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Runtime error
DongfuJiang
commited on
Commit
•
62174a3
1
Parent(s):
7d90e18
update
Browse files
app.py
CHANGED
@@ -6,7 +6,14 @@ from typing import List
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MAX_BASE_LLM_NUM = 20
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MIN_BASE_LLM_NUM = 3
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-
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LLM-Blender is an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.
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"""
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EXAMPLES_DATASET = load_dataset("llm-blender/mix-instruct", split='validation', streaming=True)
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@@ -21,7 +28,6 @@ for example in SHUFFLED_EXAMPLES_DATASET.take(100):
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CANDIDATE_EXAMPLES[example['instruction']+example['input']] = example['candidates']
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# Download ranker checkpoint
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os.system("ls -l /home/user/.local/lib/python3.10/site-packages/llm_blender")
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if not os.path.exists("pairranker-deberta-v3-large.zip"):
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os.system("gdown https://drive.google.com/uc?id=1EpvFu_qYY0MaIu0BAAhK-sYKHVWtccWg")
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if not os.path.exists("pairranker-deberta-v3-large"):
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@@ -35,13 +41,13 @@ ranker_config.ranker_type = "pairranker"
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ranker_config.model_type = "deberta"
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ranker_config.model_name = "microsoft/deberta-v3-large" # ranker backbone
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ranker_config.load_checkpoint = "./pairranker-deberta-v3-large" # ranker checkpoint <your checkpoint path>
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ranker_config.source_maxlength =
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ranker_config.candidate_maxlength =
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ranker_config.n_tasks = 1 # number of singal that has been used to train the ranker. This checkpoint is trained using BARTScore only, thus being 1.
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fuser_config = llm_blender.GenFuserConfig()
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fuser_config.model_name = "llm-blender/gen_fuser_3b" # our pre-trained fuser
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fuser_config.max_length = 1024
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fuser_config.candidate_maxlength =
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blender_config = llm_blender.BlenderConfig()
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blender_config.device = "cpu" # blender ranker and fuser device
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blender = llm_blender.Blender(blender_config, ranker_config, fuser_config)
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@@ -74,7 +80,7 @@ def update_base_llm_dropdown_along_examples(dummy_text):
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ex_llm_outputs = {f"LLM-{i+1}": candidates[i]['text'] for i in range(len(candidates))}
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return ex_llm_outputs
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def check_save_ranker_inputs(inst, input, llm_outputs):
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if not inst and not input:
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raise gr.Error("Please enter instruction or input context")
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@@ -87,23 +93,29 @@ def check_save_ranker_inputs(inst, input, llm_outputs):
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"candidates": list(llm_outputs.values()),
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}
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def check_fuser_inputs(blender_state,
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pass
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def llms_rank(inst, input, llm_outputs):
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candidates = list(llm_outputs.values())
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def display_ranks(ranks):
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return ", ".join([f"LLM-{i+1}: {rank}" for i, rank in enumerate(ranks)])
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def llms_fuse(blender_state,
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inst = blender_state['inst']
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input = blender_state['input']
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candidates = blender_state['candidates']
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top_k_candidates = get_topk_candidates_from_ranks([ranks], [candidates], top_k=top_k_for_fuser)[0]
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def display_fuser_output(fuser_output):
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return fuser_output
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@@ -111,16 +123,18 @@ def display_fuser_output(fuser_output):
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with gr.Blocks(theme='ParityError/Anime') as demo:
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gr.Markdown(DESCRIPTIONS)
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with gr.Row():
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with gr.Column():
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inst_textbox = gr.Textbox(lines=1, label="Instruction", placeholder="Enter instruction here", show_label=True)
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input_textbox = gr.Textbox(lines=4, label="Input Context", placeholder="Enter input context here", show_label=True)
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with gr.Column():
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saved_llm_outputs = gr.State(value={})
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with gr.Row():
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base_llm_outputs_save_button = gr.Button('Save', variant='primary')
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@@ -136,28 +150,67 @@ with gr.Blocks(theme='ParityError/Anime') as demo:
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)
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blender_state = gr.State(value={})
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with gr.
