Instructions to use eustlb/GLM-ASR-Nano-2512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use eustlb/GLM-ASR-Nano-2512 with Transformers:

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

pipe = pipeline("text-generation", model="eustlb/GLM-ASR-Nano-2512")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForSeq2SeqLM

processor = AutoProcessor.from_pretrained("eustlb/GLM-ASR-Nano-2512")
model = AutoModelForSeq2SeqLM.from_pretrained("eustlb/GLM-ASR-Nano-2512", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use eustlb/GLM-ASR-Nano-2512 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "eustlb/GLM-ASR-Nano-2512"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "eustlb/GLM-ASR-Nano-2512",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/eustlb/GLM-ASR-Nano-2512

SGLang

How to use eustlb/GLM-ASR-Nano-2512 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 "eustlb/GLM-ASR-Nano-2512" \
    --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": "eustlb/GLM-ASR-Nano-2512",
		"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 "eustlb/GLM-ASR-Nano-2512" \
        --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": "eustlb/GLM-ASR-Nano-2512",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use eustlb/GLM-ASR-Nano-2512 with Docker Model Runner:
```
docker model run hf.co/eustlb/GLM-ASR-Nano-2512
```

GLM-ASR-Nano-2512

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{
  "audio_token": "<|pad|>",
  "default_transcription_prompt": "Please transcribe this audio into text",
  "feature_extractor": {
    "chunk_length": 30,
    "dither": 0.0,
    "feature_extractor_type": "WhisperFeatureExtractor",
    "feature_size": 128,
    "hop_length": 160,
    "n_fft": 400,
    "n_samples": 480000,
    "nb_max_frames": 3000,
    "padding_side": "right",
    "padding_value": 0.0,
    "return_attention_mask": false,
    "sampling_rate": 16000
  },
  "max_audio_len": 600,
  "processor_class": "GlmAsrProcessor"
}