Instructions to use AdarshSingh7647/Naive-SFT-Table-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AdarshSingh7647/Naive-SFT-Table-8B with Transformers:

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

pipe = pipeline("text-generation", model="AdarshSingh7647/Naive-SFT-Table-8B")
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("AdarshSingh7647/Naive-SFT-Table-8B")
model = AutoModelForCausalLM.from_pretrained("AdarshSingh7647/Naive-SFT-Table-8B", 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 AdarshSingh7647/Naive-SFT-Table-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AdarshSingh7647/Naive-SFT-Table-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AdarshSingh7647/Naive-SFT-Table-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AdarshSingh7647/Naive-SFT-Table-8B

SGLang

How to use AdarshSingh7647/Naive-SFT-Table-8B 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 "AdarshSingh7647/Naive-SFT-Table-8B" \
    --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": "AdarshSingh7647/Naive-SFT-Table-8B",
		"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 "AdarshSingh7647/Naive-SFT-Table-8B" \
        --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": "AdarshSingh7647/Naive-SFT-Table-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AdarshSingh7647/Naive-SFT-Table-8B with Docker Model Runner:
```
docker model run hf.co/AdarshSingh7647/Naive-SFT-Table-8B
```

Naive-SFT-Table-8B

Qwen3-8B for single-call listwise table reranking: given a question and candidate tables, it returns the full ranking in one generation. Trained with Naive-SFT: it outputs the ranking directly, with no reasoning trace. Code · Eklav · Std-SFT

Results

	SQA	TAT-QA	TabFact	NQ-Tables	AITQA	FeTaQA	OTT-QA	OpenWikiTables
Qwen3-8B (base)	69.51	50.51	60.03	72.10	77.04	89.63	81.27	88.68
▶ Naive-SFT	71.41	49.59	62.20	68.88	68.23	91.42	88.25	91.98
Std-SFT	72.05	52.03	66.61	74.73	88.25	88.06	83.20	90.29
Eklav	73.81	54.59	68.90	72.20	85.99	91.95	90.27	93.78

Input / output format

Input — a chat message with the question followed by each candidate table, labeled ### Table 1, ### Table 2, ...:

Question: Which table shows 2022 quarterly revenue by region?

### Table 1
| Region | Q1 2022 | Q2 2022 | Q3 2022 | Q4 2022 |
|---|---|---|---|---|
| North America | 120 | 134 | 128 | 145 |
| Europe | 88 | 91 | 95 | 102 |

### Table 2
| Product | Units Sold | Year |
|---|---|---|
| Widget A | 4200 | 2021 |

### Table 3
| Region | Headcount |
|---|---|
| North America | 340 |

Output — a single JSON object with the ranked, one-indexed candidate positions, best first:

{"ranked_tables": [1, 3, 2]}

Usage with vLLM

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

repo = "AdarshSingh7647/Naive-SFT-Table-8B"
tok = AutoTokenizer.from_pretrained(repo)
llm = LLM(model=repo, dtype="bfloat16", max_model_len=32768)

system = ("You are a table relevance expert. Given a question and a set of candidate tables "
          "rank them from most to least useful for answering the question. Output exactly "
          "JSON with key ranked_tables.")
user = "Question: ...\n\n### Table 1\n...\n\n### Table 2\n...\n"

msgs = [{"role": "system", "content": system}, {"role": "user", "content": user}]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
out = llm.generate([text], SamplingParams(temperature=0.6, top_p=0.95, max_tokens=2048))
print(out[0].outputs[0].text)

Citation

@misc{singh2026tabrank,
      title={TabRank: Chain-of-Thought Distillation for Table Re-Rankers},
      author={Adarsh Singh and Kushal Raj Bhandari and Jianxi Gao and Soham Dan and Vivek Gupta},
      year={2026},
      eprint={2607.25182},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2607.25182}
}