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fuser_outputs = gr.Textbox(lines=4, label="Fusing outputs", placeholder="Fusing outputs", show_label=True)
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with gr.Row():
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rank_button = gr.Button('Rank LLM Outputs', variant='primary'
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with gr.Accordion(label='Advanced options', open=False):
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-
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top_k_for_fuser = gr.Slider(
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label='Top
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minimum=1,
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maximum=3,
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step=1,
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value=
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)
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examples_dummy_textbox = gr.Textbox(lines=1, label="", placeholder="", show_label=False, visible=False)
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@@ -211,30 +264,22 @@ with gr.Blocks(theme='ParityError/Anime') as demo:
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rank_button.click(
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fn=check_save_ranker_inputs,
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inputs=[inst_textbox, input_textbox, saved_llm_outputs],
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outputs=blender_state,
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).success(
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fn=llms_rank,
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inputs=[inst_textbox, input_textbox, saved_llm_outputs],
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outputs=[saved_rank_outputs],
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).then(
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fn=display_ranks,
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inputs=[saved_rank_outputs],
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outputs=rank_outputs,
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)
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fuse_button.click(
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fn=check_fuser_inputs,
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inputs=[blender_state,
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outputs=[],
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).success(
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fn=llms_fuse,
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inputs=[blender_state,
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outputs=[saved_fuse_outputs],
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).then(
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fn=display_fuser_output,
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inputs=[saved_fuse_outputs],
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outputs=fuser_outputs,
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)
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clear_button.click(
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@@ -243,6 +288,38 @@ with gr.Blocks(theme='ParityError/Anime') as demo:
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outputs=[rank_outputs, fuser_outputs, blender_state, saved_rank_outputs],
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)
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MAX_BASE_LLM_NUM = 20
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MIN_BASE_LLM_NUM = 3
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SOURCE_MAX_LENGTH = 256
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DEFAULT_SOURCE_MAX_LENGTH = 128
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CANDIDATE_MAX_LENGTH = 256
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DEFAULT_CANDIDATE_MAX_LENGTH = 128
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FUSER_MAX_NEW_TOKENS = 512
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DEFAULT_FUSER_MAX_NEW_TOKENS = 256
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DESCRIPTIONS = """# LLM-BLENDER
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LLM-Blender is an innovative ensembling framework to attain consistently superior performance by leveraging the diverse strengths of multiple open-source large language models (LLMs). LLM-Blender cut the weaknesses through ranking and integrate the strengths through fusing generation to enhance the capability of LLMs.
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"""
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EXAMPLES_DATASET = load_dataset("llm-blender/mix-instruct", split='validation', streaming=True)
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CANDIDATE_EXAMPLES[example['instruction']+example['input']] = example['candidates']
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# Download ranker checkpoint
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if not os.path.exists("pairranker-deberta-v3-large.zip"):
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os.system("gdown https://drive.google.com/uc?id=1EpvFu_qYY0MaIu0BAAhK-sYKHVWtccWg")
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if not os.path.exists("pairranker-deberta-v3-large"):
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ranker_config.model_type = "deberta"
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ranker_config.model_name = "microsoft/deberta-v3-large" # ranker backbone
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ranker_config.load_checkpoint = "./pairranker-deberta-v3-large" # ranker checkpoint <your checkpoint path>
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ranker_config.source_maxlength = DEFAULT_SOURCE_MAX_LENGTH
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ranker_config.candidate_maxlength = DEFAULT_CANDIDATE_MAX_LENGTH
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ranker_config.n_tasks = 1 # number of singal that has been used to train the ranker. This checkpoint is trained using BARTScore only, thus being 1.
