Instructions to use torchao-dev/opt-125m-float8dq-row-0.13-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="torchao-dev/opt-125m-float8dq-row-0.13-dev")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("torchao-dev/opt-125m-float8dq-row-0.13-dev") model = AutoModelForCausalLM.from_pretrained("torchao-dev/opt-125m-float8dq-row-0.13-dev") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "torchao-dev/opt-125m-float8dq-row-0.13-dev" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "torchao-dev/opt-125m-float8dq-row-0.13-dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/torchao-dev/opt-125m-float8dq-row-0.13-dev
- SGLang
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev 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 "torchao-dev/opt-125m-float8dq-row-0.13-dev" \ --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": "torchao-dev/opt-125m-float8dq-row-0.13-dev", "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 "torchao-dev/opt-125m-float8dq-row-0.13-dev" \ --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": "torchao-dev/opt-125m-float8dq-row-0.13-dev", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use torchao-dev/opt-125m-float8dq-row-0.13-dev with Docker Model Runner:
docker model run hf.co/torchao-dev/opt-125m-float8dq-row-0.13-dev
File size: 2,071 Bytes
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"_remove_final_layer_norm": false,
"activation_dropout": 0.0,
"activation_function": "relu",
"architectures": [
"OPTForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 2,
"do_layer_norm_before": true,
"dropout": 0.1,
"enable_bias": true,
"eos_token_id": 2,
"ffn_dim": 3072,
"hidden_size": 768,
"init_std": 0.02,
"layer_norm_elementwise_affine": true,
"layerdrop": 0.0,
"max_position_embeddings": 2048,
"model_type": "opt",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pad_token_id": 1,
"prefix": "</s>",
"quantization_config": {
"include_input_output_embeddings": false,
"modules_to_not_convert": null,
"quant_method": "torchao",
"quant_type": {
"default": {
"_data": {
"activation_dtype": {
"_data": "float8_e4m3fn",
"_type": "torch.dtype"
},
"activation_value_lb": null,
"activation_value_ub": null,
"granularity": [
{
"_data": {},
"_type": "PerRow",
"_version": 1
},
{
"_data": {},
"_type": "PerRow",
"_version": 1
}
],
"kernel_preference": {
"_data": "AUTO",
"_type": "KernelPreference"
},
"mm_config": {
"_data": {
"emulate": false,
"pad_inner_dim": false,
"use_fast_accum": true
},
"_type": "Float8MMConfig",
"_version": 1
},
"set_inductor_config": true,
"weight_dtype": {
"_data": "float8_e4m3fn",
"_type": "torch.dtype"
}
},
"_type": "Float8DynamicActivationFloat8WeightConfig",
"_version": 1
}
},
"quant_type_kwargs": {},
"untie_embedding_weights": false
},
"torch_dtype": "bfloat16",
"transformers_version": "4.55.2",
"use_cache": true,
"vocab_size": 50272,
"word_embed_proj_dim": 768
}
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