初始化项目，由ModelHub XC社区提供模型
Model: Ramikan-BR/tinyllama-coder-py-4bit-v10 Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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- sft
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- code
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- lora
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- peft
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base_model: unsloth/tinyllama-chat-bnb-4bit
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pipeline_tag: text-generation
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datasets: Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl
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---
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# Uploaded model
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- **Developed by:** Ramikan-BR
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- **Model type:** [text-generation/Python Coder]
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- **Language(s) (NLP):** [en]
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/tinyllama-chat-bnb-4bit
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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### Training Data
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datasets: [Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl](https://huggingface.co/datasets/Ramikan-BR/data-oss_instruct-decontaminated_python.jsonl)
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### Training Procedure
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The model was refined using [Unsloath](https://github.com/unslothai/unsloth). The dataset [ise-uiuc/Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder-OSS-Instruct-75K/blob/main/data-oss_instruct-decontaminated.jsonl) was adjusted, leaving only data on python and divided into 10 parts, each refinement occurred for 2 epochs, using adafactor optimizer or adamw_8bit (adafactor seems to deliver less loss).
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### Model Sources [optional]
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base_model: [unsloth/tinyllama-chat-bnb-4bit](https://huggingface.co/unsloth/tinyllama-chat-bnb-4bit)
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model: [Ramikan-BR/tinyllama-coder-py-4bit-v10](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10)
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gguf_f16: [tinyllama-coder-py-4bit-v10-unsloth.F16.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.F16.gguf)
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gguf_Q4_K_M: [tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf)
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gguf_Q8_0: [tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf](https://huggingface.co/Ramikan-BR/tinyllama-coder-py-4bit-v10/blob/main/tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf)
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#### Training Hyperparameters
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Notebook [Unsloath](https://github.com/unslothai/unsloth) that I used for AI refinement: [TinyLlama](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)
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```python
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%%capture
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# Installs Unsloth, Xformers (Flash Attention) and all other packages!
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!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
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!pip install --no-deps xformers trl peft accelerate bitsandbytes # xformers "xformers<0.0.26"
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import os
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from google.colab import drive
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drive.mount('/content/drive')
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from unsloth import FastLanguageModel
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import torch
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max_seq_length = 4096 # Choose any! We auto support RoPE Scaling internally!
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dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
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load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
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# 4bit pre quantized models we support for 4x faster downloading + no OOMs.
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fourbit_models = [
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"unsloth/mistral-7b-bnb-4bit",
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"unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
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"unsloth/llama-2-7b-bnb-4bit",
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"unsloth/llama-2-13b-bnb-4bit",
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"unsloth/codellama-34b-bnb-4bit",
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"unsloth/tinyllama-bnb-4bit",
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"unsloth/gemma-7b-bnb-4bit", # New Google 6 trillion tokens model 2.5x faster!
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"unsloth/gemma-2b-bnb-4bit",
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] # More models at https://huggingface.co/unsloth
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "Ramikan-BR/tinyllama-coder-py-4bit_LORA-v9", # "unsloth/tinyllama" for 16bit loading
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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# token = "hf_...", # use one if using gated models like meta-llama/Llama-2-7b-hf
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)
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model = FastLanguageModel.get_peft_model(
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model,
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r = 256, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128
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target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
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"gate_proj", "up_proj", "down_proj",],
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lora_alpha = 512,
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lora_dropout = 0, # Currently only supports dropout = 0
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bias = "none", # Currently only supports bias = "none"
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use_gradient_checkpointing = True, # @@@ IF YOU GET OUT OF MEMORY - set to True @@@
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random_state = 3407,
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use_rslora = False, # We support rank stabilized LoRA
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loftq_config = None, # And LoftQ
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)
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alpaca_prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Input:
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{}
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### Output:
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{}"""
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EOS_TOKEN = tokenizer.eos_token
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def formatting_prompts_func(examples):
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inputs = examples["problem"]
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outputs = examples["solution"]
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texts = []
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for input, output in zip(inputs, outputs):
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# Must add EOS_TOKEN, otherwise your generation will go on forever!
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text = alpaca_prompt.format(input, output) + EOS_TOKEN
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texts.append(text)
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return { "text" : texts}
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pass
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from datasets import load_dataset
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dataset = load_dataset('json', data_files='/content/drive/MyDrive/data-oss_instruct-py-10.jsonl', split='train')
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dataset = dataset.map(formatting_prompts_func, batched=True)
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from trl import SFTTrainer
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from transformers import TrainingArguments
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from unsloth import is_bfloat16_supported
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from transformers.utils import logging
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logging.set_verbosity_info()
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trainer = SFTTrainer(
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model = model,
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tokenizer = tokenizer,
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train_dataset = dataset,
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dataset_text_field = "text",
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max_seq_length = max_seq_length,
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dataset_num_proc = 2,
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packing = True, # Packs short sequences together to save time!
