--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [h2oai/h2o-danube3-500m-chat](https://huggingface.co/h2oai/h2o-danube3-500m-chat) - Fine-tuning dataset: [zakariarada/oasst](https://huggingface.co/datasets/zakariarada/oasst) ## Training To train the model using your custom dataset, you can follow the steps below. This example demonstrates how to fine-tune the `h2oai/h2o-danube3-500m-chat` model using the Hugging Face `transformers` library. ### Code Example ```python import pandas as pd from transformers import ( AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer ) from datasets import Dataset # Load Dataset data_path = "train_full.pq" df = pd.read_parquet(data_path) # Prepare Dataset for Training dataset = Dataset.from_pandas(df) def preprocess_function(examples): # Combine 'instruction' and 'parent_id' as input prompt instruction = examples["instruction"] parent_id = examples["parent_id"] input_prompt = f"{parent_id}: {instruction}" if parent_id else instruction return { "input_text": input_prompt, "target_text": examples["output"] } # Preprocess Dataset dataset = dataset.map(preprocess_function, remove_columns=["id", "parent_id", "instruction", "output"]) # Load Tokenizer and Model model_name = "h2oai/h2o-danube3-500m-chat" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Tokenize Data def tokenize_function(examples): return tokenizer( examples["input_text"], padding="max_length", truncation=True, max_length=512 ) tokenized_dataset = dataset.map(tokenize_function, batched=True) # Training Arguments training_args = TrainingArguments( output_dir="./output/TCLM-beta/", # Directory to save model checkpoints num_train_epochs=3, # Increase epochs for better fine-tuning results per_device_train_batch_size=4, # Adjust based on GPU memory, increase if possible gradient_accumulation_steps=4, # Accumulate gradients to simulate a larger batch size evaluation_strategy="steps", # Evaluate more frequently for detailed tracking eval_steps=500, # Evaluate every 500 steps to track progress without over-evaluating save_strategy="steps", # Save checkpoints during training save_steps=500, # Save model every 500 steps save_total_limit=2, # Limit to the two best models to save disk space learning_rate=5e-5, # Lower learning rate for fine-tuning weight_decay=0.01, # Slight weight decay to prevent overfitting lr_scheduler_type="cosine", # Cosine schedule for smoother learning rate decay warmup_ratio=0.06, # Warmup to stabilize initial training logging_dir="./logs", # Directory to save training logs logging_steps=50, # Log progress every 50 steps for better monitoring fp16=True, # Enable mixed precision for faster training with less memory load_best_model_at_end=True, # Load the best model at the end based on evaluation metric metric_for_best_model="eval_loss", # Use evaluation loss to determine the best model greater_is_better=False, # Lower loss is better ) # Trainer Setup trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_dataset, tokenizer=tokenizer, ) # Train Model trainer.train() ``` ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.45.0 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login() ``` - Or directly pass your to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="zakariarada/TCLM-beta", torch_dtype="auto", trust_remote_code=True, device_map={"": "cuda:0"}, token=True, ) # generate configuration can be modified to your needs # generate_text.model.generation_config.min_new_tokens = 2 # generate_text.model.generation_config.max_new_tokens = 256 # generate_text.model.generation_config.do_sample = False # generate_text.model.generation_config.num_beams = 1 # generate_text.model.generation_config.temperature = float(0.0) # generate_text.model.generation_config.repetition_penalty = float(1.0) messages = [ {"role": "user", "content": "Hi, how are you?"}, {"role": "assistant", "content": "I'm doing great, how about you?"}, {"role": "user", "content": "Why is drinking water so healthy?"}, ] res = generate_text( messages, renormalize_logits=True ) print(res[0]["generated_text"][-1]['content']) ``` You can print a sample prompt after applying chat template to see how it is feed to the tokenizer: ```python print(generate_text.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, )) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "zakariarada/TCLM-beta" # either local folder or Hugging Face model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. messages = [ {"role": "user", "content": "Hi, how are you?"}, {"role": "assistant", "content": "I'm doing great, how about you?"}, {"role": "user", "content": "Why is drinking water so healthy?"}, ] tokenizer = AutoTokenizer.from_pretrained( model_name, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() # generate configuration can be modified to your needs # model.generation_config.min_new_tokens = 2 # model.generation_config.max_new_tokens = 256 # model.generation_config.do_sample = False # model.generation_config.num_beams = 1 # model.generation_config.temperature = float(0.0) # model.generation_config.repetition_penalty = float(1.0) inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True, ).to("cuda") tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 1536, padding_idx=0) (layers): ModuleList( (0-15): 16 x LlamaDecoderLayer( (self_attn): LlamaSdpaAttention( (q_proj): Linear(in_features=1536, out_features=1536, bias=False) (k_proj): Linear(in_features=1536, out_features=768, bias=False) (v_proj): Linear(in_features=1536, out_features=768, bias=False) (o_proj): Linear(in_features=1536, out_features=1536, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=1536, out_features=4096, bias=False) (up_proj): Linear(in_features=1536, out_features=4096, bias=False) (down_proj): Linear(in_features=4096, out_features=1536, bias=False) (act_fn): SiLU() ) (input_layernorm): LlamaRMSNorm((1536,), eps=1e-05) (post_attention_layernorm): LlamaRMSNorm((1536,), eps=1e-05) ) ) (norm): LlamaRMSNorm((1536,), eps=1e-05) (rotary_emb): LlamaRotaryEmbedding() ) (lm_head): Linear(in_features=1536, out_features=32000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. 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