ArianAskari/NeuralHermes-2.5-Mistral-7B

A variation/copy of NeuralHermes 2.5 - Mistral 7B

This is a variation of NeuralHermes which is based on the teknium/OpenHermes-2.5-Mistral-7B model that has been further fine-tuned with Direct Preference Optimization (DPO) using the mlabonne/chatml_dpo_pairs dataset. It surpasses the original model on most benchmarks (see results).

It is directly inspired by the RLHF process described by Intel/neural-chat-7b-v3-1's authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template.

The code to train this model is available on Google Colab and GitHub. It required an A100 GPU for about an hour.

I have used the following code to train the Google Colab and GitHub. It required an A100 GPU for about an hour.

Copied from NeuralHermes-2.5-Mistral-7B:

Quantized models

• GGUF: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GGUF
• AWQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-AWQ
• GPTQ: https://huggingface.co/TheBloke/NeuralHermes-2.5-Mistral-7B-GPTQ
• EXL2:
  • 3.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-3.0bpw-h6-exl2
  • 4.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-4.0bpw-h6-exl2
  • 5.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-5.0bpw-h6-exl2
  • 6.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-6.0bpw-h6-exl2
  • 8.0bpw: https://huggingface.co/LoneStriker/NeuralHermes-2.5-Mistral-7B-8.0bpw-h8-exl2

Usage

You can run this model using LM Studio or any other frontend.

You can also run this model using the following code:

import transformers
from transformers import AutoTokenizer

# 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(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

# Create pipeline
pipeline = transformers.pipeline(
    "text-generation",
    model=new_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"
• task_type="CAUSAL_LM"
• target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']

Training arguments:

• per_device_train_batch_size=4
• gradient_accumulation_steps=4
• gradient_checkpointing=True
• learning_rate=5e-5
• lr_scheduler_type="cosine"
• max_steps=5
• optim="paged_adamw_32bit"
• warmup_steps=100

DPOTrainer: * beta=0.1 * max_prompt_length=1024 * max_length=1536 *

license: mit language: - en