Instructions to use KasuleTrevor/Qwen2-Luganda-ASR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KasuleTrevor/Qwen2-Luganda-ASR with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-Audio-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "KasuleTrevor/Qwen2-Luganda-ASR")

Transformers

How to use KasuleTrevor/Qwen2-Luganda-ASR with Transformers:

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

pipe = pipeline("text-generation", model="KasuleTrevor/Qwen2-Luganda-ASR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForSeq2SeqLM

processor = AutoProcessor.from_pretrained("KasuleTrevor/Qwen2-Luganda-ASR")
model = AutoModelForSeq2SeqLM.from_pretrained("KasuleTrevor/Qwen2-Luganda-ASR")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KasuleTrevor/Qwen2-Luganda-ASR with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KasuleTrevor/Qwen2-Luganda-ASR"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KasuleTrevor/Qwen2-Luganda-ASR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KasuleTrevor/Qwen2-Luganda-ASR

SGLang

How to use KasuleTrevor/Qwen2-Luganda-ASR 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 "KasuleTrevor/Qwen2-Luganda-ASR" \
    --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": "KasuleTrevor/Qwen2-Luganda-ASR",
		"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 "KasuleTrevor/Qwen2-Luganda-ASR" \
        --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": "KasuleTrevor/Qwen2-Luganda-ASR",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use KasuleTrevor/Qwen2-Luganda-ASR with Docker Model Runner:
```
docker model run hf.co/KasuleTrevor/Qwen2-Luganda-ASR
```

Qwen2-Luganda-ASR

This model is a fine-tuned version of Qwen/Qwen2-Audio-7B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.0822

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
3.599	1.1972	85	1.3491
1.1634	2.3944	170	1.0822

Framework versions

PEFT 0.17.1
Transformers 4.49.0
Pytorch 2.8.0+cu128
Datasets 4.4.1
Tokenizers 0.21.4

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

F32

BF16

F16

Model tree for KasuleTrevor/Qwen2-Luganda-ASR

Base model

Qwen/Qwen2-Audio-7B-Instruct

Adapter

(13)

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