Instructions to use mashriram/Qwen3-4B-Instruct-TableLLM-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mashriram/Qwen3-4B-Instruct-TableLLM-SFT with Transformers:

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

pipe = pipeline("text-generation", model="mashriram/Qwen3-4B-Instruct-TableLLM-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mashriram/Qwen3-4B-Instruct-TableLLM-SFT")
model = AutoModelForCausalLM.from_pretrained("mashriram/Qwen3-4B-Instruct-TableLLM-SFT", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mashriram/Qwen3-4B-Instruct-TableLLM-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mashriram/Qwen3-4B-Instruct-TableLLM-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mashriram/Qwen3-4B-Instruct-TableLLM-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mashriram/Qwen3-4B-Instruct-TableLLM-SFT

SGLang

How to use mashriram/Qwen3-4B-Instruct-TableLLM-SFT 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 "mashriram/Qwen3-4B-Instruct-TableLLM-SFT" \
    --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": "mashriram/Qwen3-4B-Instruct-TableLLM-SFT",
		"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 "mashriram/Qwen3-4B-Instruct-TableLLM-SFT" \
        --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": "mashriram/Qwen3-4B-Instruct-TableLLM-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use mashriram/Qwen3-4B-Instruct-TableLLM-SFT with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mashriram/Qwen3-4B-Instruct-TableLLM-SFT to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mashriram/Qwen3-4B-Instruct-TableLLM-SFT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mashriram/Qwen3-4B-Instruct-TableLLM-SFT to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="mashriram/Qwen3-4B-Instruct-TableLLM-SFT",
    max_seq_length=2048,
)

Docker Model Runner
How to use mashriram/Qwen3-4B-Instruct-TableLLM-SFT with Docker Model Runner:
```
docker model run hf.co/mashriram/Qwen3-4B-Instruct-TableLLM-SFT
```

mashriram commited on Oct 11, 2025

Commit

10e4b71

verified ·

1 Parent(s): 4b1dcb1

Upload model trained with Unsloth

Browse files

Upload model trained with Unsloth 2x faster

Files changed (2) hide show

adapter_model.safetensors +2 -2
generation_config.json +14 -0

adapter_model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e9a88a2af57d9c5b7f9bb87be2ec1ea14d9c1b9153e121306ff8cfe47d78ab46
-size 264308896

 version https://git-lfs.github.com/spec/v1
+oid sha256:ca6a726f66cbce3e4c692db4d31f94044aa0ea271d2bd80322324e0f313ad7b6
+size 243861720

generation_config.json ADDED Viewed

	@@ -0,0 +1,14 @@

+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_length": 262144,
+  "pad_token_id": 151654,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.56.2"
+}