Instructions to use prithivMLmods/TESS-QwenRe-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/TESS-QwenRe-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/TESS-QwenRe-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/TESS-QwenRe-1.5B")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/TESS-QwenRe-1.5B")
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 prithivMLmods/TESS-QwenRe-1.5B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/prithivMLmods/TESS-QwenRe-1.5B

SGLang

How to use prithivMLmods/TESS-QwenRe-1.5B 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 "prithivMLmods/TESS-QwenRe-1.5B" \
    --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": "prithivMLmods/TESS-QwenRe-1.5B",
		"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 "prithivMLmods/TESS-QwenRe-1.5B" \
        --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": "prithivMLmods/TESS-QwenRe-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/TESS-QwenRe-1.5B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/TESS-QwenRe-1.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

TESS-QwenRe-1.5B

TESS-QwenRe-1.5B is a chain-of-thought reasoning model, distilled from DeepSeek R1 1.5B and fine-tuned from Qwen-1.5B. It is designed to tackle mathematical problems in English and Chinese, with an emphasis on long-context reasoning and step-by-step explanations — ideal for tutoring, competitive exam preparation, and STEM education tools.

Key Features

Chain-of-Thought Math Reasoning
Trained to generate intermediate reasoning steps, TESS-QwenRe-1.5B offers transparent and interpretable solutions for math problems — essential for educational clarity and verification.
Bilingual Support (English + Chinese)
Supports mathematical problem solving and explanation in both English and Simplified Chinese, enabling global and bilingual learning applications.
Long-Context Problem Solving
Specially optimized for solving multi-step, long-form math problems — perfect for word problems, reasoning chains, and competitive math exams.
Distilled from DeepSeek R1 1.5B
Combines the reasoning capabilities of DeepSeek R1 with the lightweight and efficient architecture of Qwen-1.5B, delivering powerful results in a compact footprint.
Step-by-Step Explanations
Mimics expert human problem solving with clear, structured steps that help learners follow along and develop understanding.

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/TESS-QwenRe-1.5B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Solve: A train travels 180 km in 3 hours. What is its average speed?"
messages = [
    {"role": "system", "content": "You are a helpful tutor skilled in solving math problems with step-by-step explanations."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Intended Use

Math Tutoring Assistants: Ideal for school and exam-level math instruction with detailed explanations.
Bilingual EdTech Apps: Useful in Chinese-English math learning platforms.
STEM Reasoning Tasks: Reasoning support for science, engineering, and logical problem domains.
Efficient LLM Deployments: Well-suited for on-device or browser-based reasoning agents.

Limitations

Specialized Domain:
Tuned for math and logic; may be less effective in open-ended or creative tasks.
Compact Model Constraints:
As a 1.5B parameter model, it may underperform on extremely complex or abstract problems versus larger models.
Inherited Bias:
Distilled and fine-tuned from larger models; outputs should be monitored in sensitive contexts.
Prompt Dependency:
Accurate and structured prompts lead to the best outcomes in problem-solving scenarios.