Instructions to use ShinjiCodeEVA/qwen3-4b-base-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ShinjiCodeEVA/qwen3-4b-base-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-4B-Base")
model = PeftModel.from_pretrained(base_model, "ShinjiCodeEVA/qwen3-4b-base-lora")

Transformers

How to use ShinjiCodeEVA/qwen3-4b-base-lora with Transformers:

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

pipe = pipeline("text-generation", model="ShinjiCodeEVA/qwen3-4b-base-lora")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ShinjiCodeEVA/qwen3-4b-base-lora")
model = AutoModelForCausalLM.from_pretrained("ShinjiCodeEVA/qwen3-4b-base-lora")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ShinjiCodeEVA/qwen3-4b-base-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ShinjiCodeEVA/qwen3-4b-base-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ShinjiCodeEVA/qwen3-4b-base-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ShinjiCodeEVA/qwen3-4b-base-lora

SGLang

How to use ShinjiCodeEVA/qwen3-4b-base-lora 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 "ShinjiCodeEVA/qwen3-4b-base-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ShinjiCodeEVA/qwen3-4b-base-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ShinjiCodeEVA/qwen3-4b-base-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ShinjiCodeEVA/qwen3-4b-base-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use ShinjiCodeEVA/qwen3-4b-base-lora 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 ShinjiCodeEVA/qwen3-4b-base-lora 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 ShinjiCodeEVA/qwen3-4b-base-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ShinjiCodeEVA/qwen3-4b-base-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ShinjiCodeEVA/qwen3-4b-base-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use ShinjiCodeEVA/qwen3-4b-base-lora with Docker Model Runner:
```
docker model run hf.co/ShinjiCodeEVA/qwen3-4b-base-lora
```

Model Card for Model ID

Model Details

Model Description

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Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

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Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

This repository contains a PEFT LoRA adapter for unsloth/Qwen3-4B-Base, not a fully merged base model.

Load the adapter

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "unsloth/Qwen3-4B-Base"
adapter_id = "YOUR_USERNAME/YOUR_REPO_NAME"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id)
model = PeftModel.from_pretrained(base_model, adapter_id)

Upload to Hugging Face

From the project root:

python -m pip install -U "huggingface_hub[cli]"
export HF_TOKEN=hf_xxx
export HF_REPO_ID=your-username/your-repo-name
bash upload_to_hf.sh

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

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Metrics

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Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Model Card Contact

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Framework versions

PEFT 0.18.1

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Paper for ShinjiCodeEVA/qwen3-4b-base-lora

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 49