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Model: DavidBPunkt/Strand-Rust-Coder-14B-v1
Source: Original Platform

.gitattributes

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README.md

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+---
+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) 

added_tokens.json

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+{
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+  "<tool_call>": 151657,
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+  "<|box_start|>": 151648,
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+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are an expert in Rust programming language.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are an expert in Rust programming language.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 48,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.56.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.56.2"
+}

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