Instructions to use Siddh07ETH/Pluto-Genesis-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Siddh07ETH/Pluto-Genesis-0.6B with Transformers:

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

pipe = pipeline("text-generation", model="Siddh07ETH/Pluto-Genesis-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Siddh07ETH/Pluto-Genesis-0.6B")
model = AutoModelForCausalLM.from_pretrained("Siddh07ETH/Pluto-Genesis-0.6B")
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 Siddh07ETH/Pluto-Genesis-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Siddh07ETH/Pluto-Genesis-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Siddh07ETH/Pluto-Genesis-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Siddh07ETH/Pluto-Genesis-0.6B

SGLang

How to use Siddh07ETH/Pluto-Genesis-0.6B 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 "Siddh07ETH/Pluto-Genesis-0.6B" \
    --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": "Siddh07ETH/Pluto-Genesis-0.6B",
		"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 "Siddh07ETH/Pluto-Genesis-0.6B" \
        --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": "Siddh07ETH/Pluto-Genesis-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Siddh07ETH/Pluto-Genesis-0.6B with Docker Model Runner:
```
docker model run hf.co/Siddh07ETH/Pluto-Genesis-0.6B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🪐 Pluto-Genesis-0.6B

An instruction-tuned sub-1B language model fine-tuned on 80K curated samples

Model Description

Pluto-Genesis-0.6B is a fine-tuned instruction-following language model built on top of Qwen3-0.6B. It was trained using QLoRA (Quantized Low-Rank Adaptation) on a curated mixture of 80,000 high-quality instruction-response pairs spanning general reasoning, mathematical problem solving, and code generation.

This model is part of the Pluto AI research project by Siddharth N.R., exploring efficient fine-tuning of sub-1B language models on consumer-grade hardware.

Research Goal: Demonstrate that a sub-1B model fine-tuned on carefully curated data can achieve competitive performance on reasoning, math, and coding benchmarks while remaining deployable on consumer hardware.

Training Details

Property	Value
Base Model	Qwen/Qwen3-0.6B
Parameters	596 Million (596,049,920)
Method	QLoRA (4-bit NF4 + LoRA)
LoRA Rank	r=64, α=128
LoRA Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Training Steps	2,475
Final Training Loss	0.2741
Precision	FP16
Optimizer	Paged AdamW 8-bit
Learning Rate	2e-4 (cosine schedule)
Effective Batch Size	32 (2 × 16 grad accum)
Sequence Length	1024 tokens
Hardware	Tesla T4 (16 GB)
Framework	Transformers + PEFT + TRL

Training Data

Domain	Dataset	Samples	Skills Targeted
🧠 General Reasoning	OpenHermes-2.5	30,000	Instruction following, reasoning
🔢 Mathematics	Orca-Math-200K	30,000	Word problems, step-by-step math
💻 Code	CodeFeedback-Filtered	20,000	Code generation, debugging
Total		80,000

Benchmarks

📊 Benchmarks will be added shortly. The model is currently being evaluated on ARC-Challenge, HellaSwag, MMLU, GSM8K, and TruthfulQA using lm-evaluation-harness v0.4.4.

Usage

GGUF Quantizations

GGUF versions for llama.cpp, Ollama and LM Studio are available here:

https://huggingface.co/Siddh07ETH/Pluto-Genesis-0.6B-GGUF

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Siddh07ETH/Pluto-Genesis-0.6B",
    torch_dtype=torch.float16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Siddh07ETH/Pluto-Genesis-0.6B")

messages = [{"role": "user", "content": "Explain what a neural network is."}]

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.3,
        do_sample=True,
        top_p=0.9,
        repetition_penalty=1.1,
    )

response = tokenizer.decode(
    output[0][inputs.input_ids.shape[1]:],
    skip_special_tokens=True
)
print(response)

Low Memory Inference (4-bit)

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
    "Siddh07ETH/Pluto-Genesis-0.6B",
    quantization_config=quant_config,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Siddh07ETH/Pluto-Genesis-0.6B")

Recommended Generation Settings

Setting	Value	Reason
`temperature`	0.3	Conservative — reduces hallucinations
`top_p`	0.9	Focused vocabulary
`repetition_penalty`	1.1	Prevents rambling
`max_new_tokens`	200–512	Keeps answers concise
`do_sample`	True	Required when temperature < 1.0

Limitations

Model size: At 596M parameters this model will hallucinate on topics outside its training distribution. Always verify factual claims.
Context length: Trained on sequences up to 1024 tokens. Performance may degrade on longer contexts.
Knowledge cutoff: The model does not have access to real-time information.
Research only: Not intended for production deployment without further evaluation and safety testing.

Author

Siddharth N.R. HuggingFace

Citation

@misc{plutogenesis2026,
  author    = {Siddharth N.R.},
  title     = {Pluto-Genesis-0.6B: An Instruction-Tuned Sub-1B Language Model},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/Siddh07ETH/Pluto-Genesis-0.6B}
}

License

Apache 2.0 — see LICENSE. Base model Qwen3-0.6B is also Apache 2.0.

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Safetensors

Model size

0.6B params

Tensor type

F16

Model tree for Siddh07ETH/Pluto-Genesis-0.6B

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B