Instructions to use risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16 with Transformers:

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

pipe = pipeline("text-generation", model="risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16")
model = AutoModelForCausalLM.from_pretrained("risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16

SGLang

How to use risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16 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 "risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16" \
    --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": "risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16",
		"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 "risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16" \
        --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": "risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16 with Docker Model Runner:
```
docker model run hf.co/risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Betawi Language Model: Bokap Betawi-7B

Bokap Betawi is a language model fine-tuned from Qwen 2.5 7B on 1,000 instruction-output pairs synthetic Betawi language dataset generated using GPT-4o. This model is specifically made to enhance the performance of tasks in bahasa Betawi.

Version Alpha

This repository contains the fp16 version, see here for another version:

Version	URL
fp16	Bokap-Betawi-v0-Qwen2.5-7B-fp16
4bit	Bokap-Betawi-v0-Qwen2.5-7B-4bit
GGUF	Bokap-Betawi-v0-Qwen2.5-7B-fp16-Q8_0-GGUF

Description

Developed by: Risang Panggalih
Finetuned from model: Qwen 2.5 7B

Downloads last month: 28

Model tree for risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16

Quantizations

3 models

Dataset used to train risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16

Collection including risangpanggalih/Bokap-Betawi-v0-Qwen2.5-7B-fp16

Lingos

Collection

Large Language Model(s), NLP, etc • 3 items • Updated Apr 30, 2025