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Model: vicgalle/Humanish-Roleplay-Llama-3.1-8B Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- roleplay
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- rp
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- human
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license: apache-2.0
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datasets:
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- ResplendentAI/NSFW_RP_Format_DPO
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- Undi95/Weyaxi-humanish-dpo-project-noemoji
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---
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# Humanish-Roleplay-Llama-3.1-8B
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A DPO-tuned Llama-3.1 to behave more "humanish", i.e., avoiding all the AI assistant slop. It also works for role-play (RP). To achieve this, the model was fine-tuned over a series of datasets:
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* General conversations from Claude Opus, from `Undi95/Meta-Llama-3.1-8B-Claude`
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* `Undi95/Weyaxi-humanish-dpo-project-noemoji`, to make the model react as a human, rejecting assistant-like or too neutral responses.
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* `ResplendentAI/NSFW_RP_Format_DPO`, to steer the model towards using the \*action\* format in RP settings. Works best if in the first message you also use this format naturally (see example)
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## Usage example
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```
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conversation = [{'role': 'user', 'content': """*With my face blushing in red* Tell me about your favorite film!"""}]
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prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.8)
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```
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The response is
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> \*Giggles nervously\* Oh my, um, \*clears throat\*, well... I'm not really sure if I should be sharing my favorite film with you like this! \*Blushes even more\*
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> But if you insist, \*gulps\*, I suppose I could tell you about this really romantic movie I saw once... \*Leans in close and whispers\* It's called \"When Harry Met Sally\"! \*Squeals and covers mouth\*
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> You know, it's this super cute story about two friends who try to keep things platonic for a whole year, but end up falling for each other in the end! \*Squirms in seat\* The chemistry between the leads is just... \*Sighs wistfully\*
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> There's this famous scene in a diner where... \*Waves hand in front of face\*... where you know what happens, and it's just so \*Blushes bright red\*... realistic and relatable! \*Giggles\* I mean, we've all been there, right? \*Winks\*
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> Anyway, it's just such a classic rom-com with great dialogue and a perfect soundtrack! \*Sways dreamily\* I could watch it over and over again. \*Tilts head\* What about you, do you have a favorite film? \*Peeks at you with big, hopeful eyes\* \*Squeals\* I'm dying to know! \*Bounces in seat excitedly\*
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## Fine-tuning script
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The script I used to train this model is available at [this file](./train_human.py). It takes less than one hour in a T4 GPU, and requires the following libs:
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```
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peft 0.11.1
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transformers 4.44.0.dev0
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trl 0.9.6
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```
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config.json
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config.json
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{
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"_name_or_path": "Undi95/Meta-Llama-3.1-8B-Claude-bf16",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128009,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.44.0.dev0",
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"use_cache": false,
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"vocab_size": 128256
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}
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generation_config.json
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"temperature": 0.6,
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}
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17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
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||||
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410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2063
tokenizer_config.json
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2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
146
train_human.py
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146
train_human.py
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# %%
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from datasets import load_dataset, Dataset
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from trl import DPOTrainer, DPOConfig
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from peft import LoraConfig
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from peft import prepare_model_for_kbit_training
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import torch
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import pandas as pd
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# %%
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dataset = load_dataset("Undi95/Weyaxi-humanish-dpo-project-noemoji")["train"]
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model_name = "Undi95/Meta-Llama-3.1-8B-Claude-bf16"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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tokenizer.padding_side = "right"
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tokenizer.pad_token = tokenizer.eos_token
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# %%
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tokenizer.chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}"
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# %%
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dataset2 = load_dataset("ResplendentAI/NSFW_RP_Format_DPO")['train']
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# %%
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dataset = dataset.to_pandas(
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)
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dataset2 = dataset2.to_pandas()
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dataset = Dataset.from_pandas(pd.concat([dataset.sample(400), dataset2]).sample(frac=1))
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# %%
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def template_prompt(system, prompt):
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if system is None:
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messages = [
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{"role": "user", "content": prompt},
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]
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else:
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messages = [
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{
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"role": "system",
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"content": system,
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},
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{"role": "user", "content": prompt},
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]
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prompt = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=False
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)
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return prompt
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def template_answer(answer):
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messages = [
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{
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"role": "assistant",
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"content": answer,
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},
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]
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answer = tokenizer.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=False
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)
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return answer
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# %%
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# create new columns
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dataset = dataset.map(
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lambda x: {
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"prompt": template_prompt(None, x["prompt"]).replace("<|start_header_id|>assistant<|end_header_id|>\n\n", "")
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}, # change this according to the dataset!!!
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)
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# %%
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dataset = dataset.map(
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lambda x: {"chosen": template_answer(x["chosen"]).replace('<|begin_of_text|>', '').replace('><|start_header_id|>assistant<|end_header_id|>\n\n', '>')},
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)
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dataset = dataset.map(
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lambda x: {"rejected": template_answer(x["rejected"]).replace('<|begin_of_text|>', '').replace('><|start_header_id|>assistant<|end_header_id|>\n\n', '>')},
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)
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# %%
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dataset[0]
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# %%
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# LoRA configuration
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peft_config = LoraConfig(
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r=16,
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lora_alpha=32,
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lora_dropout=0.05,
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bias="none",
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task_type="CAUSAL_LM",
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target_modules=[
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"k_proj",
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"gate_proj",
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"v_proj",
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"up_proj",
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"q_proj",
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"o_proj",
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"down_proj",
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],
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)
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# Model to fine-tune
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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load_in_4bit=True,
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device_map="auto",
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)
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model.config.use_cache = False
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model.gradient_checkpointing_enable()
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model = prepare_model_for_kbit_training(model)
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# %%
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output_name = f"checkpoints/exp_human_{model_name}"
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training_args = DPOConfig(
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per_device_train_batch_size=1,
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gradient_accumulation_steps=4,
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num_train_epochs=1,
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gradient_checkpointing=True,
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output_dir=output_name,
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logging_steps=1,
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max_steps=50
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)
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trainer = DPOTrainer(
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model,
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ref_model=None,
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train_dataset=dataset,
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tokenizer=tokenizer,
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args=training_args,
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peft_config=peft_config,
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)
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trainer.train()
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trainer.save_model(output_name)
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Reference in New Issue
Block a user