Instructions to use ParadiseYu/TS-Reasoner-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ParadiseYu/TS-Reasoner-7B with Transformers:

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

pipe = pipeline("text-generation", model="ParadiseYu/TS-Reasoner-7B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("ParadiseYu/TS-Reasoner-7B", trust_remote_code=True)
model = AutoModel.from_pretrained("ParadiseYu/TS-Reasoner-7B", trust_remote_code=True, 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ParadiseYu/TS-Reasoner-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ParadiseYu/TS-Reasoner-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ParadiseYu/TS-Reasoner-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ParadiseYu/TS-Reasoner-7B

SGLang

How to use ParadiseYu/TS-Reasoner-7B 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 "ParadiseYu/TS-Reasoner-7B" \
    --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": "ParadiseYu/TS-Reasoner-7B",
		"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 "ParadiseYu/TS-Reasoner-7B" \
        --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": "ParadiseYu/TS-Reasoner-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ParadiseYu/TS-Reasoner-7B with Docker Model Runner:
```
docker model run hf.co/ParadiseYu/TS-Reasoner-7B
```

TS-Reasoner-7B

Commit History

Aug 2

21:01
Adding `safetensors` variant of this model (#1)⁠

ParadiseYu +1: SFconvertbot

856b339

Jul 10

16:36
Fix trust_remote_code loading: use scanner-visible relative imports⁠

ParadiseYu

Verified signature
c1eff94
01:58
Make loading self-contained: auto-build frozen TimesFM backbone⁠

ParadiseYu

Verified signature
942af00

Jan 29

04:21
Update README.md

ParadiseYu

Verified signature
1d884c8
04:20
Upload folder using huggingface_hub

ParadiseYu

Verified signature
808dbb6
03:27
initial commit

ParadiseYu

Verified signature
0b5d438

Commit History

Aug 2

Adding `safetensors` variant of this model (#1)⁠

Jul 10

Fix trust_remote_code loading: use scanner-visible relative imports⁠

Make loading self-contained: auto-build frozen TimesFM backbone⁠

Jan 29

Update README.md

Upload folder using huggingface_hub

initial commit