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metadata
base_model: mlabonne/Marcoro14-7B-slerp
license: cc-by-nc-4.0
tags:
  - mlabonne/Marcoro14-7B-slerp
  - dpo
  - rlhf
datasets:
  - mlabonne/chatml_dpo_pairs

NeuralMarcoro14-7B

This is a DPO fine-tuned version of mlabonne/Marcoro14-7B-slerp using the chatml_dpo_pairs preference dataset. It improves the performance of the model on Nous benchmark suite and the Open LLM Benchmark.

It is currently the best-performing 7B LLM on the Open LLM Leaderboard (08/01/24).

You can try it out in this Space (GGUF Q4_K_M).

⚑ Quantized models

πŸ† Evaluation

Open LLM Leaderboard

Nous

Model AGIEval GPT4ALL TruthfulQA Bigbench Average
NeuralMarcoro14-7B 44.59 76.17 65.94 46.9 58.4
Marcoro14-7B-slerp 44.66 76.24 64.15 45.64 57.67
Change -0.07 -0.07 +1.79 +1.26 +0.73

🧩 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=200
  • optim="paged_adamw_32bit"
  • warmup_steps=100

DPOTrainer:

  • beta=0.1
  • max_prompt_length=1024
  • max_length=1536

πŸ’» Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/NeuralMarcoro14-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])