Text Generation
Safetensors
PyTorch
English
gpt_neox
causal-lm
pythia
autoround
intel-autoround
auto-round
intel
woq
gptq
auto-gptq
autogptq
eleutheraI
8-bit precision
Instructions to use fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym
- SGLang
How to use fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym 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 "fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym" \ --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": "fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym", "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 "fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym" \ --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": "fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym with Docker Model Runner:
docker model run hf.co/fbaldassarri/EleutherAI_pythia-160m-autogptq-int8-gs128-sym
File size: 1,435 Bytes
466b752 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | {
"_name_or_path": "EleutherAI/pythia-160m",
"architectures": [
"GPTNeoXForCausalLM"
],
"attention_bias": true,
"attention_dropout": 0.0,
"bos_token_id": 0,
"classifier_dropout": 0.1,
"eos_token_id": 0,
"hidden_act": "gelu",
"hidden_dropout": 0.0,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"layer_norm_eps": 1e-05,
"max_position_embeddings": 2048,
"model_type": "gpt_neox",
"num_attention_heads": 12,
"num_hidden_layers": 12,
"partial_rotary_factor": 0.25,
"quantization_config": {
"amp": false,
"autoround_version": "0.4.5",
"batch_size": 4,
"bits": 8,
"damp_percent": 0.01,
"data_type": "int",
"desc_act": false,
"enable_minmax_tuning": true,
"enable_norm_bias_tuning": false,
"enable_quanted_input": true,
"gradient_accumulate_steps": 1,
"group_size": 128,
"iters": 200,
"low_gpu_mem_usage": false,
"lr": 0.005,
"minmax_lr": 0.005,
"nsamples": 128,
"quant_method": "gptq",
"scale_dtype": "torch.float16",
"seqlen": 512,
"sym": true,
"to_quant_block_names": null,
"true_sequential": false
},
"rope_scaling": null,
"rope_theta": 10000,
"rotary_emb_base": 10000,
"rotary_pct": 0.25,
"tie_word_embeddings": false,
"torch_dtype": "float32",
"transformers_version": "4.48.2",
"use_cache": true,
"use_parallel_residual": true,
"vocab_size": 50304
}
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