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args = TrainingArguments(
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per_device_train_batch_size = 2,
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gradient_accumulation_steps = 256,
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warmup_ratio = 0.1,
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num_train_epochs = 2,
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learning_rate = 2e-4,
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fp16 = not torch.cuda.is_bf16_supported(),
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bf16 = torch.cuda.is_bf16_supported(),
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logging_steps = 1,
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optim = "adafactor", # adamw_torch ou adamw_torch_fused +10% velocidade ou adafactor ou adamw_8bit
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weight_decay = 0.1,
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lr_scheduler_type = "linear",
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seed = 3407,
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output_dir = "outputs",
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),
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)
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trainer_stats = trainer.train()
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model.save_pretrained("lora_model") # Local saving
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tokenizer.save_pretrained("lora_model")
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model.push_to_hub("Ramikan-BR/tinyllama-coder-py-4bit_LORA-v10", token = "hf_...") # Online saving
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tokenizer.push_to_hub("Ramikan-BR/tinyllama-coder-py-4bit_LORA-v10", token = "hf_...") # Online saving
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# Merge to 16bit
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model.save_pretrained_merged("model", tokenizer, save_method = "merged_16bit",)
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model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "merged_16bit", token = "hf_...")
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# Merge to 4bit
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if False: model.save_pretrained_merged("model", tokenizer, save_method = "merged_4bit",)
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if False: model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "merged_4bit", token = "hf_...")
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# Just LoRA adapters
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if False: model.save_pretrained_merged("model", tokenizer, save_method = "lora",)
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if False: model.push_to_hub_merged("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, save_method = "lora", token = "hf_...")
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# Save to 8bit Q8_0
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model.save_pretrained_gguf("model", tokenizer,)
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model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, token = "hf_...")
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# Save to 16bit GGUF
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model.save_pretrained_gguf("model", tokenizer, quantization_method = "f16")
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model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "f16", token = "hf_...")
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# Save to q4_k_m GGUF
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model.save_pretrained_gguf("model", tokenizer, quantization_method = "q4_k_m")
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model.push_to_hub_gguf("Ramikan-BR/tinyllama-coder-py-4bit-v10", tokenizer, quantization_method = "q4_k_m", token = "hf_...")
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Loss for 5 epochs in the last training session of the last part of the dataset:
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==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1
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\\ /| Num examples = 407 | Num Epochs = 5
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O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 256
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\ / Total batch size = 512 | Total steps = 5
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"-____-" Number of trainable parameters = 201,850,880
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[5/5 29:36, Epoch 3/5]
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Step Training Loss
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1 0.568000
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2 0.145300
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3 0.506100
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4 0.331900
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5 0.276100
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Quick test 1 after training the last part of the dataset:
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# alpaca_prompt = Copied from above
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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inputs = tokenizer(
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[
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alpaca_prompt.format(
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"Continue the fibonnaci sequence.", # instruction
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"1, 1, 2, 3, 5, 8", # input
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"", # output - leave this blank for generation!
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)
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], return_tensors = "pt").to("cuda")
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AI Response: ['<s> Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Input:\nContinue the fibonnaci sequence.\n\n### Output:\n1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 420, 787, 1444, 2881, 4765, 8640']
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Quick test 2 after training the last part of the dataset:
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# alpaca_prompt = Copied from above
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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inputs = tokenizer(
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[
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alpaca_prompt.format(
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"Continue the fibonnaci sequence.", # instruction
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"1, 1, 2, 3, 5, 8", # input
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"", # output - leave this blank for generation!
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)
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], return_tensors = "pt").to("cuda")
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from transformers import TextStreamer
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text_streamer = TextStreamer(tokenizer)
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_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)
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AI Response: <s> Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Input:
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Continue the fibonnaci sequence.
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### Output:
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1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 420, 787, 1444, 2881, 4765, 8640, 17281, 31362, 65325, 128672, 251345, 410000, 720000, 1280000,
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Quick test 3 after training the last part of the dataset:
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if False:
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from unsloth import FastLanguageModel
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "lora_model", # YOUR MODEL YOU USED FOR TRAINING
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max_seq_length = max_seq_length,
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dtype = dtype,
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load_in_4bit = load_in_4bit,
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)
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FastLanguageModel.for_inference(model) # Enable native 2x faster inference
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# alpaca_prompt = You MUST copy from above!
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inputs = tokenizer(
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[
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alpaca_prompt.format(
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"What is a famous tall tower in Paris?", # instruction
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"", # input
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"", # output - leave this blank for generation!
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)
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], return_tensors = "pt").to("cuda")
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from transformers import TextStreamer
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text_streamer = TextStreamer(tokenizer)
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_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 64)
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AI Response: <s> Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Input:
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What is a famous tall tower in Paris?
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### Output:
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The famous tall tower in Paris is the Eiffel Tower. It is a 300-meter-tall steel tower located in the heart of Paris, France. The tower was built in 18892 and is a popular tourist attraction. It is also a symbol of the city
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outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
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tokenizer.batch_decode(outputs)
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```
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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