Instructions to use SkyAsl/Rust-Master-thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SkyAsl/Rust-Master-thinking with Transformers:

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

pipe = pipeline("text-generation", model="SkyAsl/Rust-Master-thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SkyAsl/Rust-Master-thinking")
model = AutoModelForCausalLM.from_pretrained("SkyAsl/Rust-Master-thinking")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SkyAsl/Rust-Master-thinking with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SkyAsl/Rust-Master-thinking

SGLang

How to use SkyAsl/Rust-Master-thinking 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 "SkyAsl/Rust-Master-thinking" \
    --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": "SkyAsl/Rust-Master-thinking",
		"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 "SkyAsl/Rust-Master-thinking" \
        --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": "SkyAsl/Rust-Master-thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SkyAsl/Rust-Master-thinking with Docker Model Runner:
```
docker model run hf.co/SkyAsl/Rust-Master-thinking
```

🧠 Rust-Master-thinking

This repository contains a fine-tuned version of unsloth/phi-4-reasoning, trained with LoRA on the Tesslate/Rust_Dataset. The goal of this project is to enhance the model's reasoning, explanation, and step-by-step thinking abilities specifically for Rust-related tasks.

🚀 Model Purpose

This model was fine-tuned to:

Improve Rust coding explanations
Generate high-quality reasoning traces
Provide step-by-step problem solving
Give detailed and structured answers

The training format follows:

<|user|>
{prompt}
<|assistant|>
<think>
{reasoning}
</think>
{response}

🔧 How to Use

Install dependencies (if not installed):

pip install transformers bitsandbytes

Load model normally:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "SkyAsl/Rust-Master-thinking"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
model.eval()

prompt = "Explain why Rust ownership prevents data races."

input_text = (
    f"<|user|>\n{prompt}\n"
    f"<|assistant|>\n<think>\n"
)

inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=3000,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        repetition_penalty=1.2,
    )

print(tokenizer.decode(output[0], skip_special_tokens=False))

🧩 Base Model

unsloth/phi-4-reasoning

14B parameter reasoning-optimized model
Uses internal <think> reasoning
Strong on step-by-step chain-of-thought tasks

🛠 Fine-Tuning Details

Setting	Value
Method	LoRA (PEFT)
Rank (r)	16
Alpha	32
Dropout	0.05
Target Modules	q/k/v/o proj, mlp (up/down/gate)
Max Length	512
Precision	4-bit QLoRA
Batch Size	16
Grad Accum	8
LR	2e-4
Scheduler	cosine
Epochs	1

🤖 Evaluation

Epoch	Training Loss	Validation Loss
1	2.251500	2.191743

📚 Dataset

Tesslate/Rust_Dataset

Includes:

Rust prompts
Step-by-step reasoning
Final answers

This dataset improves the model's ability to produce structured and accurate explanations for Rust programming tasks.

Downloads last month: 2

Safetensors

Model size

15B params

Tensor type

F32

F16

Model tree for SkyAsl/Rust-Master-thinking

Base model

microsoft/phi-4

Finetuned

microsoft/Phi-4-reasoning

Finetuned

unsloth/phi-4-reasoning

Adapter

(1)

this model

SkyAsl
/

Rust-Master-thinking