Update app.py
Browse files
app.py
CHANGED
@@ -9,19 +9,23 @@ class Z(object):
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def init(self):
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pass
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def
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prompt0 = txt0
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# for Wizard-Vicuna-13B
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prompt00 = f'''USER: {prompt0}
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ASSISTANT:'''
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prompt00 = f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt0}
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### Response:'''
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response0 = llm(prompt00, max_new_tokens=198, temperature=paramTemp) # 0.5, 0.3
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@@ -29,22 +33,34 @@ ASSISTANT:'''
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from ctransformers import AutoModelForCausalLM
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# wizzard vicuna
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# see https://github.com/melodysdreamj/WizardVicunaLM
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llm = AutoModelForCausalLM.from_pretrained('TheBloke/Wizard-Vicuna-13B-Uncensored-GGML', model_file='Wizard-Vicuna-13B-Uncensored.ggmlv3.q4_0.bin', model_type='llama')
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#llm = AutoModelForCausalLM.from_pretrained('mverrilli/dolly-v2-12b-ggml', model_file='ggml-model-q5_0.bin', model_type='dolly-v2')
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z = Z()
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z.llm = llm
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z.init()
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def
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global z
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return z.
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iface = gr.Interface(fn=
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iface.launch()
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def init(self):
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pass
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def run0(self, txt0, paramTemp):
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prompt0 = txt0
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# for Wizard-Vicuna-13B
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prompt00 = f'''USER: {prompt0}
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ASSISTANT:'''
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# for TheBloke/Wizard-Vicuna-13B-Uncensored-GGML
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prompt00 = f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt0}
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### Response:'''
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# raw
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prompt00 = prompt0
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response0 = llm(prompt00, max_new_tokens=198, temperature=paramTemp) # 0.5, 0.3
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from ctransformers import AutoModelForCausalLM
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# experiment
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#llm = AutoModelForCausalLM.from_pretrained('mverrilli/dolly-v2-12b-ggml', model_file='ggml-model-q5_0.bin', model_type='dolly-v2')
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# experiment
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#llm = AutoModelForCausalLM.from_pretrained('mverrilli/dolly-v2-7b-ggml', model_file='ggml-model-q5_0.bin', model_type='dolly-v2')
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# wizzard vicuna
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# see https://github.com/melodysdreamj/WizardVicunaLM
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llm = AutoModelForCausalLM.from_pretrained('TheBloke/Wizard-Vicuna-13B-Uncensored-GGML', model_file='Wizard-Vicuna-13B-Uncensored.ggmlv3.q4_0.bin', model_type='llama')
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modelInfo = {'path2':'TheBloke/hippogriff-30b-chat-GGML:hippogriff-30b.ggmlv3.q4_1.bin', 'promptType':'raw', 'modelType':'llama'}
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print('[D] load LMt...')
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self.lm0 = AutoModelForCausalLM.from_pretrained(modelInfo['path2'].split(':')[0], model_file=modelInfo['path2'].split(':')[1], model_type=modelInfo['modelType'])
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print('[D] ...done')
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z = Z()
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z.llm = llm
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z.init()
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def run0(prompt, temperature):
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global z
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return z.run0(prompt, temperature)
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iface = gr.Interface(fn=run0, inputs=["text", gr.Slider(0.0, 1.0, value=0.41)], outputs="text")
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iface.launch()
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