Fortytwo-Network

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. The 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. It 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. - Rust-specialized fine-tuning on 15 diverse programming task categories - Peer-validated synthetic dataset (191,008 verified examples, 94.3% compile rate) - LoRA-based fine-tuning for efficient adaptation - Benchmarked across Rust-specific suites: - RustEvo^2 - Evaluation on Hold-Out Set - Deployed in the Fortytwo decentralized inference network for collective AI reasoning | Model | Hold-Out Set | RustEvo^2 | |------------|------------------|---------------| | Fortytwo-Rust-One-14B (Ours) | 48.00% | 43.00% | | openai/gpt-5-codex | 47.00% | 28.00% | | anthropic/claude-sonnet-4.5 | 46.00% | 21.00% | | anthropic/claude-3.7-sonnet | 42.00% | 31.00% | | qwen/qwen3-max | 42.00% | 40.00% | | qwen/qwen3-coder-plus | 41.00% | 22.00% | | x-ai/grok-4 | 39.00% | 37.00% | | deepseek/deepseek-v3.1-terminus | 37.00% | 33.00% | | Qwen3-Coder-30B-A3B-Instruct | 36.00% | 20.00% | | openai/gpt-4o-latest | 34.00% | 39.00% | | deepseek/deepseek-chat | 34.00% | 41.00% | | google/gemini-2.5-flash | 33.00% | 7.00% | | Qwen2.5-Coder-14B-Instruct (Base) | 29.00% | 30.00% | | Qwen2.5-Coder-32B-Instruct | 29.00% | 31.00% | | google/gemini-2.5-pro | 28.00% | 22.00% | | qwen/qwen-2.5-72b | 28.00% | 32.00% | | Tesslate/Tessa-Rust-T1-7B | 23.00% | 19.00% | Benchmarks on code tasks measured using unit-test pass rate@1 in Docker-isolated Rust 1.86.0 environment. | Task | Base | Strand-14B | |------|------|-------------| | testgeneration | 0.00 | 0.51 | | apiusageprediction | 0.27 | 0.71 | | functionnaming | 0.53 | 0.87 | | coderefactoring | 0.04 | 0.19–0.20 | | variablenaming | 0.87 | 1.00 | | codegeneration | 0.40 | 0.49 | Largest improvements appear in test generation, API usage prediction, and refactoring – areas demanding strong semantic reasoning about Rust’s ownership and lifetime rules. Fortytwo-Network/Strandset-Rust-v1 (191,008 examples, 15 categories) Built through Fortytwo’s Swarm Inference pipeline, where multiple SLMs generate and cross-validate examples with peer review consensus and output aggregation. - 94.3% compile success rate - 73.2% consensus acceptance - Coverage of 89% of Rust language features - Tasks include: - `codegeneration`, `codecompletion`, `bugdetection`, `refactoring`, `optimization` - `docstringgeneration`, `codereview`, `summarization`, `testgeneration` - `naming`, `API usage prediction`, `search` Dataset construction involved 2,383 crates from crates.io, automatic compilation tests, and semantic validation of ownership and lifetime correctness. | Setting | Value | |----------|-------| | Base model | Qwen2.5-Coder-14B-Instruct | | Method | LoRA (r=64, α=16) | | Learning rate | 5e-5 | | Batch size | 128 | | Epochs | 3 | | Optimizer | AdamW | | Precision | bfloat16 | | Objective | Completion-only loss | | Context length | 32,768 | | Framework | PyTorch + FSDP + Flash Attention 2 | | Hardware | 8× H200 GPUs | - Base: Qwen2.5-Coder (14 B parameters, GQA attention, extended RoPE embeddings) - Tokenizer: 151 k vocabulary optimized for Rust syntax - Context: 32 k tokens - Fine-tuning: Parameter-efficient LoRA adapters (≈1% of parameters updated) - Deployment: Compatible with local deployment and Fortytwo Capsule runtime for distributed swarm inference - All evaluations executed in Docker-isolated Rust 1.86.0 environment - Code tasks: measured via unit test pass rate - Documentation & naming tasks: scored via LLM-based correctness (Claude Sonnet 4 judge) - Code completion & API tasks: syntax-weighted Levenshtein similarity - Comment generation: compilation success metric Rust is a high-safety, low-level language with complex ownership semantics that make it uniquely challenging for general-purpose LLMs. At the same time, there is simply not enough high-quality training data on Rust, as it remains a relatively modern and rapidly evolving language. This 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. Strand-Rust-Coder demonstrates how specialized models can outperform giant centralized models – achieving domain mastery with a fraction of the compute. Through 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. This