Uploaded model
- Developed by: Asuncom
- License: apache-2.0
- Finetuned from model : unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --upgrade pip
!pip install --no-deps "xformers<0.0.26" "trl<0.9.0" peft accelerate bitsandbytes
from unsloth import FastLanguageModel
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
fourbit_models = [
"unsloth/Meta-Llama-3.1-8B-bnb-4bit", # Llama-3.1 15 trillion tokens model 2x faster!
"unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
"unsloth/Meta-Llama-3.1-70B-bnb-4bit",
"unsloth/Meta-Llama-3.1-405B-bnb-4bit", # We also uploaded 4bit for 405b!
"unsloth/Mistral-Nemo-Base-2407-bnb-4bit", # New Mistral 12b 2x faster!
"unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"unsloth/mistral-7b-v0.3-bnb-4bit", # Mistral v3 2x faster!
"unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"unsloth/Phi-3-mini-4k-instruct", # Phi-3 2x faster!d
"unsloth/Phi-3-medium-4k-instruct",
"unsloth/gemma-2-9b-bnb-4bit",
"unsloth/gemma-2-27b-bnb-4bit", # Gemma 2x faster!
] # More models at https://huggingface.co/unsloth
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit",
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
)
# ========================================================
# Test before training
# ========================================================
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
alpaca_prompt.format(
"请把现代汉语翻译成古文", # instruction
"其品行廉正，所以至死也不放松对自己的要求。", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
model = FastLanguageModel.get_peft_model(
model,
r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 16,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
# [NEW] "unsloth" uses 30% less VRAM, fits 2x larger batch sizes!
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
)
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
def formatting_prompts_func(examples):
instructions = examples["instruction"]
inputs = examples["input"]
outputs = examples["output"]
texts = []
for instruction, input, output in zip(instructions, inputs, outputs):
# Must add EOS_TOKEN, otherwise your generation will go on forever!
text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN
texts.append(text)
return { "text" : texts, }
pass
from datasets import load_dataset
dataset = load_dataset("Asuncom/shiji-qishiliezhuan", split = "train")
dataset = dataset.map(formatting_prompts_func, batched = True,)
from trl import SFTTrainer
from transformers import TrainingArguments
from unsloth import is_bfloat16_supported
trainer = SFTTrainer(
model = model,
tokenizer = tokenizer,
train_dataset = dataset,
dataset_text_field = "text",
max_seq_length = max_seq_length,
dataset_num_proc = 2,
packing = False, # Can make training 5x faster for short sequences.
args = TrainingArguments(
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
warmup_steps = 5,
# num_train_epochs = 1, # Set this for 1 full training run.
max_steps = 100,
learning_rate = 2e-4,
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
output_dir = "outputs",
),
)
#@title Show current memory stats
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
import wandb
# 初始化一个离线模式的W&B运行
wandb.init(mode="offline", project="asuncom", entity="asuncom")
trainer_stats = trainer.train()
#@title Show final memory and time stats
used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
used_memory_for_lora = round(used_memory - start_gpu_memory, 3)
used_percentage = round(used_memory /max_memory*100, 3)
lora_percentage = round(used_memory_for_lora/max_memory*100, 3)
print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.")
print(f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.")
print(f"Peak reserved memory = {used_memory} GB.")
print(f"Peak reserved memory for training = {used_memory_for_lora} GB.")
print(f"Peak reserved memory % of max memory = {used_percentage} %.")
print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.")
# alpaca_prompt = Copied from above
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
alpaca_prompt.format(
"请把现代汉语翻译成古文", # instruction
"其品行廉正，所以至死也不放松对自己的要求。", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
model.save_pretrained("lora_model") # Local saving
tokenizer.save_pretrained("lora_model")
model.push_to_hub("Asuncom/Llama-3.1-8B-bnb-4bit-shiji", token = "hf_huggingface的密钥NeKb") # Online saving
tokenizer.push_to_hub("Asuncom/Llama-3.1-8B-bnb-4bit-shiji", token = "hf_huggingface的密钥saving
if False:
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length = max_seq_length,
dtype = dtype,
load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
# alpaca_prompt = You MUST copy from above!
inputs = tokenizer(
[
alpaca_prompt.format(
"What is a famous tall tower in Paris?", # instruction
"", # input
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
# Merge to 16bit
if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",)
if False: model.push_to_hub_merged("Asuncom/Llama-3.1-8B-bnb-4bit-shiji", tokenizer, save_method = "merged_16bit", token = "hf_huggingface的密钥NeKb")
# Merge to 4bit
if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",)
if False: model.push_to_hub_merged("Asuncom/Llama-3.1-8B-bnb-4bit-shiji", tokenizer, save_method = "merged_4bit", token = "hf_huggingface的密钥oRA adapters
if False: model.save_pretrained_merged("model", tokenizer, save_method = "lora",)
if False: model.push_to_hub_merged("Asuncom/Llama-3.1-8B-bnb-4bit-shiji", tokenizer, save_method = "lora", token = "hf_huggingface的密钥
# Save to 8bit Q8_0
if False: model.save_pretrained_gguf("model", tokenizer,)
# Remember to go to https://huggingface.co/settings/tokens for a token!
# And change hf to your username!
if False: model.push_to_hub_gguf("Asuncom/Llama-3.1-8B-bnb-4bit-shiji", tokenizer, token = "")
# Save to 16bit GGUF
if False: model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16")
if False: model.push_to_hub_gguf("Asuncom/Llama-3.1-8B-bnb-4bit-shiji", tokenizer, quantization_method = "f16", token = "")
# Save to q4_k_m GGUF
if False: model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")
if True: model.push_to_hub_gguf("Asuncom/Llama-3.1-8B-bnb-4bit-shiji", tokenizer, quantization_method = "q4_k_m", token = "hf_xxxxx")
# Save to multiple GGUF options - much faster if you want multiple!
if False:
model.push_to_hub_gguf(
"Asuncom/Llama-3.1-8B-bnb-4bit-shiji", # Change hf to your username!
