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basic gradio app
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4 changed files with 951 additions and 12 deletions
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demo/app.py
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580
demo/app.py
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import os
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import sys
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from pathlib import Path
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import gradio as gr
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import torch
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# Add the src directory to the path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from ctx_to_lora.data.processing import tokenize_ctx_text
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from ctx_to_lora.model_loading import get_tokenizer
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from ctx_to_lora.modeling import hypernet
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sys.modules["ctx_to_lora.modeling_utils"] = hypernet
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# Global state
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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modulated_model = None
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chat_history = []
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ctx_tokenizer = None
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base_tokenizer = None
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try:
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DEFAULT_CONTEXT = Path("data/sakana_wiki.txt").read_text(encoding="utf-8").strip()
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except FileNotFoundError:
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DEFAULT_CONTEXT = ""
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WARNING_MESSAGE = (
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"⚠️ **Caution**: This is an academic proof-of-concept application.\n"
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"The model may generate inaccurate information or hallucinate facts."
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)
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FOOTER = """
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⚠️ This model is an experimental prototype and is only available for educational and research and development purposes. It is not suitable for commercial use or in environments where failure can have significant effects (mission-critical environments).
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The use of this model is at the user's own risk and its performance and results is not guaranteed in any way.
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Sakana AI is not responsible for any direct or indirect loss resulting from using this model, regardless of the outcome.
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"""
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def load_custom_chat_template(tokenizer, model_name):
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if "gemma" in model_name.lower():
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template_path = "chat_templates/google/gemma-2-2b-it.jinja"
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if os.path.exists(template_path):
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with open(template_path) as f:
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template_content = f.read()
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tokenizer.chat_template = template_content
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print(f"Loaded custom chat template from {template_path}")
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return True
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return False
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def get_available_checkpoints():
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checkpoints = []
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trained_dir = Path("trained_t2l")
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if trained_dir.exists():
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for model_dir in trained_dir.iterdir():
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if model_dir.is_dir():
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for ckpt_file in model_dir.glob("**/*.bin"):
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if "pytorch_model.bin" in ckpt_file.name:
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checkpoints.append(str(ckpt_file))
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train_outputs = Path("train_outputs/runs")
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if train_outputs.exists():
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for run_dir in train_outputs.iterdir():
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if run_dir.is_dir():
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for ckpt_dir in run_dir.glob("checkpoint-*"):
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ckpt_file = ckpt_dir / "pytorch_model.bin"
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if ckpt_file.exists():
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checkpoints.append(str(ckpt_file))
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return sorted(checkpoints) if checkpoints else ["No checkpoints found"]
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def load_checkpoint(checkpoint_path: str) -> tuple[str, gr.update]:
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global modulated_model, ctx_tokenizer, base_tokenizer, chat_history
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if not checkpoint_path or checkpoint_path == "No checkpoints found":
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return "⚠️ Please select a valid checkpoint", gr.update()
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try:
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print(f"Loading checkpoint: {checkpoint_path}")
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from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
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state_dict = torch.load(checkpoint_path, weights_only=False)
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modulated_model = ModulatedPretrainedModel.from_state_dict(
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state_dict,
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train=False,
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use_flash_attn=True,
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use_sequence_packing=False,
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)
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modulated_model = modulated_model.to(device).to(torch.bfloat16)
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modulated_model.eval()
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ctx_encoder_model_name_or_path = (
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modulated_model.ctx_encoder_args.ctx_encoder_model_name_or_path
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or modulated_model.base_model.config.name_or_path
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)
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ctx_tokenizer = get_tokenizer(ctx_encoder_model_name_or_path, train=False)
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base_tokenizer = get_tokenizer(
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modulated_model.base_model.config.name_or_path, train=False
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)
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load_custom_chat_template(
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base_tokenizer, modulated_model.base_model.config.name_or_path
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)
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chat_history = [{"role": "system", "content": ""}]
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model_name = modulated_model.base_model.config.name_or_path
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success_msg = (
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f"✅ Successfully loaded checkpoint!\n\nBase Model: {model_name}\n\n"
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"You can now add context and start chatting."
