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