--- license: apache-2.0 language: - en tags: - pluto-genesis - qwen3 - sub-1b - qlora - instruction-tuning - reasoning - mathematics - coding - fine-tuned - conversational - text-generation-inference base_model: Qwen/Qwen3-0.6B pipeline_tag: text-generation library_name: transformers datasets: - teknium/OpenHermes-2.5 - microsoft/orca-math-word-problems-200k - m-a-p/CodeFeedback-Filtered-Instruction model-index: - name: Pluto-Genesis-0.6B results: [] --- # 🪐 Pluto-Genesis-0.6B **An instruction-tuned sub-1B language model fine-tuned on 80K curated samples** [](https://huggingface.co/Siddh07ETH/Pluto-Genesis-0.6B) [](https://opensource.org/licenses/Apache-2.0) []() [](https://huggingface.co/Qwen/Qwen3-0.6B) --- ## Model Description **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. 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](https://huggingface.co/datasets/teknium/OpenHermes-2.5) | 30,000 | Instruction following, reasoning | | 🔢 Mathematics | [Orca-Math-200K](https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k) | 30,000 | Word problems, step-by-step math | | 💻 Code | [CodeFeedback-Filtered](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) | 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 ### 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](https://huggingface.co/Siddh07ETH) --- ## 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](https://opensource.org/licenses/Apache-2.0). Base model [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) is also Apache 2.0.
