Instructions to use Vinnnf/Thinkless-1.5B-RL-DeepScaleR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vinnnf/Thinkless-1.5B-RL-DeepScaleR with Transformers:

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

pipe = pipeline("text-generation", model="Vinnnf/Thinkless-1.5B-RL-DeepScaleR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Vinnnf/Thinkless-1.5B-RL-DeepScaleR")
model = AutoModelForCausalLM.from_pretrained("Vinnnf/Thinkless-1.5B-RL-DeepScaleR", device_map="auto")
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 Vinnnf/Thinkless-1.5B-RL-DeepScaleR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vinnnf/Thinkless-1.5B-RL-DeepScaleR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vinnnf/Thinkless-1.5B-RL-DeepScaleR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vinnnf/Thinkless-1.5B-RL-DeepScaleR

SGLang

How to use Vinnnf/Thinkless-1.5B-RL-DeepScaleR 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 "Vinnnf/Thinkless-1.5B-RL-DeepScaleR" \
    --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": "Vinnnf/Thinkless-1.5B-RL-DeepScaleR",
		"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 "Vinnnf/Thinkless-1.5B-RL-DeepScaleR" \
        --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": "Vinnnf/Thinkless-1.5B-RL-DeepScaleR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Vinnnf/Thinkless-1.5B-RL-DeepScaleR with Docker Model Runner:
```
docker model run hf.co/Vinnnf/Thinkless-1.5B-RL-DeepScaleR
```

Vinnnf commited on May 17, 2025

Commit

d01fce2

verified ·

1 Parent(s): e1aa7ec

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -5

README.md CHANGED Viewed

@@ -29,11 +29,13 @@ model = AutoModelForCausalLM.from_pretrained(
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 instruction = "Please reason step by step, and put your final answer within \\boxed{}."
-prompt = f"{instruction}\nHow many r's are in the word \"strawberry\""
-# prompt = f"{instruction}\nThe arithmetic mean of 7, 2, $x$ and 10 is 9. What is the value of $x$?"
 messages = [
-    {"role": "user", "content": prompt}
 ]
 text = tokenizer.apply_chat_template(
@@ -46,7 +48,7 @@ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
 generated_ids = model.generate(
     **model_inputs,
-    max_new_tokens=4096
 )
 generated_ids = [
     output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
@@ -60,7 +62,6 @@ think_mode = ("<think>" in response)
 print(text+response)
 print(f"\nThink Mode: {think_mode}")
 print(f"Number of tokens: {num_tokens}")
 ```

 tokenizer = AutoTokenizer.from_pretrained(model_name)
 instruction = "Please reason step by step, and put your final answer within \\boxed{}."
+# prompt = "How many r's are in the word \"strawberry\""
+# prompt = "The arithmetic mean of 7, 2, $x$ and 10 is 9. What is the value of $x$?"
+prompt = "Let $S$ be the set of points $(a,b)$ with $0 \le a,$ $b \le 1$ such that the equation \[x^4 + ax^3 - bx^2 + ax + 1 = 0\] has at least one real root.  Determine the area of the graph of $S.$"
 messages = [
+    {"role": "user", "content": f"{instruction}\n{prompt}"},
 ]
 text = tokenizer.apply_chat_template(
 generated_ids = model.generate(
     **model_inputs,
+    max_new_tokens=16384
 )
 generated_ids = [
     output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
 print(text+response)
 print(f"\nThink Mode: {think_mode}")
 print(f"Number of tokens: {num_tokens}")
 ```