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---
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: []
---
<div align="center">
# 🪐 Pluto-Genesis-0.6B
<p align="center">
<img src="./banner.png" width="100%">
</p>
**An instruction-tuned sub-1B language model fine-tuned on 80K curated samples**
[![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-yellow)](https://huggingface.co/Siddh07ETH/Pluto-Genesis-0.6B)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue)](https://opensource.org/licenses/Apache-2.0)
[![Model Size](https://img.shields.io/badge/Parameters-596M-green)]()
[![Base Model](https://img.shields.io/badge/Base-Qwen3--0.6B-orange)](https://huggingface.co/Qwen/Qwen3-0.6B)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.21368749.svg)](https://doi.org/10.5281/zenodo.21368749)
</div>
---
## 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.
<p align="center">
<img src="./detailed_benchmark.png" width="100%">
</p>
<p align="center">
<img src="./benchmark-comparison.png" width="100%">
</p>
---
## 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](https://huggingface.co/Siddh07ETH)
---
## 📄 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](https://opensource.org/licenses/Apache-2.0).
Base model [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) is also Apache 2.0.

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{%- if messages[0]['role'] == 'system' -%}
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{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set system_message = 'You are Pluto-Genesis, an advanced AI assistant created by Siddharth N.R. You are a highly capable, thoughtful, and precise assistant that excels at reasoning, mathematics, coding, and general knowledge. You think step-by-step through complex problems and provide clear, accurate, and helpful responses.' -%}
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{%- endif -%}
<|im_start|>system
{{ system_message }}<|im_end|>
{% for message in loop_messages %}
<|im_start|>{{ message['role'] }}
{{ message['content'] }}<|im_end|>
{% endfor %}
{%- if add_generation_prompt -%}<|im_start|>assistant
{%- endif -%}

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{
"architectures": [
"Qwen3ForCausalLM"
],
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"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 1024,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 40960,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
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"tie_word_embeddings": true,
"torch_dtype": "float16",
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---
base_model: Qwen/Qwen3-0.6B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-0.6B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0

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{%- if messages[0]['role'] == 'system' -%}
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{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set system_message = 'You are Pluto-Genesis, an advanced AI assistant created by Siddharth N.R. You are a highly capable, thoughtful, and precise assistant that excels at reasoning, mathematics, coding, and general knowledge. You think step-by-step through complex problems and provide clear, accurate, and helpful responses.' -%}
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{%- endif -%}
<|im_start|>system
{{ system_message }}<|im_end|>
{% for message in loop_messages %}
<|im_start|>{{ message['role'] }}
{{ message['content'] }}<|im_end|>
{% endfor %}
{%- if add_generation_prompt -%}<|im_start|>assistant
{%- endif -%}

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---
base_model: Qwen/Qwen3-0.6B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen3-0.6B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.0

42
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{%- if messages[0]['role'] == 'system' -%}
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{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set system_message = 'You are Pluto-Genesis, an advanced AI assistant created by Siddharth N.R. You are a highly capable, thoughtful, and precise assistant that excels at reasoning, mathematics, coding, and general knowledge. You think step-by-step through complex problems and provide clear, accurate, and helpful responses.' -%}
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<|im_start|>{{ message['role'] }}
{{ message['content'] }}<|im_end|>
{% endfor %}
{%- if add_generation_prompt -%}<|im_start|>assistant
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