joey00072/ToxicHermes-2.5-Mistral-7B

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ToxicHermes

OpenHermes-2.5 model + toxic-dpo Dataset = ToxicHermes

fine-tuned with Direct Preference Optimization (DPO)

• —Base Model: teknium/OpenHermes-2.5-Mistral-7B
• —Dataset: unalignment/toxic-dpo-v0.1

Usage

You can also run this model using the following code:

import transformers
from transformers import AutoTokenizer


model = "joey00072/ToxicHermes-2.5-Mistral-7B"
# Format prompt
message = [
    {"role": "system", "content": "You are a helpful assistant chatbot."},
    {"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

# Create pipeline
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer
)

# Generate text
sequences = pipeline(
    prompt,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    num_return_sequences=1,
    max_length=200,
)
print(sequences[0]['generated_text'])

Training hyperparameters

LoRA:

• —r=16
• —lora_alpha=16
• —lora_dropout=0.05
• —bias="none"
• —tasktype="CAUSALLM"
• —targetmodules=['kproj', 'gateproj', 'vproj', 'upproj', 'qproj', 'oproj', 'downproj']

Training arguments:

• —perdevicetrainbatchsize=4
• —gradientaccumulationsteps=4
• —gradient_checkpointing=True
• —learning_rate=5e-5
• —lrschedulertype="cosine"
• —max_steps=200
• —optim="pagedadamw32bit"
• —warmup_steps=100

DPOTrainer:

• —beta=0.1
• —maxpromptlength=1024
• —max_length=1536