Instructions to use willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90 with Transformers:

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

pipe = pipeline("text-generation", model="willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90")
model = AutoModelForCausalLM.from_pretrained("willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90", 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 willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90

SGLang

How to use willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90 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 "willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90" \
    --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": "willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90",
		"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 "willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90" \
        --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": "willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90 with Docker Model Runner:
```
docker model run hf.co/willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90
```

Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90

Entropy-aware On-Policy Distillation (EOPD) checkpoint, iteration 90, built on top of Qwen3-8B-Base. Trained with the slime RL framework.

This is a straight conversion of the Megatron torch_dist checkpoint iter_0000090 to HuggingFace safetensors format (BF16). The tokenizer and config are inherited from Qwen3-8B-Base.

Architecture: Qwen3ForCausalLM (36 layers, hidden 4096, 32 heads / 8 KV heads, vocab 151936)
Precision: bfloat16
Parameters: ~8.19B

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

Downloads last month: 29

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for willhx/Qwen3-8B-Base-Math-SeaSFT-Search-EOPD-Tau-iter90

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