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)
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return success_msg, gr.update(interactive=True)
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except Exception as e:
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import traceback
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error_msg = (
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f"❌ Error loading checkpoint:\n{str(e)}\n\n{traceback.format_exc()}"
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)
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print(error_msg)
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return error_msg, gr.update(interactive=False)
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def process_multiple_contexts(contexts: list[str]) -> dict:
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contexts = [ctx for ctx in contexts if ctx.strip()]
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if not contexts:
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contexts = [""]
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print(f"Processing {len(contexts)} non-empty contexts")
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tokenized_contexts = tokenize_ctx_text({"context": contexts}, ctx_tokenizer)
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ctx_ids = tokenized_contexts["ctx_ids"]
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ctx_ids = [
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torch.tensor(ctx_id, dtype=torch.long, device=device) for ctx_id in ctx_ids
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]
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ctx_attn_mask = [torch.ones_like(ids) for ids in ctx_ids]
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ctx_attn_mask = [
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torch.tensor(mask, dtype=torch.long, device=device) for mask in ctx_attn_mask
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]
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ctx_ids = torch.nn.utils.rnn.pad_sequence(
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ctx_ids,
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batch_first=True,
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padding_value=0,
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)
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ctx_attn_mask = torch.nn.utils.rnn.pad_sequence(
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ctx_attn_mask,
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batch_first=True,
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padding_value=0,
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)
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return {"ctx_ids": ctx_ids, "ctx_attn_mask": ctx_attn_mask}
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def add_user_message(message: str, history):
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if not message.strip():
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return history, ""
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return history + [[message, None]], ""
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def generate_response(
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history: list[list[str]],
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system_msg: str,
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context_1: str,
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scaler_1: float,
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context_2: str,
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scaler_2: float,
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context_3: str,
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scaler_3: float,
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bias_scaler: float,
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):
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global modulated_model, chat_history, ctx_tokenizer, base_tokenizer
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if modulated_model is None:
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history[-1][1] = "Please load a checkpoint first."
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yield history
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return
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if not history or history[-1][0] is None:
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yield history
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return
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try:
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user_message = history[-1][0]
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if system_msg.strip() and chat_history[0]["role"] == "system":
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chat_history[0]["content"] = system_msg.strip()
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chat_history.append({"role": "user", "content": user_message})
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contexts = []
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scalers = []
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if context_1 and context_1.strip():
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contexts.append(context_1)
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scalers.append(scaler_1)
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if context_2 and context_2.strip():
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contexts.append(context_2)
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scalers.append(scaler_2)
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if context_3 and context_3.strip():
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contexts.append(context_3)
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scalers.append(scaler_3)
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if not contexts:
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contexts = [""]
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scalers = [1.0]
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print(f"Processing {len(contexts)} contexts with scalers: {scalers}")
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print(f"Bias scaler: {bias_scaler}")
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with torch.inference_mode(), torch.amp.autocast(str(device)):
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ctx_inputs = process_multiple_contexts(contexts)
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ctx_ids = ctx_inputs["ctx_ids"].to(device)
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ctx_attn_mask = ctx_inputs["ctx_attn_mask"].to(device)
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scalers_tensor = torch.tensor(scalers, dtype=torch.float32, device=device)
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model_inputs = base_tokenizer.apply_chat_template(
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chat_history, return_tensors="pt", add_generation_prompt=True
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).to(device)
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print(f"Contexts: {contexts}")
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print(f"Chat history: {chat_history}")
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outputs = modulated_model.generate(
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ctx_ids=ctx_ids,
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ctx_attn_mask=ctx_attn_mask,
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n_ctx_chunks=torch.tensor([len(ctx_ids)], device=ctx_ids.device),
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scalers=scalers_tensor,
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bias_scaler=bias_scaler,
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input_ids=model_inputs,
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max_new_tokens=512,
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do_sample=False,
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temperature=0,
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)
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response = base_tokenizer.decode(
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outputs[0][model_inputs.shape[1] :], skip_special_tokens=True
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)
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chat_history.append({"role": "assistant", "content": response})
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words = response.split()
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partial_response = ""
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for word in words:
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partial_response += word + " "
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history[-1][1] = partial_response.strip()
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yield history
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history[-1][1] = response
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yield history
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except Exception as e:
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import traceback
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error_msg = f"❌ Error: {str(e)}"
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print(f"Error generating response: {str(e)}\n\n{traceback.format_exc()}")
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history[-1][1] = error_msg
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yield history
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def reset_chat(system_msg: str):
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global chat_history
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chat_history = [
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{"role": "system", "content": system_msg.strip() if system_msg else ""}
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]
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return [[None, WARNING_MESSAGE]], "Chat history reset successfully!"
