Instructions to use kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-Coder-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora")

Transformers

How to use kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora with Transformers:

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

pipe = pipeline("text-generation", model="kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora

SGLang

How to use kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-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 "kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora" \
    --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": "kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora",
		"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 "kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora" \
        --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": "kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora with Docker Model Runner:
```
docker model run hf.co/kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora
```

dendriva-qwen2.5-coder-3b-instruct-lora / README.md

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metadata

base_model: Qwen/Qwen2.5-Coder-3B-Instruct
base_model_relation: adapter
library_name: peft
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE
language:
  - en
  - id
pipeline_tag: text-generation
tags:
  - lora
  - peft
  - transformers
  - unsloth
  - qwen
  - qwen-coder

Dendriva Qwen2.5-Coder 3B Instruct — LoRA

PEFT LoRA adapter trained from Qwen/Qwen2.5-Coder-3B-Instruct. This is the lightweight, trainable-format export for Unsloth or Transformers. It requires the base model at load time.

The ready-to-run LM Studio quantization is available in kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-gguf.

Training provenance

Selected checkpoint: checkpoint-69
Epochs: 3
Steps: 69
Context length: 32,768
LoRA rank / alpha / dropout: 16 / 16 / 0
Learning rate: 2e-4
Batch size: 2
Optimizer: AdamW 8-bit
Warmup steps: 3
Training tokens reported by Unsloth Studio: 8,927,658

The repository intentionally excludes optimizer, scheduler, RNG, and trainer state because those are not required for local inference.

Load with Transformers and PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen2.5-Coder-3B-Instruct"
adapter_id = "kangsyahrul/dendriva-qwen2.5-coder-3b-instruct-lora"

tokenizer = AutoTokenizer.from_pretrained(adapter_id)
base_model = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype="auto",
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, adapter_id)

In Unsloth Desktop, use the Hugging Face model source and enter the full adapter repository ID. Authenticate with a Hugging Face token because the repository is private.

Evaluation status

The adapter files and tokenizer were verified after upload. The training run completed successfully, but no comprehensive held-out coding or Manim benchmark is published with this repository. Compile, render, and test generated code before use.

License

This derivative follows the Qwen Research License of the base model. Review the base-model license before redistribution or commercial use.