Instructions to use fdyrd/QwenMath-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fdyrd/QwenMath-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="fdyrd/QwenMath-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fdyrd/QwenMath-0.5B")
model = AutoModelForCausalLM.from_pretrained("fdyrd/QwenMath-0.5B")
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 fdyrd/QwenMath-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fdyrd/QwenMath-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fdyrd/QwenMath-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fdyrd/QwenMath-0.5B

SGLang

How to use fdyrd/QwenMath-0.5B 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 "fdyrd/QwenMath-0.5B" \
    --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": "fdyrd/QwenMath-0.5B",
		"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 "fdyrd/QwenMath-0.5B" \
        --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": "fdyrd/QwenMath-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fdyrd/QwenMath-0.5B with Docker Model Runner:
```
docker model run hf.co/fdyrd/QwenMath-0.5B
```

QwenMath

A generation LLM which can solve math problems.

Training Statistics

training-method: lora
training-time: "5:42"
data-size: 500
epoch: 3
total_flos: "1372250GF"
train_loss: 0.6441
train_samples_per_second: 4.385
train_steps_per_second: 0.544

Validation Set Performance

Dataset used: test split of fdyrd/MATH. Metric: accuracy

Level	Algebra	Intermediate Algebra	Prealgebra	Precalculus	Number Theory	Geometry	Counting & Probability	Average
Level 1	0.541 : 135	0.192 : 52	0.477 : 86	0.228 : 57	0.467 : 30	0.263 : 38	0.359 : 39	0.361
Level 2	0.323 : 201	0.109 : 128	0.367 : 177	0.044 : 113	0.38 : 92	0.134 : 82	0.248 : 101	0.229
Level 3	0.291 : 261	0.046 : 195	0.308 : 224	0.0 : 127	0.262 : 122	0.088 : 102	0.16 : 100	0.165
Level 4	0.18 : 283	0.024 : 248	0.22 : 191	0.009 : 114	0.169 : 142	0.064 : 125	0.09 : 111	0.108
Level 5	0.088 : 307	0.004 : 280	0.104 : 193	0.0 : 135	0.136 : 154	0.023 : 132	0.065 : 123	0.06
Average	0.285	0.075	0.295	0.056	0.283	0.114	0.184	0.166

Test Set Performance

[
  {
    "dataset": "MATH500",
    "url": "https://huggingface.co/datasets/qq8933/MATH500",
    "accuracy": 0.286
  },
  {
    "dataset": "GSM8K",
    "url": "https://huggingface.co/datasets/openai/gsm8k",
    "accuracy": 0.382
  }
]

Downloads last month: 11

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for fdyrd/QwenMath-0.5B

Base model

Qwen/Qwen2.5-0.5B

Finetuned

(614)

this model

fdyrd
/

QwenMath-0.5B

QwenMath

Training Statistics

Validation Set Performance

Test Set Performance

Model tree for fdyrd/QwenMath-0.5B

Dataset used to train fdyrd/QwenMath-0.5B