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def update_context_display(num_contexts: int):
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updates = []
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for i in range(3):
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updates.append(gr.update(visible=(i < num_contexts)))
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return updates
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custom_css = """
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:root {
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color-scheme: light;
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}
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.gradio-container {
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font-family: 'Inter', sans-serif;
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}
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.chat-container {
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border-radius: 10px;
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border: 1px solid #e0e0e0;
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}
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.context-field {
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background-color: #f9f9f9;
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border-radius: 8px;
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padding: 15px;
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margin-bottom: 10px;
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border: 1px solid #e0e0e0;
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}
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.status-box {
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border-radius: 8px;
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padding: 15px;
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margin: 10px 0;
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}
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.primary-button {
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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border: none;
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border-radius: 6px;
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color: white;
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font-weight: 600;
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}
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.secondary-button {
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background-color: #607d8b;
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border: none;
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border-radius: 6px;
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color: white;
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}
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#chatbot {
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height: 500px;
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}
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.instruction-text {
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font-style: italic;
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color: #666;
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font-size: 0.9em;
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margin-bottom: 10px;
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}
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.warning-box {
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background-color: #fff3cd;
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border: 1px solid #ffc107;
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border-radius: 8px;
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padding: 12px;
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margin: 10px 0;
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color: #856404;
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font-size: 0.95em;
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}
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.warning-box strong {
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color: #d97706;
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}
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"""
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def create_demo():
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with gr.Blocks(
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title="Context-to-LoRA Chat Interface",
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theme=gr.themes.Soft(),
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css=custom_css,
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) as demo:
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gr.Markdown(
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"""
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# 🎯 Context-to-LoRA Chat Interface
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Load a hypernetwork checkpoint and chat with a context-modulated language model.
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Add multiple contexts with individual scaling parameters to influence the model's responses.
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"""
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)
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gr.Markdown(
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"""
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### 📖 Usage Instructions
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1. **Load a Checkpoint**: Select a hypernetwork checkpoint from the dropdown and click "Load Checkpoint"
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2. **Configure Contexts**:
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- Use the slider to choose how many contexts you want (0-3)
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- Enter your context information in each text field
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- Adjust the scaling sliders to control the influence of each context
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3. **Set Bias Scaler**: Adjust the bias scaler to control overall model behavior
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4. **Start Chatting**: Once the model is loaded, type your message and press Shift+Enter or click Send
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5. **Reset**: Use the "Reset Chat" button to start a new conversation
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💡 **Tip**: You can use context to provide background information or specific knowledge
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that should influence the model's responses.
