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Model: Ramikan-BR/tinyllama-coder-py-4bit-v10 Source: Original Platform
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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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config.json
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config.json
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{
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"_name_or_path": "unsloth/tinyllama-chat-bnb-4bit",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5632,
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"max_position_embeddings": 4096,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 22,
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"num_key_value_heads": 4,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 2.0,
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"type": "linear"
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},
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.40.2",
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"unsloth_version": "2024.5",
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"use_cache": true,
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"vocab_size": 32000
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 1,
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"eos_token_id": 2,
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"max_length": 2048,
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"pad_token_id": 0,
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"transformers_version": "4.40.2"
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}
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3
model.safetensors
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3
model.safetensors
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version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0faa579c809df42b6c4cafae313cd1918fd83ebfcee169d466ec86dc40c589c6
|
||||
size 2200119664
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tinyllama-coder-py-4bit-v10-unsloth.F16.gguf
Normal file
3
tinyllama-coder-py-4bit-v10-unsloth.F16.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0035158417da031798c0641b335ddab3e2a06fca2ceaaaf9d4cf9d9f40039e64
|
||||
size 2201017472
|
||||
3
tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf
Normal file
3
tinyllama-coder-py-4bit-v10-unsloth.Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bd2cce05baa5e92e0c25c2f6ae201910c7ec94cc722e47719e2f7e8c79d24357
|
||||
size 667815104
|
||||
3
tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf
Normal file
3
tinyllama-coder-py-4bit-v10-unsloth.Q8_0.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8864d14d884d96ec0b4398ef33e8b6ee732cea4f103d9caaad295a05217b6648
|
||||
size 1169808512
|
||||
93392
tokenizer.json
Normal file
93392
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||
size 499723
|
||||
42
tokenizer_config.json
Normal file
42
tokenizer_config.json
Normal file
@@ -0,0 +1,42 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": false,
|
||||
"model_max_length": 4096,
|
||||
"pad_token": "<unk>",
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user