59 Rust Coder 14B

image/jpeg

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

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

---

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

---

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.

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

---

Fortytwo – An open, networked intelligence shaped collectively by its participants

Join the swarm: fortytwo.network

X: @fortytwo