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fuser_config = llm_blender.GenFuserConfig()
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fuser_config.model_name = "llm-blender/gen_fuser_3b" # our pre-trained fuser
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fuser_config.max_length = 1024
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fuser_config.candidate_maxlength = DEFAULT_CANDIDATE_MAX_LENGTH
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blender_config = llm_blender.BlenderConfig()
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blender_config.device = "cpu" # blender ranker and fuser device
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blender = llm_blender.Blender(blender_config, ranker_config, fuser_config)
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ex_llm_outputs = {f"LLM-{i+1}": candidates[i]['text'] for i in range(len(candidates))}
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return ex_llm_outputs
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def check_save_ranker_inputs(inst, input, llm_outputs, blender_config):
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if not inst and not input:
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raise gr.Error("Please enter instruction or input context")
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"candidates": list(llm_outputs.values()),
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}
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def check_fuser_inputs(blender_state, blender_config, ranks):
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pass
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def llms_rank(inst, input, llm_outputs, blender_config):
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candidates = list(llm_outputs.values())
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rank_params = {
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"source_max_length": blender_config['source_max_length'],
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"candidate_max_length": blender_config['candidate_max_length'],
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}
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ranks = blender.rank(instructions=[inst], inputs=[input], candidates=[candidates])[0]
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return [ranks, ", ".join([f"LLM-{i+1}: {rank}" for i, rank in enumerate(ranks)])]
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def llms_fuse(blender_state, blender_config, ranks):
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inst = blender_state['inst']
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input = blender_state['input']
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candidates = blender_state['candidates']
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top_k_for_fuser = blender_config['top_k_for_fuser']
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fuse_params = blender_config.copy()
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del fuse_params["top_k_for_fuser"]
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top_k_candidates = get_topk_candidates_from_ranks([ranks], [candidates], top_k=top_k_for_fuser)[0]
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fuser_outputs = blender.fuse(instructions=[inst], inputs=[input], candidates=[top_k_candidates], **fuse_params)[0]
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return [fuser_outputs, fuser_outputs]
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def display_fuser_output(fuser_output):
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return fuser_output
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with gr.Blocks(theme='ParityError/Anime') as demo:
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gr.Markdown(DESCRIPTIONS)
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gr.Markdown("## Input and Base LLMs")
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with gr.Row():
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with gr.Column():
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inst_textbox = gr.Textbox(lines=1, label="Instruction", placeholder="Enter instruction here", show_label=True)
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input_textbox = gr.Textbox(lines=4, label="Input Context", placeholder="Enter input context here", show_label=True)
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with gr.Column():
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saved_llm_outputs = gr.State(value={})
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with gr.Group():
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selected_base_llm_name_dropdown = gr.Dropdown(label="Base LLM",
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choices=[f"LLM-{i+1}" for i in range(MIN_BASE_LLM_NUM)], value="LLM-1", show_label=True)
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selected_base_llm_output = gr.Textbox(lines=4, label="LLM-1 (Click Save to save current content)",
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placeholder="Enter LLM-1 output here", show_label=True)
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with gr.Row():
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base_llm_outputs_save_button = gr.Button('Save', variant='primary')
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)
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blender_state = gr.State(value={})
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saved_rank_outputs = gr.State(value=[])
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saved_fuse_outputs = gr.State(value=[])
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gr.Markdown("## Blender Outputs")
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with gr.Group():
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rank_outputs = gr.Textbox(lines=1, label="Ranking outputs", placeholder="Ranking outputs", show_label=True)
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fuser_outputs = gr.Textbox(lines=4, label="Fusing outputs", placeholder="Fusing outputs", show_label=True)
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with gr.Row():
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rank_button = gr.Button('Rank LLM Outputs', variant='primary')
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fuse_button = gr.Button('Fuse Top-K ranked outputs', variant='primary')
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clear_button = gr.Button('Clear Blender Outputs', variant='primary')
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blender_config = gr.State(value={
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"source_max_length": DEFAULT_SOURCE_MAX_LENGTH,
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"candidate_max_length": DEFAULT_CANDIDATE_MAX_LENGTH,
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"top_k_for_fuser": 3,
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"max_new_tokens": DEFAULT_FUSER_MAX_NEW_TOKENS,
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"temperature": 0.7,
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"top_p": 1.0,
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})
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with gr.Accordion(label='Advanced options', open=False):
