Strand-Rust-Coder-14B-v1 GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 05fa625ea.

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Quantization Beyond the IMatrix

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here:
👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

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Click here to get info on choosing the right GGUF model format

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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

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Performance Summary

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

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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.

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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

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Training Configuration

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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

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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

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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.

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🔬 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

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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.

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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

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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]:]))

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

Join the swarm: fortytwo.network

X: @fortytwo

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