--- license: apache-2.0 datasets: - Fortytwo-Network/Strandset-Rust-v1 base_model: - Qwen/Qwen2.5-Coder-14B-Instruct pipeline_tag: text-generation library_name: transformers ---  # 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. [Strand-Rust-Coder-v1: Technical Report](https://huggingface.co/blog/Fortytwo-Network/strand-rust-coder-tech-report) ## 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`, `optimization` - `docstring_generation`, `code_review`, `summarization`, `test_generation` - `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. Dataset: [Fortytwo-Network/Strandset-Rust-v1](https://huggingface.co/datasets/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)](https://arxiv.org/abs/2510.24801) - *Fortytwo Swarm Inference – Technical Report* - [Self-Supervised Inference of Agents in Trustless Environments (arXiv)](https://arxiv.org/abs/2409.08386) – *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](https://fortytwo.network) --- ## Inference Examples ### Using `pipeline` ```python from transformers import pipeline pipe = pipeline("text-generation", model="Fortytwo-Network/Strand-Rust-Coder-14B-v1") messages = [ {"role": "user", "content": "Write a Rust function that finds the first string longer than 10 characters in a vector."}, ] pipe(messages) ``` ### Using Transformers Directly ```python # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Fortytwo-Network/Strand-Rust-Coder-14B-v1") model = AutoModelForCausalLM.from_pretrained("Fortytwo-Network/Strand-Rust-Coder-14B-v1") messages = [ {"role": "user", "content": "Write a Rust function that finds the first string longer than 10 characters in a vector."}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) ``` --- ## Quantized Versions 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** | |------------------|-----------|------------------|----------------| | **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 | Quant versions: [Fortytwo-Network/Strand-Rust-Coder-14B-v1-GGUF](https://huggingface.co/Fortytwo-Network/Strand-Rust-Coder-14B-v1-GGUF) --- **Fortytwo – An open, networked intelligence shaped collectively by its participants** Join the swarm: [fortytwo.network](https://fortytwo.network) X: [@fortytwo](https://x.com/fortytwo)
