Instructions to use Blinorot/AL-SSLAM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Blinorot/AL-SSLAM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Blinorot/AL-SSLAM-R") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Blinorot/AL-SSLAM-R", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Blinorot/AL-SSLAM-R with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Blinorot/AL-SSLAM-R" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Blinorot/AL-SSLAM-R", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Blinorot/AL-SSLAM-R
- SGLang
How to use Blinorot/AL-SSLAM-R 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 "Blinorot/AL-SSLAM-R" \ --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": "Blinorot/AL-SSLAM-R", "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 "Blinorot/AL-SSLAM-R" \ --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": "Blinorot/AL-SSLAM-R", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Blinorot/AL-SSLAM-R with Docker Model Runner:
docker model run hf.co/Blinorot/AL-SSLAM-R
File size: 2,339 Bytes
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"architectures": [
"Qwen3AudioWrappedForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"audio_adapter_configs": [
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"adapter_name": "downsampler_conformer",
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"downsampler_depth": 1,
"encoder_embedding_dim": 768,
"encoder_name": "sslam",
"layer_fusion_config": {
"layer_fusion_type": "weighted_average"
},
"llm_embedding_dim": 2560,
"norm_type": "layer",
"pre_average": true,
"use_conformer": false
}
],
"audio_encoder_configs": [
{
"encoder_name": "sslam"
}
],
"audio_fusion_config": {
"fusion_type": "sum"
},
"audio_postprocessing_config": {
"postprocessing_type": "identity"
},
"audio_sep_d_embed": 2560,
"bos_token_id": 151643,
"dtype": "float32",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9728,
"layer_types": [
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"max_position_embeddings": 262144,
"max_window_layers": 36,
"model_type": "qwen3_audio",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 5000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.1",
"use_cache": false,
"use_explicit_audio_tokens": false,
"use_sliding_window": false,
"vocab_size": 151936
}
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