#!/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': { 'gptq_for_llama': False, # Use AutoGPTQ by default, set to True for gptq-for-llama '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) """