work validates Fortytwo’s thesis: intelligence can scale horizontally through networked specialization rather than centralized scale. - Fortytwo: Swarm Inference with Peer-Ranked Consensus (arXiv) - Fortytwo Swarm Inference – Technical Report - Self-Supervised Inference of Agents in Trustless Environments (arXiv) – High-level overview of Fortytwo architecture - Rust code generation, completion, and documentation - Automated refactoring and test generation - Integration into code copilots and multi-agent frameworks - Research on domain-specialized model training and evaluation Limitations - May underperform on purely algorithmic or multi-language tasks (e.g., HumanEval-style puzzles). - Not suitable for generating unverified production code without compilation and test validation. Strand-Rust-Coder models are integrated into Fortytwo’s decentralized Swarm Inference Network, where specialized models collaborate and rank each other’s outputs. This structure enables peer-reviewed inference, improving reliability while reducing hallucinations and cost. To run a Fortytwo node or contribute your own models and fine-tunes, visit: fortytwo.network This repository provides GGUF-format quantizations of the model 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. These quantizations significantly reduce memory requirements while preserving near-original accuracy, making deployment possible on a wide range of consumer hardware. | Quantization | File Size | Bit Precision | Description | |------------------|-----------|------------------|----------------| | Q80 | 15.7 GB | 8-bit | Near-full precision, for most demanding local inference | | Q6K | 12.1 GB | 6-bit | Balanced performance and efficiency | | Q5KM | 10.5 GB | 5-bit | Lightweight deployment with strong accuracy retention | | Q4KM | 8.99 GB | 4-bit | Ultra-fast, compact variant for consumer GPUs and laptops | You can load the GGUF models with llama.cpp or compatible backends: Or run interactively in Jan, LM Studio or Ollama by simply importing the model. These quantized weights are distributed under the same Apache 2.0 License as the original model. Fortytwo – An open, networked intelligence shaped collectively by its participants

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. The 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. It 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. - Rust-specialized fine-tuning on 15 diverse programming task categories - Peer-validated synthetic dataset (191,008 verified examples, 94.3% compile rate) - LoRA-based fine-tuning for efficient adaptation - Benchmarked across Rust-specific suites: - RustEvo^2 - Evaluation on Hold-Out Set - Deployed in the Fortytwo decentralized inference network for collective AI reasoning | Model | Hold-Out Set | RustEvo^2 | |------------|------------------|---------------| | Fortytwo-Rust-One-14B (Ours) | 48.00% | 43.00% | | openai/gpt-5-codex | 47.00% | 28.00% | | anthropic/claude-sonnet-4.5 | 46.00% | 21.00% | | anthropic/claude-3.7-sonnet | 42.00% | 31.00% | | qwen/qwen3-max | 42.00% | 40.00% | | qwen/qwen3-coder-plus | 41.00% | 22.00% | | x-ai/grok-4 | 39.00% | 37.00% | | deepseek/deepseek-v3.1-terminus | 37.00% | 33.00% | | Qwen3-Coder-30B-A3B-Instruct | 36.00% | 20.00% | | openai/gpt-4o-latest | 34.00% | 39.00% | | deepseek/deepseek-chat | 34.00% | 41.00% | | google/gemini-2.5-flash | 33.00% | 7.00% | | Qwen2.5-Coder-14B-Instruct (Base) | 29.00% | 30.00% | | Qwen2.5-Coder-32B-Instruct | 29.00% | 31.00% | | google/gemini-2.5-pro | 28.00% | 22.00% | | qwen/qwen-2.5-72b | 28.00% | 32.00% | | Tesslate/Tessa-Rust-T1-7B | 23.00% | 19.00% | Benchmarks on code tasks measured using unit-test pass rate@1 in Docker-isolated Rust 1.86.0 environment. | Task | Base | Strand-14B | |------|------|-------------| | testgeneration | 0.00 | 0.51 | | apiusageprediction | 0.27 | 0.71 | | functionnaming | 0.53 | 0.87 | | coderefactoring | 0.04 | 0.19–0.20 | | variablenaming | 0.87 | 1.00 | | codegeneration | 0.40 | 0.49 | Largest improvements appear in test generation, API usage prediction, and refactoring – areas demanding strong semantic reasoning about Rust’s ownership and lifetime rules. Fortytwo-Network/Strandset-Rust-v1 (191,008 examples, 15 categories) Built