Instructions to use 84basi/lora-10-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 84basi/lora-10-1 with Transformers:

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

pipe = pipeline("text-generation", model="84basi/lora-10-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("84basi/lora-10-1")
model = AutoModelForCausalLM.from_pretrained("84basi/lora-10-1", 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 84basi/lora-10-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "84basi/lora-10-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "84basi/lora-10-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/84basi/lora-10-1

SGLang

How to use 84basi/lora-10-1 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 "84basi/lora-10-1" \
    --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": "84basi/lora-10-1",
		"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 "84basi/lora-10-1" \
        --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": "84basi/lora-10-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use 84basi/lora-10-1 with Docker Model Runner:
```
docker model run hf.co/84basi/lora-10-1
```

lora-10-1 / README.md

84basi

Upload full model

c3c69ad verified 5 months ago

preview code

Raw

History Blame Contribute Delete

1.5 kB

	---
	base_model: unsloth/Qwen3-4B-Instruct-2507
	datasets:
	- u-10bei/structured_data_with_cot_dataset_512_v5
	language:
	- en
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- full-finetune
	- structured-output
	---

	qwen3-4b-structured-output-lora

	This repository provides a full fine-tuned model based on
	unsloth/Qwen3-4B-Instruct-2507 using BF16 full fine-tuning + NEFTune.

	This repository contains the complete model weights.

	## Training Objective

	This model is trained to improve structured output accuracy
	(JSON / YAML / XML / TOML / CSV).

	CoT (Chain-of-Thought) is removed from training data during preprocessing.

	## Training Configuration

	- Base model: unsloth/Qwen3-4B-Instruct-2507
	- Method: Full fine-tuning (BF16)
	- Max sequence length: 2048
	- Epochs: 1
	- Learning rate: 2e-05
	- NEFTune noise alpha: 5.0

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer
	import torch

	model_id = "84basi/lora-10-1"

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)
	```

	## Sources & Terms (IMPORTANT)

	Training data: u-10bei/structured_data_with_cot_dataset_512_v5

	Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
	Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.