Instructions to use anmoldhandhania93/ANMOLGPT-4B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anmoldhandhania93/ANMOLGPT-4B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="anmoldhandhania93/ANMOLGPT-4B-v0.2")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("anmoldhandhania93/ANMOLGPT-4B-v0.2")
model = AutoModelForMultimodalLM.from_pretrained("anmoldhandhania93/ANMOLGPT-4B-v0.2", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anmoldhandhania93/ANMOLGPT-4B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anmoldhandhania93/ANMOLGPT-4B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anmoldhandhania93/ANMOLGPT-4B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anmoldhandhania93/ANMOLGPT-4B-v0.2

SGLang

How to use anmoldhandhania93/ANMOLGPT-4B-v0.2 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 "anmoldhandhania93/ANMOLGPT-4B-v0.2" \
    --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": "anmoldhandhania93/ANMOLGPT-4B-v0.2",
		"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 "anmoldhandhania93/ANMOLGPT-4B-v0.2" \
        --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": "anmoldhandhania93/ANMOLGPT-4B-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use anmoldhandhania93/ANMOLGPT-4B-v0.2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for anmoldhandhania93/ANMOLGPT-4B-v0.2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for anmoldhandhania93/ANMOLGPT-4B-v0.2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for anmoldhandhania93/ANMOLGPT-4B-v0.2 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="anmoldhandhania93/ANMOLGPT-4B-v0.2",
    max_seq_length=2048,
)

Docker Model Runner
How to use anmoldhandhania93/ANMOLGPT-4B-v0.2 with Docker Model Runner:
```
docker model run hf.co/anmoldhandhania93/ANMOLGPT-4B-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ANMOLGPT-4B-v0.2

Second public release of the ANMOLGPT family of open-source Large Language Models.

Overview

ANMOLGPT-4B-v0.2 is an instruction-following language model built by fine-tuning Qwen3.5-4B using QLoRA with Unsloth Studio.

This release builds upon the foundation established by ANMOLGPT-3B-v0.1, introducing a more capable base model while preserving the project's focus on reproducible training, transparent evaluation, and open-source development.

The objective of v0.2 is to improve reasoning, instruction following, and overall language understanding while providing a stronger foundation for future releases.

Base Model

Model: Qwen3.5-4B
Fine-tuning Method: QLoRA
Framework: Unsloth Studio
Export Format: Hugging Face Transformers

Training Details

Dataset

Databricks Dolly 25K

Training Configuration

Parameter	Value
Base Model	Qwen3.5-4B
Fine-tuning	QLoRA
Quantization	4-bit
Framework	Unsloth Studio
Optimizer	AdamW
Precision	4-bit

Benchmark Results

Benchmark Comparison

Benchmark	ANMOLGPT-3B-v0.1	ANMOLGPT-4B-v0.2
HellaSwag (acc_norm)	73.21	73.25
PIQA	77.97	77.97
ARC-Easy	77.95	77.86
Winogrande	70.24	70.24
TruthfulQA MC2	42.52	42.52
MMLU	65.59	65.60

Example Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "anmoldhandhania93/ANMOLGPT-4B-v0.2"

tokenizer = AutoTokenizer.from_pretrained(model_name)

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

prompt = "Explain how reinforcement learning differs from supervised learning."

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Uses

ANMOLGPT-4B-v0.2 is suitable for:

Conversational AI
Learning and experimentation
Prompt engineering
Educational applications
Software development assistance
General-purpose text generation

Limitations

This model is an experimental open-source release.

Limitations include:

May produce inaccurate or fabricated information.
Performance has not yet been fully benchmarked across all standard evaluations.
Not intended for safety-critical or production use without additional validation.
Outputs should be reviewed before use in professional environments.

Roadmap

v0.2

✅ Upgraded to Qwen3.5-4B
✅ Improved training pipeline
🔄 Comprehensive benchmark evaluation

v0.5

Mixed high-quality instruction datasets
Longer training schedule
Improved reasoning and coding capabilities

v1.0

Domain-specific continued pre-training
Advanced instruction tuning
Human evaluation
Retrieval-Augmented Generation (RAG)
Tool calling
Production-ready deployment

Project Goals

The long-term goal of ANMOLGPT is to explore efficient and transparent development of open-source language models while documenting the complete engineering lifecycle:

Training
Fine-tuning
Benchmarking
Deployment
Continuous improvement

Acknowledgements

ANMOLGPT is built using the outstanding work of the open-source AI community.

Special thanks to:

Qwen Team
Unsloth AI
Hugging Face
EleutherAI
Databricks (Dolly Dataset)

Citation

@misc{anmolgpt2026,
  title={ANMOLGPT-4B-v0.2},
  author={Anmol Dhandhania},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/anmoldhandhania93/ANMOLGPT-4B-v0.2}
}