Instructions to use KhanCold/llama3-8b-spader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KhanCold/llama3-8b-spader with Transformers:

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

pipe = pipeline("text-generation", model="KhanCold/llama3-8b-spader")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KhanCold/llama3-8b-spader")
model = AutoModelForCausalLM.from_pretrained("KhanCold/llama3-8b-spader", 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 KhanCold/llama3-8b-spader with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KhanCold/llama3-8b-spader"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KhanCold/llama3-8b-spader",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KhanCold/llama3-8b-spader

SGLang

How to use KhanCold/llama3-8b-spader 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 "KhanCold/llama3-8b-spader" \
    --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": "KhanCold/llama3-8b-spader",
		"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 "KhanCold/llama3-8b-spader" \
        --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": "KhanCold/llama3-8b-spader",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KhanCold/llama3-8b-spader with Docker Model Runner:
```
docker model run hf.co/KhanCold/llama3-8b-spader
```

SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering

This repository contains the fine-tuned Llama-3.1-8B model checkpoint developed using the SPADER reinforcement learning framework, as presented in the paper SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering.

Model Description

SPADER is a reinforcement learning framework designed for long-horizon tool-use agents in Multi-Answer QA. It introduces:

Step-wise Peer Advantage (SPA): A critic-free step-level credit assignment mechanism that aligns parallel trajectories by decision step and estimates advantages from peer returns.
Diversity-Aware Exploration Reward: Promotes long-tail entity discovery by upweighting rare findings and downweighting redundant ones.

This checkpoint represents the Llama-3.1-8B-Instruct base model trained with SPADER.

Repository: KhanCold/spader
Paper: arXiv:2606.00593

Citation

@misc{shi2026spaderstepwisepeeradvantage,
      title={SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering}, 
      author={Qiming Shi and Zhaolu Kang and Yunfan Zhou and Di Weng and Yingcai Wu},
      year={2026},
      eprint={2606.00593},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.00593}, 
}