fortytwo-network/strand-rust-coder-14b-v1-gguf overview

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      "license": "apache-2.0",
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    "readme_markdown": "---\nlicense: apache-2.0\nbase_model:\n- Fortytwo-Network/Strand-Rust-Coder-14B-v1\nbase_model_relation: quantized\ndatasets:\n- Fortytwo-Network/Strandset-Rust-v1\npipeline_tag: text-generation\nlibrary_name: transformers\n---\n\n\n![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63aeda3a2314b93f9e706a68/I6WwY8U7I5V8lc138UmGt.jpeg)\n\n# Strand-Rust-Coder-14B-v1\n\n## Overview\n\n**Strand-Rust-Coder-14B-v1** is the first domain-specialized Rust language model created through **Fortytwo’s Swarm Inference**, a decentralized AI architecture where multiple models collaboratively generate, validate, and rank outputs through peer consensus.\n\nThe model fine-tunes **Qwen2.5-Coder-14B** for Rust-specific programming tasks using a **191K-example synthetic dataset** built via multi-model generation and peer-reviewed validation.  \nIt achieves **43–48% accuracy** on Rust-specific benchmarks – surpassing much larger proprietary models like GPT-5 Codex on Rust tasks – while maintaining competitive general coding performance.\n\n[Strand-Rust-Coder-v1: Technical Report](https://huggingface.co/blog/Fortytwo-Network/strand-rust-coder-tech-report)\n\n## Key Features\n\n- **Rust-specialized fine-tuning** on 15 diverse programming task categories  \n- **Peer-validated synthetic dataset** (191,008 verified examples, 94.3% compile rate)  \n- **LoRA-based fine-tuning** for efficient adaptation  \n- **Benchmarked across Rust-specific suites:**\n  - **RustEvo^2**  \n  - **Evaluation on Hold-Out Set**\n- **Deployed in the Fortytwo decentralized inference network** for collective AI reasoning  \n\n---\n\n## Performance Summary\n\n| **Model** | **Hold-Out Set** | **RustEvo^2** |\n|------------|------------------|---------------|\n| **Fortytwo-Rust-One-14B (Ours)** | **48.00%** | **43.00%** |\n| openai/gpt-5-codex | 47.00% | 28.00% |\n| anthropic/claude-sonnet-4.5 | 46.00% | 21.00% |\n| anthropic/claude-3.7-sonnet | 42.00% | 31.00% |\n| qwen/qwen3-max | 42.00% | 40.00% |\n| qwen/qwen3-coder-plus | 41.00% | 22.00% |\n| x-ai/grok-4 | 39.00% | 37.00% |\n| deepseek/deepseek-v3.1-terminus | 37.00% | 33.00% |\n| Qwen3-Coder-30B-A3B-Instruct | 36.00% | 20.00% |\n| openai/gpt-4o-latest | 34.00% | 39.00% |\n| deepseek/deepseek-chat | 34.00% | 41.00% |\n| google/gemini-2.5-flash | 33.00% | 7.00% |\n| Qwen2.5-Coder-14B-Instruct (Base) | 29.00% | 30.00% |\n| Qwen2.5-Coder-32B-Instruct | 29.00% | 31.00% |\n| google/gemini-2.5-pro | 28.00% | 22.00% |\n| qwen/qwen-2.5-72b | 28.00% | 32.00% |\n| Tesslate/Tessa-Rust-T1-7B | 23.00% | 19.00% |\n\n*Benchmarks on code tasks measured using unit-test pass rate@1 in Docker-isolated Rust 1.86.0 environment.*\n\n---\n\n## Task Breakdown\n\n| Task | Base | Strand-14B |\n|------|------|-------------|\n| test_generation | 0.00 | 0.51 |\n| api_usage_prediction | 0.27 | 0.71 |\n| function_naming | 0.53 | 0.87 |\n| code_refactoring | 0.04 | 0.19–0.20 |\n| variable_naming | 0.87 | 1.00 |\n| code_generation | 0.40 | 0.49 |\n\nLargest improvements appear in *test generation*, *API usage prediction*, and *refactoring* – areas demanding strong semantic reasoning about Rust’s ownership and lifetime rules.\n\n---\n\n## Dataset\n\n**Fortytwo-Network/Strandset-Rust-v1 (191,008 examples, 15 categories)**  \nBuilt through Fortytwo’s *Swarm Inference* pipeline, where multiple SLMs generate and cross-validate examples with peer review consensus and output aggregation.\n\n- 94.3% compile success rate  \n- 73.2% consensus acceptance  \n- Coverage of 89% of Rust language features  \n- Tasks include:\n  - `code_generation`, `code_completion`, `bug_detection`, `refactoring`, `optimization`\n  - `docstring_generation`, `code_review`, `summarization`, `test_generation`\n  - `naming`, `API usage prediction`, `search`  \n\nDataset construction involved 2,383 crates from crates.io, automatic compilation tests, and semantic validation of ownership and lifetime correctness.\n\nDataset: [Fortytwo-Network/Strandset-Rust-v1](https://huggingface.co/datasets/Fortytwo-Network/Strandset-Rust-v1)\n\n---\n\n## Training Configuration\n\n| Setting | Value |\n|----------|-------|\n| Base model | Qwen2.5-Coder-14B-Instruct |\n| Method | LoRA (r=64, α=16) |\n| Learning rate | 5e-5 |\n| Batch size | 128 |\n| Epochs | 3 |\n| Optimizer | AdamW |\n| Precision | bfloat16 |\n| Objective | Completion-only loss |\n| Context length | 32,768 |\n| Framework | PyTorch + FSDP + Flash Attention 2 |\n| Hardware | 8× H200 GPUs |\n\n---\n\n## Model Architecture\n\n- **Base:** Qwen2.5-Coder (14 B parameters, GQA attention, extended RoPE embeddings)  \n- **Tokenizer:** 151 k vocabulary optimized for Rust syntax  \n- **Context:** 32 k tokens  \n- **Fine-tuning:** Parameter-efficient LoRA adapters (≈1% of parameters updated)  \n- **Deployment:** Compatible with local deployment and Fortytwo Capsule runtime for distributed swarm inference  \n\n---\n\n## Evaluation Protocol\n\n- All evaluations executed in Docker-isolated Rust 1.86.0 environment  \n- **Code tasks:** measured via unit test pass rate  \n- **Documentation & naming tasks:** scored via LLM-based correctness (Claude Sonnet 4 judge)  \n- **Code completion & API tasks:** syntax-weighted Levenshtein similarity  \n- **Comment generation:** compilation success metric  \n\n---\n\n## Why It Matters\n\nRust is a high-safety, low-level language with complex ownership semantics that make it uniquely challenging for general-purpose LLMs.  \nAt the same time, there is simply **not enough high-quality training data on Rust**, as it remains a relatively modern and rapidly evolving language.  \nThis scarcity of large, reliable Rust datasets – combined with the language’s intricate borrow checker and type system – makes it an ideal benchmark for evaluating true model understanding and reasoning precision.\n\n**Strand-Rust-Coder** demonstrates how **specialized models** can outperform giant centralized models – achieving domain mastery with a fraction of the compute.  \nThrough **Fortytwo’s Swarm Inference**, the network was able to generate an **extremely accurate synthetic dataset**, enabling a **state-of-the-art Rust model** to be built through an efficient **LoRA fine-tune** rather than full retraining.\n\nThis work validates Fortytwo’s thesis: **intelligence can scale horizontally through networked specialization rather than centralized scale.**\n\n---\n\n## Research & References\n\n- [Fortytwo: Swarm Inference with Peer-Ranked Consensus (arXiv)](https://arxiv.org/abs/2510.24801) - *Fortytwo Swarm Inference – Technical Report*  \n- [Self-Supervised Inference of Agents in Trustless Environments (arXiv)](https://arxiv.org/abs/2409.08386) – *High-level overview of Fortytwo architecture*  \n\n---\n\n## Intended Use\n\n- Rust code generation, completion, and documentation  \n- Automated refactoring and test generation  \n- Integration into code copilots and multi-agent frameworks  \n- Research on domain-specialized model training and evaluation  \n\n### Limitations\n- May underperform on purely algorithmic or multi-language tasks (e.g., HumanEval-style puzzles).  \n- Not suitable for generating unverified production code without compilation and test validation.  \n\n---\n\n## Integration with Fortytwo Network\n\nStrand-Rust-Coder models are integrated into **Fortytwo’s decentralized Swarm Inference Network**, where specialized models collaborate and rank each other’s outputs.  \nThis structure enables **peer-reviewed inference**, improving reliability while reducing hallucinations and cost.\n\nTo run a Fortytwo node or contribute your own models and fine-tunes, visit: [fortytwo.network](https://fortytwo.network)\n\n---\n\n## GGUF Quantized Versions\n\nThis repository provides **GGUF-format quantizations** of the model [Fortytwo-Network/Strand-Rust-Coder-14B-v1](https://huggingface.co/Fortytwo-Network/Strand-Rust-Coder-14B-v1), optimized for local inference using tools such as **llama.cpp**, **Jan**, **Ollama**, **LM Studio** and other compatible runtimes.\n\nThese quantizations significantly reduce memory requirements while preserving near-original accuracy, making deployment possible on a wide range of consumer hardware.\n\n| **Quantization** | **File Size** | **Bit Precision** | **Description** |\n|------------------|-----------|------------------|----------------|\n| **Q8_0** | 15.7 GB | **8-bit** | Near-full precision, for most demanding local inference |\n| **Q6_K** | 12.1 GB | **6-bit** | Balanced performance and efficiency |\n| **Q5_K_M** | 10.5 GB | **5-bit** | Lightweight deployment with strong accuracy retention |\n| **Q4_K_M** | 8.99 GB | **4-bit** | Ultra-fast, compact variant for consumer GPUs and laptops |\n\n---\n\n### Usage\n\nYou can load the GGUF models with **llama.cpp** or compatible backends:\n\n```bash\n./main -m models/Strand-Rust-Coder-14B-v1.Q5_K_M.gguf -p \"Write a Rust function that reads a file line by line.\"\n```\n\nOr run interactively in **Jan**, **LM Studio** or **Ollama** by simply importing the model.\n\n---\n\n### License\n\nThese quantized weights are distributed under the same **Apache 2.0 License** as the original model.\n\n\n**Fortytwo – An open, networked intelligence shaped collectively by its participants**  \n\nJoin the swarm: [fortytwo.network](https://fortytwo.network)\n\nX: [@fortytwo](https://x.com/fortytwo) ",
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  "tags": [
    "transformers",
    "gguf",
    "text-generation",
    "dataset:Fortytwo-Network/Strandset-Rust-v1",
    "arxiv:2510.24801",
    "arxiv:2409.08386",
    "base_model:Fortytwo-Network/Strand-Rust-Coder-14B-v1",
    "base_model:quantized:Fortytwo-Network/Strand-Rust-Coder-14B-v1",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us"
  ],
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  "last_modified": "2026-01-05T17:20:17.000Z",
  "created_at": "2025-10-13T14:49:53.000Z",
  "pipeline_tag": "text-generation",
  "library_name": "transformers"
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