tokenizer,
quantization_method = ["q4_k_m", "q8_0", "q5_k_m",],
token = "hf_huggingface的密钥NeKb", # Get a token at https://huggingface.co/settings/tokens
)
model.push_to_hub_gguf(
"Asuncom/Llama-3.1-8B-bnb-4bit-shiji", # Change hf to your username!
tokenizer,
quantization_method = ["q4_k_m", "q8_0", "q5_k_m",],
token = "hf_huggingface的密钥NeKb", # Get a token at https://huggingface.co/settings/tokens
)
[ 279/ 292] blk.30.attn_output.weight - [ 4096, 4096, 1, 1], type = f16, converting to q5_K .. size = 32.00 MiB -> 11.00 MiB
[ 280/ 292] blk.30.attn_q.weight - [ 4096, 4096, 1, 1], type = f16, converting to q5_K .. size = 32.00 MiB -> 11.00 MiB
[ 281/ 292] blk.30.attn_v.weight - [ 4096, 1024, 1, 1], type = f16, converting to q6_K .. size = 8.00 MiB -> 3.28 MiB
[ 282/ 292] blk.31.ffn_gate.weight - [ 4096, 14336, 1, 1], type = f16, converting to q5_K .. size = 112.00 MiB -> 38.50 MiB
[ 283/ 292] blk.31.ffn_up.weight - [ 4096, 14336, 1, 1], type = f16, converting to q5_K .. size = 112.00 MiB -> 38.50 MiB
[ 284/ 292] blk.31.attn_k.weight - [ 4096, 1024, 1, 1], type = f16, converting to q5_K .. size = 8.00 MiB -> 2.75 MiB
[ 285/ 292] blk.31.attn_output.weight - [ 4096, 4096, 1, 1], type = f16, converting to q5_K .. size = 32.00 MiB -> 11.00 MiB
[ 286/ 292] blk.31.attn_q.weight - [ 4096, 4096, 1, 1], type = f16, converting to q5_K .. size = 32.00 MiB -> 11.00 MiB
[ 287/ 292] blk.31.attn_v.weight - [ 4096, 1024, 1, 1], type = f16, converting to q6_K .. size = 8.00 MiB -> 3.28 MiB
[ 288/ 292] output.weight - [ 4096, 128256, 1, 1], type = f16, converting to q6_K .. size = 1002.00 MiB -> 410.98 MiB
[ 289/ 292] blk.31.attn_norm.weight - [ 4096, 1, 1, 1], type = f32, size = 0.016 MB
[ 290/ 292] blk.31.ffn_down.weight - [14336, 4096, 1, 1], type = f16, converting to q6_K .. size = 112.00 MiB -> 45.94 MiB
[ 291/ 292] blk.31.ffn_norm.weight - [ 4096, 1, 1, 1], type = f32, size = 0.016 MB
[ 292/ 292] output_norm.weight - [ 4096, 1, 1, 1], type = f32, size = 0.016 MB
llama_model_quantize_internal: model size = 15317.02 MB
llama_model_quantize_internal: quant size = 5459.93 MB
main: quantize time = 147401.53 ms
main: total time = 147401.53 ms
Unsloth: Conversion completed! Output location: ./Asuncom/Llama-3.1-8B-bnb-4bit-shiji/unsloth.Q5_K_M.gguf
Unsloth: Uploading GGUF to Huggingface Hub...
unsloth.F16.gguf: 100%|██████████| 16.1G/16.1G [26:20<00:00, 10.2MB/s]
Saved GGUF to https://huggingface.co/Asuncom/Llama-3.1-8B-bnb-4bit-shiji
Unsloth: Uploading GGUF to Huggingface Hub...
unsloth.Q4_K_M.gguf: 100%|██████████| 4.92G/4.92G [08:05<00:00, 10.1MB/s]
Saved GGUF to https://huggingface.co/Asuncom/Llama-3.1-8B-bnb-4bit-shiji
Unsloth: Uploading GGUF to Huggingface Hub...
unsloth.Q8_0.gguf: 100%|██████████| 8.54G/8.54G [13:48<00:00, 10.3MB/s]
Saved GGUF to https://huggingface.co/Asuncom/Llama-3.1-8B-bnb-4bit-shiji
Unsloth: Uploading GGUF to Huggingface Hub...
unsloth.Q5_K_M.gguf: 100%|██████████| 5.73G/5.73G [09:24<00:00, 10.2MB/s]
Saved GGUF to https://huggingface.co/Asuncom/Llama-3.1-8B-bnb-4bit-shipython
- Downloads last month
- 122
Model tree for Asuncom/Llama-3.1-8B-bnb-4bit-shiji
Base model
unsloth/Meta-Llama-3.1-8B