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
```

kangsyahrul commited on 29 days ago

Commit

bec76a0

verified ·

1 Parent(s): 52519d4

Add model card and adapter usage guidance

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README.md +56 -185

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@@ -1,208 +1,79 @@
 ---
-base_model: unsloth/qwen2.5-coder-3b-instruct-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen2.5-coder-3b-instruct-bnb-4bit
 - lora
 - transformers
 - unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-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.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **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
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.1

 ---
+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
+```python
+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](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct/blob/main/LICENSE)
+before redistribution or commercial use.