Instructions to use Fortytwo-Network/Strand-Rust-Coder-14B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fortytwo-Network/Strand-Rust-Coder-14B-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Fortytwo-Network/Strand-Rust-Coder-14B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# 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": "Who are you?"},
]
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Fortytwo-Network/Strand-Rust-Coder-14B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fortytwo-Network/Strand-Rust-Coder-14B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fortytwo-Network/Strand-Rust-Coder-14B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fortytwo-Network/Strand-Rust-Coder-14B-v1

SGLang

How to use Fortytwo-Network/Strand-Rust-Coder-14B-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Fortytwo-Network/Strand-Rust-Coder-14B-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fortytwo-Network/Strand-Rust-Coder-14B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Fortytwo-Network/Strand-Rust-Coder-14B-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fortytwo-Network/Strand-Rust-Coder-14B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fortytwo-Network/Strand-Rust-Coder-14B-v1 with Docker Model Runner:
```
docker model run hf.co/Fortytwo-Network/Strand-Rust-Coder-14B-v1
```

inikitin commited on Oct 15, 2025

Commit

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@@ -25,7 +25,7 @@ It achieves **43–48% accuracy** on Rust-specific benchmarks – surpassing muc
 - **Peer-validated synthetic dataset** (191,008 verified examples, 94.3% compile rate)
 - **LoRA-based fine-tuning** for efficient adaptation
 - **Benchmarked across Rust-specific suites:**
-  - **RustEvo²**
   - **Evaluation on Hold-Out Set**
 - **Deployed in the Fortytwo decentralized inference network** for collective AI reasoning
@@ -33,7 +33,7 @@ It achieves **43–48% accuracy** on Rust-specific benchmarks – surpassing muc
 ## Performance Summary
-| **Model** | **Hold-Out Set** | **RustEvo²** |
 |------------|------------------|---------------|
 | **Fortytwo-Rust-One-14B (Ours)** | **48.00%** | **43.00%** |
 | openai/gpt-5-codex | 47.00% | 28.00% |

 - **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% |