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oobabooga
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#!/usr/bin/env python3
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import requests
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HOST = '0.0.0.0:5000'
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def generate(prompt, tokens=200):
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request = {'prompt': prompt, 'max_new_tokens': tokens}
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response = requests.post(f'http://{HOST}/api/v1/generate', json=request)
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if response.status_code == 200:
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return response.json()['results'][0]['text']
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def model_api(request):
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response = requests.post(f'http://{HOST}/api/v1/model', json=request)
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return response.json()
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# print some common settings
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def print_basic_model_info(response):
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basic_settings = ['truncation_length', 'instruction_template']
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print("Model: ", response['result']['model_name'])
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print("Lora(s): ", response['result']['lora_names'])
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for setting in basic_settings:
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print(setting, "=", response['result']['shared.settings'][setting])
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# model info
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def model_info():
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response = model_api({'action': 'info'})
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print_basic_model_info(response)
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# simple loader
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def model_load(model_name):
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return model_api({'action': 'load', 'model_name': model_name})
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# complex loader
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def complex_model_load(model):
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def guess_groupsize(model_name):
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if '1024g' in model_name:
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return 1024
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elif '128g' in model_name:
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return 128
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elif '32g' in model_name:
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return 32
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else:
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return -1
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req = {
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'action': 'load',
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'model_name': model,
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'args': {
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'loader': 'AutoGPTQ',
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'bf16': False,
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'load_in_8bit': False,
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'groupsize': 0,
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'wbits': 0,
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# llama.cpp
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'threads': 0,
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'n_batch': 512,
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'no_mmap': False,
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'mlock': False,
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'cache_capacity': None,
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'n_gpu_layers': 0,
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'n_ctx': 2048,
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# RWKV
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'rwkv_strategy': None,
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'rwkv_cuda_on': False,
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# b&b 4-bit
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# 'load_in_4bit': False,
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# 'compute_dtype': 'float16',
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# 'quant_type': 'nf4',
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# 'use_double_quant': False,
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# "cpu": false,
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# "auto_devices": false,
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# "gpu_memory": null,
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# "cpu_memory": null,
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# "disk": false,
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# "disk_cache_dir": "cache",
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},
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}
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model = model.lower()
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if '4bit' in model or 'gptq' in model or 'int4' in model:
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req['args']['wbits'] = 4
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req['args']['groupsize'] = guess_groupsize(model)
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elif '3bit' in model:
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req['args']['wbits'] = 3
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req['args']['groupsize'] = guess_groupsize(model)
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else:
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req['args']['gptq_for_llama'] = False
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if '8bit' in model:
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req['args']['load_in_8bit'] = True
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elif '-hf' in model or 'fp16' in model:
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if '7b' in model:
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req['args']['bf16'] = True # for 24GB
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elif '13b' in model:
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req['args']['load_in_8bit'] = True # for 24GB
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elif 'gguf' in model:
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# req['args']['threads'] = 16
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if '7b' in model:
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req['args']['n_gpu_layers'] = 100
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elif '13b' in model:
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req['args']['n_gpu_layers'] = 100
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elif '30b' in model or '33b' in model:
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req['args']['n_gpu_layers'] = 59 # 24GB
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elif '65b' in model:
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req['args']['n_gpu_layers'] = 42 # 24GB
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elif 'rwkv' in model:
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req['args']['rwkv_cuda_on'] = True
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if '14b' in model:
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req['args']['rwkv_strategy'] = 'cuda f16i8' # 24GB
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else:
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req['args']['rwkv_strategy'] = 'cuda f16' # 24GB
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return model_api(req)
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if __name__ == '__main__':
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for model in model_api({'action': 'list'})['result']:
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try:
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resp = complex_model_load(model)
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if 'error' in resp:
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print(f"❌ {model} FAIL Error: {resp['error']['message']}")
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continue
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else:
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print_basic_model_info(resp)
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ans = generate("0,1,1,2,3,5,8,13,", tokens=2)
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if '21' in ans:
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print(f"✅ {model} PASS ({ans})")
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else:
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print(f"❌ {model} FAIL ({ans})")
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except Exception as e:
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print(f"❌ {model} FAIL Exception: {repr(e)}")
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# 0,1,1,2,3,5,8,13, is the fibonacci sequence, the next number is 21.
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# Some results below.
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""" $ ./model-api-example.py
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Model: 4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda
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Lora(s): []
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truncation_length = 2048
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instruction_template = Alpaca
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✅ 4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda PASS (21)
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Model: 4bit_WizardLM-13B-Uncensored-4bit-128g
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Lora(s): []
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truncation_length = 2048
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instruction_template = WizardLM
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✅ 4bit_WizardLM-13B-Uncensored-4bit-128g PASS (21)
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Model: Aeala_VicUnlocked-alpaca-30b-4bit
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Lora(s): []
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truncation_length = 2048
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instruction_template = Alpaca
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✅ Aeala_VicUnlocked-alpaca-30b-4bit PASS (21)
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Model: alpaca-30b-4bit
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Lora(s): []
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truncation_length = 2048
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instruction_template = Alpaca
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✅ alpaca-30b-4bit PASS (21)
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"""
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