153 lines
7.1 KiB
Markdown
153 lines
7.1 KiB
Markdown
---
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library_name: transformers
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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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- qwen2.5
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- 0.5B
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- conversational
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- fast
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- lightweight
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- quantsaparklabs
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pipeline_tag: text-generation
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base_model: QuantaSparkLabs/Quantum-X
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---
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# GGUF Files for Quantum-X
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These are the GGUF files for [QuantaSparkLabs/Quantum-X](https://huggingface.co/QuantaSparkLabs/Quantum-X).
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> [!NOTE]
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> **Note:** This is the **second iteration/revision** of this model. A revision is made when a model repo gets updated with a new model.
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> This is the latest version of the model.
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>
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> [[first iteration (1)](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF-i1)]
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## Downloads
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| GGUF Link | Quantization | Description |
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| ---- | ----- | ----------- |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q2_K.gguf) | Q2_K | Lowest quality |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q3_K_S.gguf) | Q3_K_S | |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.IQ3_S.gguf) | IQ3_S | Integer quant, preferable over Q3_K_S |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.IQ3_M.gguf) | IQ3_M | Integer quant |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q3_K_M.gguf) | Q3_K_M | |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q3_K_L.gguf) | Q3_K_L | |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.IQ4_XS.gguf) | IQ4_XS | Integer quant |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q4_K_S.gguf) | Q4_K_S | Fast with good performance |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q4_K_M.gguf) | Q4_K_M | **Recommended:** Perfect mix of speed and performance |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q5_K_S.gguf) | Q5_K_S | |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q5_K_M.gguf) | Q5_K_M | |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q6_K.gguf) | Q6_K | Very good quality |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q8_0.gguf) | Q8_0 | Best quality |
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| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.f16.gguf) | f16 | Full precision, don't bother; use a quant |
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## Note from Flexan
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I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet,
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usually for models **I deem interesting and wish to try out**.
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If there are some quants missing that you'd like me to add, you may request one in the community tab.
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If you want to request a public model to be converted, you can also request that in the community tab.
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If you have questions regarding this model, please refer to [the original model repo](https://huggingface.co/QuantaSparkLabs/Quantum-X).
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You can find more info about me and what I do [here](https://huggingface.co/Flexan/Flexan).
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# Model Card for Quantum-X
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<p align="center">
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<img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/preview imgagee.png"
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alt="NYXIS Logo"
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width="160"
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height="160"
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style="border-radius: 50%; object-fit: cover;">
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</p>
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<p align="center">
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<img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/logoname.png"
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alt="NYXIS Name"
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width="700"
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style="border-radius: 18px;">
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</p>
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<p align="center">
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<a href="https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct"><img src="https://img.shields.io/badge/Base-Qwen%202.5%200.5B-blueviolet" alt="Base Model"></a>
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<a href="https://huggingface.co/datasets/teknium/OpenHermes-2.5"><img src="https://img.shields.io/badge/Data-OpenHermes%202.5-00BFFF" alt="Training Data"></a>
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<a href="#"><img src="https://img.shields.io/badge/Fine--Tune-QLoRA%20%2B%20Unsloth-FF6F00" alt="Fine-Tune Method"></a>
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<a href="#"><img src="https://img.shields.io/badge/Size-0.5B%20Params-lightgrey" alt="Model Size"></a>
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<a href="#"><img src="https://img.shields.io/badge/Speed-Blazing%20Fast-brightgreen" alt="Speed"></a>
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<a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-yellow" alt="License"></a>
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</p>
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# Quantum-X
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A compact, high‑speed conversational AI built on **Qwen 2.5 0.5B** — small enough for edge devices, smart enough for real conversation.
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## 📋 Overview
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Quantum‑X is a **0.5 billion parameter** language model developed by QuantaSparkLabs. It's fine‑tuned from Qwen 2.5 0.5B on a mix of OpenHermes‑2.5 conversations and custom identity data, giving it warm, direct conversational abilities while keeping inference blazingly fast.
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| Feature | Detail |
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|---------|--------|
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| **Base Model** | Qwen 2.5 0.5B‑Instruct |
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| **Parameters** | ~0.5B |
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| **Fine‑tuning** | QLoRA (Unsloth), 2 epochs |
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| **Training Data** | OpenHermes‑2.5 + identity examples |
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| **Tensor Precision** | FP16 |
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| **Chat Template** | ✅ Native Qwen2 chat template |
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## ✨ What It Does Well
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- **Conversational AI**: Natural, warm dialogue with identity baked in.
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- **Factual Q&A**: Answers general knowledge questions correctly.
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- **Fast Inference**: 0.5B parameters = near‑instant responses on CPU or GPU.
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- **Edge Friendly**: Runs comfortably on 2 GB RAM, even on a phone.
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## 💻 Quick Start
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "QuantaSparkLabs/Quantum-X"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True
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)
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messages = [
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{"role": "system", "content": "You are Quantum-X, created by QuantaSparkLabs."},
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{"role": "user", "content": "What is the capital of France?"}
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]
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inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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input_ids = tokenizer(inputs, return_tensors="pt").to(model.device)
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outputs = model.generate(**input_ids, max_new_tokens=100, temperature=0.7, do_sample=True)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## 🚀 Hardware Requirements
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| Environment | RAM | Storage | Ideal For |
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| :--- | :--- | :--- | :--- |
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| **CPU** | 2 GB | ~500 MB | Testing, embedded apps |
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| **GPU** | 1‑2 GB VRAM | ~500 MB | Development, serving |
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| **Edge / Mobile** | >1 GB | ~500 MB | On‑device inference |
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## ⚠️ Limitations
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- **Complex reasoning**: Multi‑step logic or advanced math may be inconsistent.
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- **Factual precision**: Can occasionally produce outdated or incorrect information.
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- **Not for high‑stakes use**: Don't use for medical, legal, or safety‑critical decisions.
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## 📄 License
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Apache 2.0
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---
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<center>Built with ❤️ by QuantaSparkLabs<br>Model ID: Quantum‑X • Rebuilt 2026</center> |