59 Rust Coder 14B

Strand-Rust-Coder-v1: Technical 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

Benchmarks on code tasks measured using unit-test pass rate@1 in Docker-isolated Rust 1.86.0 environment.

Task Breakdown

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

Training Configuration

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

Inference Examples

Using pipeline

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

# 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.

Quant versions: Fortytwo-Network/Strand-Rust-Coder-14B-v1-GGUF

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