uzabase/LLM2Vec-Llama-2-7b-hf-mntp

Model Info

This is a model that applies LLM2Vec to Llama2. Only the PEFT Adapter is distributed. LLM2Vec fine-tunes on two tasks: MNTP and SimCSE, but this repository contains the results of applying only the MNTP task.

Model Details

Model Description

• Model type: PEFT
• Language(s) (NLP): Japanese
• License: Apache2.0
• Finetuned from model: Llama-2-7b-hf

Sources

• Repository: https://github.com/McGill-NLP/llm2vec
• Paper: https://arxiv.org/abs/2404.05961

Usage

• Please see original LLM2Vec repo

Training Details

Training Data

• wikitext

Training Hyperparameter

• batch_size: 64,
• gradient_accumulation_steps: 1
• max_seq_length": 512,
• mask_token_type: "blank"
• mlm_probability: 0.2
• lora_r: 16
• torch_dtype "bfloat16"
• attn_implementation "flash_attention_2"
• bf16: true
• gradient_checkpointing: true

Accelerator Settings

• deepspeed_config:
  • gradient_accumulation_steps: 1
  • gradient_clipping: 1.0
  • offload_optimizer_device: nvme
  • offload_optimizer_nvme_path: /nvme
  • zero3_save_16bit_model: true
  • zero_stage: 2
• distributed_type: DEEPSPEED
• downcast_bf16: 'no'
• dynamo_config:
  • dynamo_backend: INDUCTOR
  • dynamo_mode: default
  • dynamo_use_dynamic: true
  • dynamo_use_fullgraph: true
• enable_cpu_affinity: false
• machine_rank: 0
• main_training_function: main
• mixed_precision: bf16
• num_machines: 1
• num_processes: 2
• rdzv_backend: static
• same_network: true
• quse_cpu: false

Framework versions

• Python: 3.12.3
• PEFT 0.11.1
• Sentence Transformers: 3.0.1
• Transformers: 4.41.0
• PyTorch: 2.3.0
• Accelerate: 0.30.1
• Datasets: 2.20.0
• Tokenizers: 0.19.1
• MTEB: 1.13.0