Instructions to use boradorish/baseline-qwen3-4b-grounded_table with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use boradorish/baseline-qwen3-4b-grounded_table with Transformers:

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

pipe = pipeline("text-generation", model="boradorish/baseline-qwen3-4b-grounded_table")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("boradorish/baseline-qwen3-4b-grounded_table")
model = AutoModelForCausalLM.from_pretrained("boradorish/baseline-qwen3-4b-grounded_table")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use boradorish/baseline-qwen3-4b-grounded_table with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "boradorish/baseline-qwen3-4b-grounded_table"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "boradorish/baseline-qwen3-4b-grounded_table",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/boradorish/baseline-qwen3-4b-grounded_table

SGLang

How to use boradorish/baseline-qwen3-4b-grounded_table 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 "boradorish/baseline-qwen3-4b-grounded_table" \
    --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": "boradorish/baseline-qwen3-4b-grounded_table",
		"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 "boradorish/baseline-qwen3-4b-grounded_table" \
        --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": "boradorish/baseline-qwen3-4b-grounded_table",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use boradorish/baseline-qwen3-4b-grounded_table with Docker Model Runner:
```
docker model run hf.co/boradorish/baseline-qwen3-4b-grounded_table
```

sft_grounded_table

This model is a fine-tuned version of Qwen/Qwen3-4B on the sunny_reasoning dataset. It achieves the following results on the evaluation set:

Loss: 0.0085

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 4e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 32
total_train_batch_size: 64
total_eval_batch_size: 2
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.007	0.3397	92	0.0101
0.0078	0.6794	184	0.0117
0.0027	1.0185	276	0.0084
0.0084	1.3581	368	0.0087
0.0061	1.6978	460	0.0078
0.0037	2.0369	552	0.0077
0.0027	2.3766	644	0.0085
0.0041	2.7163	736	0.0085

Framework versions

Transformers 4.56.2
Pytorch 2.11.0+cu128
Datasets 3.0.0
Tokenizers 0.22.2

Downloads last month: 103

Safetensors

Model size

4B params

Tensor type

F32

Model tree for boradorish/baseline-qwen3-4b-grounded_table

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Finetuned

(690)

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