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source_max_length = gr.Slider(
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label='Max length of Instruction + Input',
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minimum=1,
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maximum=SOURCE_MAX_LENGTH,
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step=1,
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value=DEFAULT_SOURCE_MAX_LENGTH,
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)
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candidate_max_length = gr.Slider(
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label='Max length of LLM-Output Candidate',
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minimum=1,
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maximum=CANDIDATE_MAX_LENGTH,
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step=1,
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value=DEFAULT_CANDIDATE_MAX_LENGTH,
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)
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top_k_for_fuser = gr.Slider(
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label='Top-k ranked candidates to fuse',
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minimum=1,
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maximum=3,
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step=1,
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value=3,
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)
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max_new_tokens = gr.Slider(
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label='Max new tokens fuser can generate',
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minimum=1,
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maximum=FUSER_MAX_NEW_TOKENS,
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step=1,
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value=DEFAULT_FUSER_MAX_NEW_TOKENS,
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)
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temperature = gr.Slider(
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label='Temperature of fuser generation',
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minimum=0.1,
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maximum=2.0,
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step=0.1,
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value=0.7,
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)
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top_p = gr.Slider(
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label='Top-p of fuser generation',
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minimum=0.05,
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maximum=1.0,
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step=0.05,
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value=1.0,
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)
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examples_dummy_textbox = gr.Textbox(lines=1, label="", placeholder="", show_label=False, visible=False)
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rank_button.click(
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fn=check_save_ranker_inputs,
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inputs=[inst_textbox, input_textbox, saved_llm_outputs, blender_config],
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outputs=blender_state,
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).success(
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fn=llms_rank,
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inputs=[inst_textbox, input_textbox, saved_llm_outputs, blender_config],
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outputs=[saved_rank_outputs, rank_outputs],
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)
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fuse_button.click(
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fn=check_fuser_inputs,
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inputs=[blender_state, blender_config, saved_rank_outputs],
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outputs=[],
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).success(
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fn=llms_fuse,
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inputs=[blender_state, blender_config, saved_rank_outputs],
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outputs=[saved_fuse_outputs, fuser_outputs],
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)
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clear_button.click(
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outputs=[rank_outputs, fuser_outputs, blender_state, saved_rank_outputs],
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)
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# update blender config
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source_max_length.change(
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fn=lambda x, y: y.update({"source_max_length": x}) or y,
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inputs=[source_max_length, blender_config],
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outputs=blender_config,
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)
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candidate_max_length.change(
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fn=lambda x, y: y.update({"candidate_max_length": x}) or y,
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inputs=[candidate_max_length, blender_config],
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outputs=blender_config,
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)
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top_k_for_fuser.change(
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fn=lambda x, y: y.update({"top_k_for_fuser": x}) or y,
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inputs=[top_k_for_fuser, blender_config],
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outputs=blender_config,
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)
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max_new_tokens.change(
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fn=lambda x, y: y.update({"max_new_tokens": x}) or y,
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inputs=[max_new_tokens, blender_config],
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outputs=blender_config,
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)
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temperature.change(
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fn=lambda x, y: y.update({"temperature": x}) or y,
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inputs=[temperature, blender_config],
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outputs=blender_config,
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)
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top_p.change(
|
318 |
+
fn=lambda x, y: y.update({"top_p": x}) or y,
|
319 |
+
inputs=[top_p, blender_config],
|
320 |
+
outputs=blender_config,
|
321 |
+
)
|
322 |
+
|
323 |
|
324 |
|
325 |
|