Instructions to use Samll/qwen3-8b-thinkprune-b3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Samll/qwen3-8b-thinkprune-b3000 with Transformers:

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

pipe = pipeline("text-generation", model="Samll/qwen3-8b-thinkprune-b3000")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

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

tokenizer = AutoTokenizer.from_pretrained("Samll/qwen3-8b-thinkprune-b3000")
model = AutoModelForCausalLM.from_pretrained("Samll/qwen3-8b-thinkprune-b3000", 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 Samll/qwen3-8b-thinkprune-b3000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Samll/qwen3-8b-thinkprune-b3000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Samll/qwen3-8b-thinkprune-b3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Samll/qwen3-8b-thinkprune-b3000

SGLang

How to use Samll/qwen3-8b-thinkprune-b3000 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 "Samll/qwen3-8b-thinkprune-b3000" \
    --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": "Samll/qwen3-8b-thinkprune-b3000",
		"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 "Samll/qwen3-8b-thinkprune-b3000" \
        --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": "Samll/qwen3-8b-thinkprune-b3000",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Samll/qwen3-8b-thinkprune-b3000 with Docker Model Runner:
```
docker model run hf.co/Samll/qwen3-8b-thinkprune-b3000
```

qwen3-8b-thinkprune-b3000

Paper: Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability

Collection: Efficient Reasoning: CoT Faithfulness & Monitorability

Method: ThinkPrune (Hou et al., 2026)

Hyperparameters


Base model	Qwen/Qwen3-8B
Method	ThinkPrune
Released as	merged full model, step 200
Completion budget L	3,000 tokens
LoRA	rank 16, alpha 32, dropout 0, all attention and MLP projections
Optimiser	AdamW, learning rate 1e-5, KL coefficient 0
Batch	32 prompts x 16 generations
Sampling	temperature 0.8, top-p 0.95
Data	`numina_amc_aime` (PRIME Eurus-2-RL-Data), ~2,223 prompts
Checkpoint selection	AIME22, every 50 steps
Hardware	2x NVIDIA GH200

Budget is 3,000 rather than 4,000 because Qwen3-8B's average base CoT on the training data is already below 4,000 tokens.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "Samll/qwen3-8b-thinkprune-b3000"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")