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
```

make 30b 3a model variant or using gpt 20b model

by Narutoouz - opened Feb 19

Discussion

Narutoouz

Feb 19

thanks for making this rust specialized quanting model.

inikitin

Fortytwo org Mar 1

We are planning a new iteration of the model trained on a larger dataset (will publish that too) generated with newer models on our network.

Besides better Rust skills, we are focusing on better instruction following, as the current model is more suited for code completion than full instruction-following coding tasks. We've been evaluating several base models and recent Qwen models are strong candidates based on our Rust benchmarks, though we're still testing.

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