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🪐 Pluto-Genesis-0.6B

Name: Pluto-Genesis-0.6B
Availability: InStock

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

DOI

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

python

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)

python

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

📄 Research Paper

The complete research paper describing the Pluto-Genesis training pipeline, benchmark evaluation, checkpoint recovery system, and engineering methodology is available on Zenodo.

Paper

Pluto-Genesis: Reliable QLoRA Fine-Tuning on Ephemeral Compute with Cross-Session Checkpoint Recovery for Sub-1B Language Models

DOI https://doi.org/10.5281/zenodo.21368749

This paper includes:

Training methodology
QLoRA configuration
Cross-session checkpoint recovery
Benchmark evaluation
Deployment workflow
Reproducibility details

Citation

bibtex

@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.

Pluto-AI-Labs/Pluto-Genesis-0.6B