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"""
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)
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with gr.Row():
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with gr.Column(scale=1):
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gr.Markdown("### 📦 Load Checkpoint")
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gr.Markdown(
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"*Select a trained hypernetwork checkpoint to begin.*",
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elem_classes="instruction-text",
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)
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checkpoint_dropdown = gr.Dropdown(
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choices=get_available_checkpoints(),
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label="Select Checkpoint",
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value=None,
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interactive=True,
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)
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load_btn = gr.Button("Load Checkpoint", variant="primary", size="lg")
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status_box = gr.Textbox(
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label="Status",
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lines=8,
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interactive=False,
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elem_classes="status-box",
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)
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gr.Markdown("---")
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gr.Markdown("### 📝 Context Configuration")
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gr.Markdown(
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"*Add contexts to influence the model's responses. Each context can be scaled independently.*",
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elem_classes="instruction-text",
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)
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num_contexts = gr.Slider(
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minimum=0,
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maximum=3,
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step=1,
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value=1,
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label="Number of Contexts",
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interactive=True,
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)
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with gr.Group(visible=True) as context_1_group:
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context_1 = gr.Textbox(
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label="Context 1",
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placeholder="Enter context information here...",
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lines=4,
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elem_classes="context-field",
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value=DEFAULT_CONTEXT,
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)
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scaler_1 = gr.Slider(
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minimum=-2.0,
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maximum=2.0,
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step=0.01,
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value=1.0,
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label="Context 1 Scaling",
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)
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with gr.Group(visible=False) as context_2_group:
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context_2 = gr.Textbox(
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label="Context 2",
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placeholder="Enter additional context information...",
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lines=4,
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elem_classes="context-field",
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)
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scaler_2 = gr.Slider(
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minimum=-2.0,
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maximum=2.0,
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step=0.01,
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value=1.0,
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label="Context 2 Scaling",
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)
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with gr.Group(visible=False) as context_3_group:
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context_3 = gr.Textbox(
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label="Context 3",
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placeholder="Enter additional context information...",
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lines=4,
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elem_classes="context-field",
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)
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scaler_3 = gr.Slider(
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minimum=-2.0,
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maximum=2.0,
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step=0.01,
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value=1.0,
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label="Context 3 Scaling",
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)
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gr.Markdown("---")
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bias_scaler = gr.Slider(
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minimum=-2.0,
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maximum=2.0,
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step=0.01,
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value=1.0,
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label="Bias Scaler",
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info="A single scalar applied to bias parameters (independent of contexts)",
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)
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with gr.Column(scale=2):
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gr.Markdown("### 💬 Chat Interface")
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system_msg = gr.Textbox(
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label="System Message",
