on a single NVIDIA T4 GPU (Google Colab free tier).

LoRA rank (r): 16
LoRA alpha: 32
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Quantization: 4-bit NormalFloat (NF4)
Epochs: 15
Learning rate: 2e-4
Batch size: 2 (effective 8 with gradient accumulation)

Data

Fine-tuned on a curated instruction-following dataset covering identity, general knowledge, AI/ML concepts, and conversational interactions.

Model Series

Model	Status	Description
VedantDev-V0.1-3B-Instruct	Released	Test release, Qwen2.5-3B base
VedantDev-V1-7B-Instruct	Planned	Production release, 7B base
VedantDev-V1-70B-Instruct	Planned	Full production, 70B base
Kairos-V1-70B-Instruct	Planned	NGO domain-specific model

Use with Ollama

Convert to GGUF and run locally:

ollama create vedantdev -f Modelfile
ollama run vedantdev

License

This model is released under the Apache License 2.0, consistent with the base Qwen2.5 model license.

Citation

@misc{vedantdev2026,
    title  = {VedantDev-V0.1-3B-Instruct: A Fine-Tuned Language Model},
    author = {Vedant Deore},
    year   = {2026},
    url    = {https://huggingface.co/vedantdeore/VedantDev-V0.1-3B-Instruct}
}

Contact

For questions or collaboration, reach out via Hugging Face.

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for vedantdeore/VedantDev-V0.1-3B-Instruct

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

(1429)

this model

Quantizations

1 model