Instructions to use tahamajs/Qwen3-4b-gsm8k-Qlora-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tahamajs/Qwen3-4b-gsm8k-Qlora-SFT with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(base_model, "tahamajs/Qwen3-4b-gsm8k-Qlora-SFT")

Transformers

How to use tahamajs/Qwen3-4b-gsm8k-Qlora-SFT with Transformers:

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

pipe = pipeline("text-generation", model="tahamajs/Qwen3-4b-gsm8k-Qlora-SFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tahamajs/Qwen3-4b-gsm8k-Qlora-SFT", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tahamajs/Qwen3-4b-gsm8k-Qlora-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tahamajs/Qwen3-4b-gsm8k-Qlora-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": "tahamajs/Qwen3-4b-gsm8k-Qlora-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tahamajs/Qwen3-4b-gsm8k-Qlora-SFT

SGLang

How to use tahamajs/Qwen3-4b-gsm8k-Qlora-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 "tahamajs/Qwen3-4b-gsm8k-Qlora-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": "tahamajs/Qwen3-4b-gsm8k-Qlora-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 "tahamajs/Qwen3-4b-gsm8k-Qlora-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": "tahamajs/Qwen3-4b-gsm8k-Qlora-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use tahamajs/Qwen3-4b-gsm8k-Qlora-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 tahamajs/Qwen3-4b-gsm8k-Qlora-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 tahamajs/Qwen3-4b-gsm8k-Qlora-SFT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tahamajs/Qwen3-4b-gsm8k-Qlora-SFT to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="tahamajs/Qwen3-4b-gsm8k-Qlora-SFT",
    max_seq_length=2048,
)

Docker Model Runner
How to use tahamajs/Qwen3-4b-gsm8k-Qlora-SFT with Docker Model Runner:
```
docker model run hf.co/tahamajs/Qwen3-4b-gsm8k-Qlora-SFT
```

Model Card for outputs_sft

outputs_sft is a Supervised Fine-Tuning (SFT) LoRA adapter on top of [Qwen/Qwen3-4B]. It was trained with TRL and PEFT.

Quick start

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from peft import AutoPeftModelForCausalLM

REPO_ID = "outputs_sft"  # Replace with your Hub repo if different

# Load base model + merge LoRA on the fly (recommended for inference)
model = AutoPeftModelForCausalLM.from_pretrained(REPO_ID, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(REPO_ID, use_fast=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")

prompt = "If you had a time machine and could go only once, where and when would you go? Explain your reasoning."
out = pipe(prompt, max_new_tokens=256, do_sample=True, top_p=0.9, temperature=0.7)[0]["generated_text"]
print(out)

# Alternatively, if you already merged the LoRA and saved the full model weights:
# model = AutoModelForCausalLM.from_pretrained(REPO_ID, device_map="auto")
# tokenizer = AutoTokenizer.from_pretrained(REPO_ID, use_fast=True)

Intended uses & limitations

Intended uses

General instruction following and helpful assistant style responses.
Short-form reasoning and everyday Q&A.
Creative writing, drafting, and rewriting.

Limitations

Not evaluated for safety-critical or high-stakes domains.
May produce inaccurate, biased, or undesired content.
Long-chain reasoning may require specialized training.

Bias, risks, and limitations: Outputs may reflect biases present in training data. Review before use in production.

Training data

Dataset not auto-detected from the notebook. Please document your data sources.

Training procedure

This model was trained with SFT using TRL/PEFT.

PEFT / LoRA Config

lora_dropout: 0.05
target_modules: ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]

Precision & Quantization

load_in_4bit: True
bnb_4bit_compute_dtype: float32
dtype: float32

Key hyperparameters

num_train_epochs: 2
per_device_train_batch_size: 10
per_device_eval_batch_size: 10
gradient_accumulation_steps: 2
learning_rate: 9e-4
lr_scheduler_type: cosine
logging_steps: 2
save_steps: 8
save_strategy: steps
bf16: True
fp16: False
seed: 42

Hardware & runtime

GPU not detected from notebook logs.

Framework versions

PEFT: 0.17.0
TRL: 0.21.0
Transformers: 4.55.1
PyTorch: 2.8.0
Datasets: 3.6.0
Tokenizers: 0.21.4

Example prompts

Explain diffusion models to a 12-year-old.
Write a polite email asking for an extension on a project.
Summarize the following text in 3 bullet points: ...

Evaluation

No formal evaluation metrics were logged in the notebook. If you run evaluations (e.g., on MT-Bench, MMLU, or a domain-specific set), please add the results here in a Model Index block or a table.

Pushing to the Hub

from huggingface_hub import HfApi, create_repo, upload_folder

REPO_ID = "outputs_sft"  # e.g., "YourUsername/outputs_sft"

# 1) Create the repo (once)
# create_repo(REPO_ID, repo_type="model", private=False)

# 2) Upload your adapter or merged model folder
upload_folder(
    repo_id=REPO_ID,
    folder_path="./outputs_sft",  # change to your output dir
    commit_message="Add SFT model",
)

License: Set license in the YAML header to a license compatible with the base model and your data (e.g., apache-2.0, mit, or the specific Qwen license if required).

Citations

@misc{vonwerra2022trl,
  title        = {{TRL: Transformer Reinforcement Learning}},
  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
  year         = 2020,
  journal      = {GitHub repository},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}

@misc{mukherjee2023peft,
  title        = {PEFT: Parameter-Efficient Fine-Tuning},
  author       = {Edward Hu and others},
  year         = 2023,
  howpublished = {\url{https://github.com/huggingface/peft}}
}

@inproceedings{wolf-etal-2020-transformers,
  title = "Transformers: State-of-the-Art Natural Language Processing",
  author = "Thomas Wolf and others",
  booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
  year = "2020"
}

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