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 Settings

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
```

SkyAsl commited on Dec 5, 2025

Commit

7ee6614

verified ·

1 Parent(s): 54070a5

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -2

README.md CHANGED Viewed

@@ -48,6 +48,10 @@ The training format follows:
 ## 🔧 How to Use
 ### Load model normally:
 ``` python
@@ -72,11 +76,11 @@ 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,
-        eos_token_id=tokenizer.convert_tokens_to_ids("</think>")
     )
 print(tokenizer.decode(output[0], skip_special_tokens=False))

 ## 🔧 How to Use
+### Install dependencies (if not installed):
+```bash
+pip install transformers bitsandbytes
+```
 ### Load model normally:
 ``` python
 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))