metadata

license: apache-2.0
pipeline_tag: text-generation

🦙💻 Safurai-Csharp-34B

📝 Article

This is a codellama/CodeLlama-7b-hf model fine-tuned using QLoRA (4-bit precision) on the mlabonne/Evol-Instruct-Python-1k.

🔧 Training

It was trained on an in 1h 11m 44s with the following configuration file:

base_model: codellama/CodeLlama-34b-hf
base_model_config: codellama/CodeLlama-34b-hf
model_type: LlamaForCausalLM
tokenizer_type: CodeLlamaTokenizer
is_llama_derived_model: true
hub_model_id: "Safurai/Evol-csharp-v1"

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: Safurai/EvolInstruct-csharp-16k-13B-Alpaca
    type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./qlora-out

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: codellama-csharp
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0003

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: 40
eval_steps: 40
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:

It is mainly designed for experimental purposes, not for inference.

💻 Usage

# pip install transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

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

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=1000,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")