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Model: Pluto-AI-Labs/Pluto-Genesis-0.6B Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- pluto-genesis
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- qwen3
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- sub-1b
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- qlora
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- instruction-tuning
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- reasoning
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- mathematics
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- coding
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- fine-tuned
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- conversational
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- text-generation-inference
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base_model: Qwen/Qwen3-0.6B
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- teknium/OpenHermes-2.5
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- microsoft/orca-math-word-problems-200k
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- m-a-p/CodeFeedback-Filtered-Instruction
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model-index:
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- name: Pluto-Genesis-0.6B
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results: []
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---
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<div align="center">
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# 🪐 Pluto-Genesis-0.6B
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<p align="center">
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<img src="./banner.png" width="100%">
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</p>
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**An instruction-tuned sub-1B language model fine-tuned on 80K curated samples**
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[](https://huggingface.co/Siddh07ETH/Pluto-Genesis-0.6B)
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[](https://opensource.org/licenses/Apache-2.0)
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[]()
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[](https://huggingface.co/Qwen/Qwen3-0.6B)
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[](https://doi.org/10.5281/zenodo.21368749)
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</div>
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---
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## Model Description
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**Pluto-Genesis-0.6B** is a fine-tuned instruction-following language model built on top of [Qwen3-0.6B](https://huggingface.co/Qwen/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.
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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.
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> **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.
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---
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## Training Details
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| Property | Value |
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|----------|-------|
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| **Base Model** | Qwen/Qwen3-0.6B |
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| **Parameters** | 596 Million (596,049,920) |
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| **Method** | QLoRA (4-bit NF4 + LoRA) |
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| **LoRA Rank** | r=64, α=128 |
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| **LoRA Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| **Training Steps** | 2,475 |
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| **Final Training Loss** | 0.2741 |
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| **Precision** | FP16 |
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| **Optimizer** | Paged AdamW 8-bit |
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| **Learning Rate** | 2e-4 (cosine schedule) |
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| **Effective Batch Size** | 32 (2 × 16 grad accum) |
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| **Sequence Length** | 1024 tokens |
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| **Hardware** | Tesla T4 (16 GB) |
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| **Framework** | Transformers + PEFT + TRL |
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---
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## Training Data
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| Domain | Dataset | Samples | Skills Targeted |
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|--------|---------|---------|----------------|
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| 🧠 General Reasoning | [OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) | 30,000 | Instruction following, reasoning |
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| 🔢 Mathematics | [Orca-Math-200K](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k) | 30,000 | Word problems, step-by-step math |
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| 💻 Code | [CodeFeedback-Filtered](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) | 20,000 | Code generation, debugging |
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| **Total** | | **80,000** | |
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---
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## Benchmarks
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> 📊 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.
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<p align="center">
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<img src="./detailed_benchmark.png" width="100%">
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</p>
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<p align="center">
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<img src="./benchmark-comparison.png" width="100%">
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</p>
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---
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## Usage
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## GGUF Quantizations
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GGUF versions for llama.cpp, Ollama and LM Studio are available here:
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> https://huggingface.co/Siddh07ETH/Pluto-Genesis-0.6B-GGUF
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### Basic Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model = AutoModelForCausalLM.from_pretrained(
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"Siddh07ETH/Pluto-Genesis-0.6B",
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torch_dtype=torch.float16,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("Siddh07ETH/Pluto-Genesis-0.6B")
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messages = [{"role": "user", "content": "Explain what a neural network is."}]
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text = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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output = model.generate(
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**inputs,
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max_new_tokens=256,
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temperature=0.3,
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do_sample=True,
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top_p=0.9,
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repetition_penalty=1.1,
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)
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response = tokenizer.decode(
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output[0][inputs.input_ids.shape[1]:],
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skip_special_tokens=True
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)
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print(response)
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```
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### Low Memory Inference (4-bit)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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quant_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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)
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model = AutoModelForCausalLM.from_pretrained(
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"Siddh07ETH/Pluto-Genesis-0.6B",
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quantization_config=quant_config,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained("Siddh07ETH/Pluto-Genesis-0.6B")
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```
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---
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## Recommended Generation Settings
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| Setting | Value | Reason |
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|---------|-------|--------|
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| `temperature` | 0.3 | Conservative — reduces hallucinations |
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| `top_p` | 0.9 | Focused vocabulary |
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| `repetition_penalty` | 1.1 | Prevents rambling |
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| `max_new_tokens` | 200–512 | Keeps answers concise |
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| `do_sample` | True | Required when temperature < 1.0 |
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---
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## Limitations
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- **Model size:** At 596M parameters this model will hallucinate on topics outside its training distribution. Always verify factual claims.
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- **Context length:** Trained on sequences up to 1024 tokens. Performance may degrade on longer contexts.
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- **Knowledge cutoff:** The model does not have access to real-time information.
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- **Research only:** Not intended for production deployment without further evaluation and safety testing.
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---
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## Author
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**Siddharth N.R.**
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[HuggingFace](https://huggingface.co/Siddh07ETH)
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---
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## 📄 Research Paper
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The complete research paper describing the Pluto-Genesis training pipeline, benchmark evaluation, checkpoint recovery system, and engineering methodology is available on Zenodo.
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**Paper**
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> Pluto-Genesis: Reliable QLoRA Fine-Tuning on Ephemeral Compute with Cross-Session Checkpoint Recovery for Sub-1B Language Models
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**DOI**
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https://doi.org/10.5281/zenodo.21368749
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This paper includes:
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- Training methodology
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- QLoRA configuration
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- Cross-session checkpoint recovery
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- Benchmark evaluation
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- Deployment workflow
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- Reproducibility details
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## Citation
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```bibtex
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@misc{plutogenesis2026,
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author = {Siddharth N.R.},
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title = {Pluto-Genesis-0.6B: An Instruction-Tuned Sub-1B Language Model},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/Siddh07ETH/Pluto-Genesis-0.6B}
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}
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```
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---
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## License
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Apache 2.0 — see [LICENSE](https://opensource.org/licenses/Apache-2.0).
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Base model [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) is also Apache 2.0.
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