through Fortytwo’s Swarm Inference pipeline, where multiple SLMs generate and cross-validate examples with peer review consensus and output aggregation. - 94.3% compile success rate - 73.2% consensus acceptance - Coverage of 89% of Rust language features - Tasks include: - `codegeneration`, `codecompletion`, `bugdetection`, `refactoring`, `optimization` - `docstringgeneration`, `codereview`, `summarization`, `testgeneration` - `naming`, `API usage prediction`, `search` Dataset construction involved 2,383 crates from crates.io, automatic compilation tests, and semantic validation of ownership and lifetime correctness. | Setting | Value | |----------|-------| | Base model | Qwen2.5-Coder-14B-Instruct | | Method | LoRA (r=64, α=16) | | Learning rate | 5e-5 | | Batch size | 128 | | Epochs | 3 | | Optimizer | AdamW | | Precision | bfloat16 | | Objective | Completion-only loss | | Context length | 32,768 | | Framework | PyTorch + FSDP + Flash Attention 2 | | Hardware | 8× H200 GPUs | - Base: Qwen2.5-Coder (14 B parameters, GQA attention, extended RoPE embeddings) - Tokenizer: 151 k vocabulary optimized for Rust syntax - Context: 32 k tokens - Fine-tuning: Parameter-efficient LoRA adapters (≈1% of parameters updated) - Deployment: Compatible with local deployment and Fortytwo Capsule runtime for distributed swarm inference - All evaluations executed in Docker-isolated Rust 1.86.0 environment - Code tasks: measured via unit test pass rate - Documentation & naming tasks: scored via LLM-based correctness (Claude Sonnet 4 judge) - Code completion & API tasks: syntax-weighted Levenshtein similarity - Comment generation: compilation success metric Rust is a high-safety, low-level language with complex ownership semantics that make it uniquely challenging for general-purpose LLMs. At the same time, there is simply not enough high-quality training data on Rust, as it remains a relatively modern and rapidly evolving language. This 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. Strand-Rust-Coder demonstrates how specialized models can outperform giant centralized models – achieving domain mastery with a fraction of the compute. Through 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. This work validates Fortytwo’s thesis: intelligence can scale horizontally through networked specialization rather than centralized scale. - Fortytwo: Swarm Inference with Peer-Ranked Consensus (arXiv) - Fortytwo Swarm Inference – Technical Report - Self-Supervised Inference of Agents in Trustless Environments (arXiv) – High-level overview of Fortytwo architecture - Rust code generation, completion, and documentation - Automated refactoring and test generation - Integration into code copilots and multi-agent frameworks - Research on domain-specialized model training and evaluation Limitations - May underperform on purely algorithmic or multi-language tasks (e.g., HumanEval-style puzzles). - Not suitable for generating unverified production code without compilation and test validation. Strand-Rust-Coder models are integrated into Fortytwo’s decentralized Swarm Inference Network, where specialized models collaborate and rank each other’s outputs. This structure enables peer-reviewed inference, improving reliability while reducing hallucinations and cost. To run a Fortytwo node or contribute your own models and fine-tunes, visit: fortytwo.network Optimized GGUF quantizations of Strand-Rust-Coder-14B-v1 are available for local and Fortytwo Node deployment, offering reduced memory footprint with minimal performance trade-off. These builds are compatible with llama.cpp, Jan, LM Studio, Ollama, and other runtimes supporting the GGUF format. | Quantization | Size | Bit Precision | Description | |------------------|-----------|------------------|----------------| | Q80 | 15.7 GB | 8-bit | Near-full precision, for most demanding local inference | | Q6K | 12.1 GB | 6-bit | Balanced performance and efficiency | | Q5KM | 10.5 GB | 5-bit | Lightweight deployment with strong accuracy retention | | Q4KM | 8.99 GB | 4-bit | Ultra-fast, compact variant for consumer GPUs and laptops | Quant versions: Fortytwo-Network/Strand-Rust-Coder-14B-v1-GGUF Fortytwo – An open, networked intelligence shaped collectively by its participants