Instructions to use aiplanet/effi-13b-quant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aiplanet/effi-13b-quant with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aiplanet/effi-13b-quant")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aiplanet/effi-13b-quant") model = AutoModelForCausalLM.from_pretrained("aiplanet/effi-13b-quant") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use aiplanet/effi-13b-quant with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aiplanet/effi-13b-quant" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aiplanet/effi-13b-quant", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aiplanet/effi-13b-quant
- SGLang
How to use aiplanet/effi-13b-quant 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 "aiplanet/effi-13b-quant" \ --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": "aiplanet/effi-13b-quant", "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 "aiplanet/effi-13b-quant" \ --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": "aiplanet/effi-13b-quant", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aiplanet/effi-13b-quant with Docker Model Runner:
docker model run hf.co/aiplanet/effi-13b-quant
Upload config.json
Browse files- config.json +48 -0
config.json
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{
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"_name_or_path": "meta-llama/Llama-2-13b-chat-hf",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 40,
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"pretraining_tp": 1,
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"quantization_config": {
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"batch_size": 1,
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"bits": 4,
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"block_name_to_quantize": "model.layers",
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"damp_percent": 0.1,
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"dataset": "c4",
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"desc_act": false,
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"disable_exllama": false,
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"group_size": 128,
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"max_input_length": null,
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"model_seqlen": 4096,
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"module_name_preceding_first_block": [
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"model.embed_tokens"
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],
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"pad_token_id": null,
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"quant_method": "gptq",
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"sym": true,
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"tokenizer": null,
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"true_sequential": true,
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"use_cuda_fp16": true
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},
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.34.1",
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"use_cache": true,
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"vocab_size": 32000
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}
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