Evolcodellama 7b

🚀 🦙💻 EvolCodeLlama-7b

This is a fine - tuned model based on the codellama/CodeLlama-7b-hf, which aims to provide more targeted text - generation capabilities. It offers a valuable resource for educational and research purposes in the field of natural language processing.

🚀 Quick Start

This section provides a brief introduction to the model and its purpose. The EvolCodeLlama-7b model is fine - tuned on the mlabonne/Evol-Instruct-Python-1k dataset using QLoRA (4 - bit precision). It is mainly designed for educational use rather than inference.

📝 Article

✨ Features

This model is a fine - tuned version of codellama/CodeLlama-7b-hf using QLoRA (4 - bit precision) on the mlabonne/Evol-Instruct-Python-1k dataset. It is primarily intended for educational purposes.

📦 Installation

The installation process mainly involves installing the necessary Python libraries. You can use the following command to install the required libraries:

pip install transformers accelerate

💻 Usage Examples

Basic Usage

# pip install transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/EvolCodeLlama-7b"
prompt = "Your prompt"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

sequences = pipeline(
    f'{prompt}',
    do_sample=True,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
    max_length=200,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

🔧 Technical Details

Training

The model was trained on an RTX 3090 in 1h 11m 44s with the following configuration file:

base_model: codellama/CodeLlama-7b-hf
base_model_config: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: EvolCodeLlama-7b

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: mlabonne/Evol-Instruct-Python-1k
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 10
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

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

warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

Here are the loss curves:

📄 License

This model is released under the Apache - 2.0 license.