---
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
---

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63aeda3a2314b93f9e706a68/I6WwY8U7I5V8lc138UmGt.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](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) 