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

Libraries

How to use dnaihao/qwen3-8b-tablellama with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dnaihao/qwen3-8b-tablellama", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dnaihao/qwen3-8b-tablellama with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/dnaihao/qwen3-8b-tablellama

SGLang

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

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

Configuration Parsing Warning:Config file config.json cannot be fetched (too big)

Configuration Parsing Warning:Config file tokenizer_config.json cannot be fetched (too big)

qwen3-8b-tablellama

Replication of TableLlama, trained from Qwen3-8B on the corresponding instruction-tuning corpus.

Released alongside the EACL 2026 Findings paper "What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects" (Deng et al., 2026) as an additional artefact extending the paper's experiments — the main 3 base × 4 training-data grid in the paper covers Mistral-v0.3, OLMo, and Phi-3-small at the 7B scale; this model adds another base-model variant trained on the same corpus.

📄 Paper: aclanthology.org/2026.findings-eacl.195
💻 Code & eval scripts: github.com/dnaihao/table-sft-eacl-2026
🤗 All replicated models: collection

Training


Base model	`Qwen/Qwen3-8B`
Training corpus	`tablellama_train.json` from `dnaihao/Table-Instructs`
Method	Full SFT via LLaMA-Factory
Learning rate	5e-7

Full hyperparameter sweep, ablations, and per-benchmark numbers are reported in the paper.

Evaluation

This model was not part of the per-benchmark evaluation reported in the paper; it is released as an additional artefact for the community. See github.com/dnaihao/table-sft-eacl-2026 for the eval setup we used on the paper's main models — the same scripts can be adapted for this checkpoint.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("dnaihao/qwen3-8b-tablellama")
model = AutoModelForCausalLM.from_pretrained(
    "dnaihao/qwen3-8b-tablellama",
    torch_dtype="auto",
    device_map="auto",
)

License

This model inherits the license of its base model (Qwen/Qwen3-8B: apache-2.0).

Citation

@inproceedings{deng-etal-2026-really,
    title = "What Really Matters for Table {LLM}s? A Meta-Evaluation of Model and Data Effects",
    author = "Deng, Naihao  and Zhang, Sheng  and Zhu, Henghui  and Chang, Shuaichen  and Zhang, Jiani  and Li, Alexander Hanbo  and Hang, Chung-Wei  and Kobayashi, Hideo  and Hu, Yiqun  and Ng, Patrick",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2026",
    year = "2026",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.findings-eacl.195/",
    doi = "10.18653/v1/2026.findings-eacl.195"
}