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Model: manvadariya1/Zynthos-Reasoning-4B-GGUF Source: Original Platform
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Zynthos-Reasoning-4B-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-4B-Base
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model_creator: Qwen
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model_name: Zynthos Reasoning 4B
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pipeline_tag: text-generation
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quantized_by: manvadariya1
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language:
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- en
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tags:
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- text-generation
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- gguf
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- reasoning
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- grpo
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- reinforcement-learning
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- math
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- code
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- agentic-ai
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- rag
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- mcp
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---
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# 🌀 Zynthos-Reasoning-4B (The Edge Reasoning Revolution)
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Zynthos-Reasoning-4B is a highly specialized, compute-optimized local reasoning model engineered to execute complex multi-step logical chain-of-thought operations directly at the edge.
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By taking the raw architectural foundation of **Qwen3-4B-Base** and applying an intensive, dual-stage training blueprint (Supervised Fine-Tuning + Group Relative Policy Optimization), Zynthos introduces an incredibly agile, low-overhead intelligence layer that matches the thinking depth of models many times its size.
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---
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## 🚀 True Local Sovereignty: The Multi-Agent & RAG Edge Engine
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Large reasoning engines are too slow and expensive to act as real-time workers. **Zynthos-Reasoning-4B** bridges this gap perfectly, acting as an efficient local processor designed for modern AI architectures:
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* **⚡ Agentic AI Ecosystems:** Natively executes autonomous agent loops. It easily maps out complex, multi-layered action plans before calling local programmatic tools or executing tool pathways.
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* **📂 Advanced Local RAG Sorting:** Rather than blindly extracting vector chunks, Zynthos reads retrieved context pipelines with adaptive reasoning—filtering out noise, evaluating facts, and synthesizing accurate answers without hallucinating.
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* **🔌 Native MCP Architecture Integration:** Ideal for driving **Model Context Protocol (MCP)** setups. It acts as the local brain that translates raw server signals, constructs valid infrastructure connections, and safely manages automated software workflows.
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* **🔢 Adaptive Mathematical Reasoning:** Features an activated `<think>` loop that dynamically scales its cognitive effort based on problem complexity—effortlessly tackling advanced algebra, code logic bugs, and structural derivations.
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---
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## 🛠️ The Paradigm-Shifting Training Pipeline
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Zynthos proves that ultra-curated data mixtures can break through the brute-force compute bottleneck. The model was aligned using a specialized asset pipeline:
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1. **Stage I: High-Fidelity SFT Alignment:** Instilled command-following structures and rigorous multi-turn code dialogue styles using `deepmath_15k_hard_sft.jsonl`, `codefeedback_sft_15k.jsonl`, and high-density `claude_traces_sft.jsonl` data sets.
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2. **Stage II: Accelerated GRPO Reinforcement Learning:** Rather than running an unguided 100,000-iteration cluster run, Zynthos underwent a highly targeted **450-iteration GRPO reinforcement learning trajectory** across specialized mathematical and programming corpora (`deepmath_grpo_60k`, `code_reasoning_grpo_43k`, `stratos_grpo_17k`, `taco_only_grpo_35k`, and `codefeedback_grpo_18k`).
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> **💡 The Efficiency Breakthrough:** This accelerated 450-iteration training setup demonstrates that precision data curation enables a 4B parameter model to achieve deep logical self-correction capabilities at a fraction of standard industry compute costs.
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---
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## 📊 Quantization & Hardware Deployment Matrix
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Every GGUF block in this lineup has been meticulously compiled to safeguard tensor values, providing predictable memory tracking and sub-millisecond execution loops across your graphics layers.
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> ### ⭐ Target Deployment Recommendation
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> * **For Absolute Peak Precision:** **`Zynthos-Reasoning-4B-F16.gguf` is the highly recommended choice.** It retains 100% of the raw, unquantized model weights, providing the ultimate logical depth, perfect tool-calling syntax, and total resistance to token regression.
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> * **For Resource-Constrained Hardware:** Use **`Zynthos-Reasoning-4B-Q4_K_M.gguf`**. At just 2.4 GB, it runs flawlessly at maximum execution speeds on low-end consumer hardware or budget machines with as little as **4 GB of total system RAM**.
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| File Name | Precision Weights | File Size | Recommended System RAM | Core Deployment Target |
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| :--- | :--- | :--- | :--- | :--- |
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| **`Zynthos-Reasoning-4B-F16.gguf`** | **Full FP16 Master** | **~7.5 GB** | **12 GB RAM** | 🏆 **Recommended Tier:** Sovereign server automation, deep math, & production agent pipelines |
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| `Zynthos-Reasoning-4B-Q8_0.gguf` | 8-bit Standard | ~4.0 GB | 8 GB RAM | Balanced Tier: Scalable local RAG scanning and heavy contextual extraction |
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| `Zynthos-Reasoning-4B-Q4_K_M.gguf` | 4-bit Medium | ~2.4 GB | **4 GB RAM** | Ultra-Fast Tier: Agile on-device agents, low-end laptop setups, and ultra-budget edge containers |
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---
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## 💻 Quickstart Implementation Playbook
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### 1. Drag-and-Drop Local Runtime (LM Studio)
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1. Download the recommended `Zynthos-Reasoning-4B-F16.gguf` variant directly from the files menu.
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2. Drop the asset file into your dedicated local model paths directory.
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3. Select the model from your dashboard dropdown, maximize **GPU Offload** settings, and start chatting locally with full chain-of-thought support.
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### 2. Programmatic Agentic Orchestration (`llama-cpp-python`)
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Build local background agent tools, automated MCP systems, or RAG processors using this direct Python automation layout:
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```python
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from llama_cpp import Llama
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# Initialize the recommended pristine FP16 engine lane
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llm = Llama(
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model_path="./Zynthos-Reasoning-4B-F16.gguf",
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n_ctx=8192, # Expanded context window for deep chain-of-thought trace paths
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n_gpu_layers=-1 # Fully offload model weight processing layers to your local GPU
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)
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prompt = """<|im_start|>system
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You are Zynthos-Reasoning, a model that thinks carefully before responding. Show your step-by-step thinking inside a <think> block, and output your final answer outside.
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<|im_end|>
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<|im_start|>user
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Write an optimized Python function to securely manage incoming Model Context Protocol (MCP) data payloads, then verify its time complexity.<|im_end|>
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<|im_start|>assistant
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<think>"""
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output = llm(prompt, max_tokens=1024, stop=["<|im_end|>"])
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print(output['choices'][0]['text'])
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Zynthos-Reasoning-4B-F16.gguf
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version https://git-lfs.github.com/spec/v1
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size 8051284864
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Zynthos-Reasoning-4B-Q4_K_M.gguf
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Zynthos-Reasoning-4B-Q4_K_M.gguf
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version https://git-lfs.github.com/spec/v1
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size 2497280384
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Zynthos-Reasoning-4B-Q8_0.gguf
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Zynthos-Reasoning-4B-Q8_0.gguf
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version https://git-lfs.github.com/spec/v1
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size 4280404864
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