Strand-Rust-Coder-14B-v1
Overview
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.
Key Features
- 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
Performance Summary
| 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 Breakdown
| Task | Base | Strand-14B |
|---|---|---|
| test_generation | 0.00 | 0.51 |
| api_usage_prediction | 0.27 | 0.71 |
| function_naming | 0.53 | 0.87 |
| code_refactoring | 0.04 | 0.19–0.20 |
| variable_naming | 0.87 | 1.00 |
| code_generation | 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.
Dataset
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:
code_generation,code_completion,bug_detection,refactoring,optimizationdocstring_generation,code_review,summarization,test_generationnaming,API usage prediction,search
Dataset construction involved 2,383 crates from crates.io, automatic compilation tests, and semantic validation of ownership and lifetime correctness.
Dataset: Fortytwo-Network/Strandset-Rust-v1
Training Configuration
| 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 |
Model Architecture
- 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
Evaluation Protocol
- 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
Why It Matters
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.
Research & References
- 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
Intended Use
- 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.
Integration with Fortytwo Network
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
GGUF Quantized Versions
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 |
|---|---|---|---|
| Q8_0 | 15.7 GB | 8-bit | Near-full precision, for most demanding local inference |
| Q6_K | 12.1 GB | 6-bit | Balanced performance and efficiency |
| Q5_K_M | 10.5 GB | 5-bit | Lightweight deployment with strong accuracy retention |
| Q4_K_M | 8.99 GB | 4-bit | Ultra-fast, compact variant for consumer GPUs and laptops |
Usage
You can load the GGUF models with llama.cpp or compatible backends:
./main -m models/Strand-Rust-Coder-14B-v1.Q5_K_M.gguf -p "Write a Rust function that reads a file line by line."
Or run interactively in Jan, LM Studio or Ollama by simply importing the model.
License
These quantized weights are distributed under the same Apache 2.0 License as the original model.
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Base model
Qwen/Qwen2.5-14B