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placeholder="Enter system message (optional)...",
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lines=2,
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)
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chatbot = gr.Chatbot(
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label="Conversation",
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height=500,
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elem_id="chatbot",
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elem_classes="chat-container",
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value=[[None, WARNING_MESSAGE]],
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)
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with gr.Row():
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msg = gr.Textbox(
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label="Your Message",
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placeholder="Type your message here... (Shift+Enter for new line)",
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lines=2,
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scale=4,
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interactive=False,
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)
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send_btn = gr.Button(
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"Send", variant="primary", scale=1, interactive=False
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||||
)
|
||||
|
||||
with gr.Row():
|
||||
clear_btn = gr.Button("🔄 Reset Chat", variant="secondary")
|
||||
reset_status = gr.Textbox(label="Reset Status", visible=False)
|
||||
|
||||
load_btn.click(
|
||||
fn=load_checkpoint,
|
||||
inputs=[checkpoint_dropdown],
|
||||
outputs=[status_box, msg],
|
||||
).then(fn=lambda: gr.update(interactive=True), outputs=[send_btn])
|
||||
|
||||
num_contexts.change(
|
||||
fn=update_context_display,
|
||||
inputs=[num_contexts],
|
||||
outputs=[context_1_group, context_2_group, context_3_group],
|
||||
)
|
||||
|
||||
msg.submit(
|
||||
fn=add_user_message,
|
||||
inputs=[msg, chatbot],
|
||||
outputs=[chatbot, msg],
|
||||
).then(
|
||||
fn=generate_response,
|
||||
inputs=[
|
||||
chatbot,
|
||||
system_msg,
|
||||
context_1,
|
||||
scaler_1,
|
||||
context_2,
|
||||
scaler_2,
|
||||
context_3,
|
||||
scaler_3,
|
||||
bias_scaler,
|
||||
],
|
||||
outputs=[chatbot],
|
||||
)
|
||||
|
||||
send_btn.click(
|
||||
fn=add_user_message,
|
||||
inputs=[msg, chatbot],
|
||||
outputs=[chatbot, msg],
|
||||
).then(
|
||||
fn=generate_response,
|
||||
inputs=[
|
||||
chatbot,
|
||||
system_msg,
|
||||
context_1,
|
||||
scaler_1,
|
||||
context_2,
|
||||
scaler_2,
|
||||
context_3,
|
||||
scaler_3,
|
||||
bias_scaler,
|
||||
],
|
||||
outputs=[chatbot],
|
||||
)
|
||||
|
||||
clear_btn.click(
|
||||
fn=reset_chat,
|
||||
inputs=[system_msg],
|
||||
outputs=[chatbot, reset_status],
|
||||
)
|
||||
|
||||
gr.Markdown(f"---\n{FOOTER.strip()}")
|
||||
return demo
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
demo = create_demo()
|
||||
demo.launch(
|
||||
server_name="0.0.0.0",
|
||||
server_port=7860,
|
||||
share=False,
|
||||
debug=True,
|
||||
)
|
||||
|
|
@ -26,6 +26,7 @@ dependencies = [
|
|||
"liger-kernel",
|
||||
"tensorboard",
|
||||
"flask",
|
||||
"gradio>=4.40.0",
|
||||
"pandas",
|
||||
"plotly",
|
||||
"rouge-score",
|
||||
|
|
|
|||
|
|
@ -820,6 +820,8 @@ class ModulatedPretrainedModel(nn.Module):
|
|||
ctx_position_ids: Integer[Tensor, "n_chunks ctx_length"] | None = None,
|
||||
n_ctx_chunks: Integer[Tensor, "n_ctx"] | None = None,
|
||||
n_queries: Integer[Tensor, "n_ctx"] | None = None,
|
||||
scalers: Float[Tensor, "n_ctx"] | None = None,
|
||||
bias_scaler: float | None = None,
|
||||
*model_inputs_args: Any,
|
||||
**model_inputs_kwargs: dict[str, Any],
|
||||
):
|
||||
|
|
@ -872,6 +874,8 @@ class ModulatedPretrainedModel(nn.Module):
|
|||
lora_bias=self.hypernet.get_head_bias()
|
||||
if self.hypernet.config.use_bias
|
||||
else None,
|
||||
scalers=scalers,
|
||||
bias_scaler=bias_scaler,
|
||||
)
|
||||
|
||||
# apply lora hook to the base model
|
||||
|
|
|
|||
378
uv.lock
generated
378
uv.lock
generated
|
|
@ -47,6 +47,15 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/63/b1/8198e3cdd11a426b1df2912e3381018c4a4a55368f6d0857ba3ca418ef93/accelerate-1.6.0-py3-none-any.whl", hash = "sha256:1aee717d3d3735ad6d09710a7c26990ee4652b79b4e93df46551551b5227c2aa", size = 354748, upload-time = "2025-04-01T11:53:01.298Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aiofiles"
|
||||
version = "24.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "aiohappyeyeballs"
|
||||
version = "2.6.1"
|
||||
|
|
@ -290,6 +299,62 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3", size = 63815, upload-time = "2025-03-13T11:10:21.14Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "audioop-lts"
|
||||
version = "0.2.2"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/38/53/946db57842a50b2da2e0c1e34bd37f36f5aadba1a929a3971c5d7841dbca/audioop_lts-0.2.2.tar.gz", hash = "sha256:64d0c62d88e67b98a1a5e71987b7aa7b5bcffc7dcee65b635823dbdd0a8dbbd0", size = 30686, upload-time = "2025-08-05T16:43:17.409Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/de/d4/94d277ca941de5a507b07f0b592f199c22454eeaec8f008a286b3fbbacd6/audioop_lts-0.2.2-cp313-abi3-macosx_10_13_universal2.whl", hash = "sha256:fd3d4602dc64914d462924a08c1a9816435a2155d74f325853c1f1ac3b2d9800", size = 46523, upload-time = "2025-08-05T16:42:20.836Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/f8/5a/656d1c2da4b555920ce4177167bfeb8623d98765594af59702c8873f60ec/audioop_lts-0.2.2-cp313-abi3-macosx_10_13_x86_64.whl", hash = "sha256:550c114a8df0aafe9a05442a1162dfc8fec37e9af1d625ae6060fed6e756f303", size = 27455, upload-time = "2025-08-05T16:42:22.283Z" },
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||||
{ url = "https://files.pythonhosted.org/packages/1b/83/ea581e364ce7b0d41456fb79d6ee0ad482beda61faf0cab20cbd4c63a541/audioop_lts-0.2.2-cp313-abi3-macosx_11_0_arm64.whl", hash = "sha256:9a13dc409f2564de15dd68be65b462ba0dde01b19663720c68c1140c782d1d75", size = 26997, upload-time = "2025-08-05T16:42:23.849Z" },
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||||
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||||
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{ url = "https://files.pythonhosted.org/packages/75/e8/cd95eef479656cb75ab05dfece8c1f8c395d17a7c651d88f8e6e291a63ab/audioop_lts-0.2.2-cp313-abi3-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:ba7c3a7e5f23e215cb271516197030c32aef2e754252c4c70a50aaff7031a2c8", size = 93892, upload-time = "2025-08-05T16:42:27.902Z" },
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Loading…
Add table
Add a link
Reference in a new issue