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Llama-3-6B-v0.1

Text Generation
Transformers
Safetensors
English
llama
Llama-3-6B
6B
text-generation-inference
Inference Endpoints
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metadata
language:
  - en
license: llama3
library_name: transformers
datasets:
  - prince-canuma/fineweb-CC-MAIN-2024-10-1B-en

Model Summary

Llama-3-6B

Introducing the world's first Llama-3 base model with 6B parameters. This model is a pretrained version of prince-canuma/Llama-3-6B-v0, which was created from Meta-Llama-3-8B using a technique called downcycling . The model was continually pretrained on 1 billion tokens of English-only text from fineweb, achieving impressive results on the evaluation set:

  • Loss: 2.4942

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • Developed by: Prince Canuma
  • Sponsored by: General
  • Model type: Llama
  • Language(s) (NLP): [More Information Needed]
  • License: MIT
  • Pretrained from model: prince-canuma/Llama-3-6B-v0

Model Sources [optional]

Uses

You can use this model to create instruct and chat versions for various use cases such as: Coding assistant, RAG, Function Calling and more.

Limitations

This model inherits some of the base model's limitations and some additional ones from it's creation process, such as:

  • Limited scope for coding and math: According to benchmarks, this model needs more pretraining/finetuning on code and math data to excel at reasoning tasks.
  • Language Limitations: This model was continually pretrained on english only data. If you are planning to use it for multilingual use cases I recommend fine-tuning or continued pretraining.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer

# Load model, config and tokenizer
model_name = "prince-canuma/Llama-3-6B-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

inputs = tokenizer(
[
   "Who created Python?"
], return_tensors = "pt")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 200)

Output:

<|begin_of_text|>Who created Python? What is Python used for? What is the difference between Python 2 and Python 3? What is the difference between Python and Python 3?
Python is a programming language that was created by Guido van Rossum in 1991. It is a widely used language for web development, data science, and machine learning. Python is also used for creating software applications and games.
Python is a powerful language that is easy to learn and use. It has a large library of built-in functions and packages that make it easy to write code. Python is also a very popular language for web development, with many popular web frameworks such as Django and Flask being written in Python.
Python is also used for data science and machine learning. It has a large library of packages for data analysis, machine learning, and artificial intelligence. Python is also used for creating software applications and games.
Python 2 and Python 3 are two different versions of the Python language. Python 2 was the original version of the

Training Details

Downcycling

A technique that allows you to create new LLMs of diversa sizes from checkpoints of large pretrained models. You take a reference model (i.e., Llama-3-8B) and copy the weights of 24 layers out of 32 layers alongside embedding and prediction heads. Then you initialize a smaller target model with 24 layers and load those pretrained weights. This new model will most likely still output legible outputs, but for it to perform well you need continue the pretraining.

Training Data

For continued pretrained, I extracted 1B tokens from Huggingface's FineWeb CC-Main-2024-10 slice.

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0002
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 2

Training results

Training Loss Epoch Step Validation Loss
7.1562 0.0 1 7.1806
2.7339 0.25 5867 2.6266
2.6905 0.5 11734 2.5872
2.6134 0.75 17601 2.5549
2.532 1.0 23468 2.5235
2.5319 1.25 29335 2.5067
2.3336 1.5 35202 2.4968
2.3486 1.75 41069 2.4942

Framework versions

  • PEFT 0.10.0
  • Transformers 4.40.0.dev0
  • Pytorch 2.2.0+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Citation [optional]

BibTeX:

@misc{prince2024downcycling,
      title={Efficient LLM Downcycling: Generating Diverse Model Sizes from Pretrained Giants},
      author={Prince Canuma},
      year={2024},
}

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: prince-canuma/Llama-3-6B-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: prince-canuma/fineweb-CC-MAIN-2024-10-1B-en
    type: completion
    split: train
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./llama-3-6b
save_safetensors: true
adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: false
pad_to_sequence_len: false

lora_r: 128
lora_alpha: 128
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:


wandb_project: llama-3-6b
wandb_entity: 
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 2
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-4

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 4
eval_table_size:
save_steps: 4000
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
    pad_token: "<|reserved_special_token_0|>"