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Model: MAISAAI/gemma-2b-coder Source: Original Platform
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README.md
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---
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tags:
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- generated_from_trainer
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- code
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- coding
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- gemma
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model-index:
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- name: gemma-2b-coder
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results: []
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license_name: gemma-terms-of-use
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license_link: https://ai.google.dev/gemma/terms
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language:
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- code
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thumbnail: https://huggingface.co/mrm8488/gemma-2b-coder/resolve/main/logo.png
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datasets:
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- HuggingFaceH4/CodeAlpaca_20K
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pipeline_tag: text-generation
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---
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<div style="text-align:center;width:250px;height:250px;">
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<img src="https://huggingface.co/mrm8488/gemma-2b-coder/resolve/main/logo.png" alt="gemma coder logo"">
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</div>
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# Gemma Coder 👩💻
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**Gemma 2B** fine-tuned on the **CodeAlpaca 20k instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library.
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## Model description 🧠
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[Gemma-2b](https://huggingface.co/google/gemma-2b)
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Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
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## Training and evaluation data 📚
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[CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K): contains 20K instruction-following data used for fine-tuning the Code Alpaca model.
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### Training hyperparameters ⚙
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Training took 1h 40 min on Free Colab T4 GPU (16GB VRAM) with the following params:
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```py
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num_train_epochs=2,
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per_device_train_batch_size=2,
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per_device_eval_batch_size=1,
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gradient_accumulation_steps=32
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learning_rate=2.5e-5,
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optim="paged_adamw_8bit",
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logging_steps=5,
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seed=66,
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load_best_model_at_end=True,
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save_strategy="steps",
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save_steps=50,
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evaluation_strategy="steps",
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eval_steps=50,
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save_total_limit=2,
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remove_unused_columns=True,
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fp16=True,
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bf16=False
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```
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### Training results 🗒️
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| Step | Training Loss | Validation Loss |
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|------|---------------|-----------------|
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| 50 | 1.467800 | 1.450770 |
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| 100 | 1.060000 | 1.064840 |
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| 150 | 0.900200 | 0.922290 |
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| 200 | 0.848400 | 0.879911 |
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| 250 | 0.838100 | 0.867354 |
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### Eval results 📊
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WIP
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### Example of usage 👩💻
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I recommend install the following version of `torch`:
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```sh
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pip install "torch>=2.1.1" -U
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```
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
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model_id = "MAISAAI/gemma-2b-coder"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
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def generate(
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instruction,
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max_new_tokens=256,
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temperature=0.1,
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top_p=0.75,
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top_k=40,
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num_beams=2,
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**kwargs,
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):
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system = f"<bos><|system|>\nYou are a helpful coding assistant.<eos>\n"
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prompt = f"{system}<|user|>\n{instruction}<eos>\n<|assistant|>\n"
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inputs = tokenizer(prompt, return_tensors="pt")
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input_ids = inputs["input_ids"].to("cuda")
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attention_mask = inputs["attention_mask"].to("cuda")
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generation_config = GenerationConfig(
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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num_beams=num_beams,
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**kwargs,
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)
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with torch.no_grad():
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generation_output = model.generate(
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input_ids=input_ids,
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attention_mask=attention_mask,
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generation_config=generation_config,
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return_dict_in_generate=True,
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max_new_tokens=max_new_tokens,
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early_stopping=True
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)
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s = generation_output.sequences[0]
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output = tokenizer.decode(s, skip_special_tokens=True)
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return output.split("<|assistant|>")[1]
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instruction = """
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Edit the following XML code to add a navigation bar to the top of a web page
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<html>
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<head>
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<title>Maisa</title>
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</head>
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"""
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print(generate(instruction))
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```
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### Citation
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```bibtex
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@misc {maisa_ai_2024,
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author = { {MAISA AI} },
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title = { gemma-2b-coder (Revision e5e4e5b) },
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year = 2024,
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url = { https://huggingface.co/MAISAAI/gemma-2b-coder },
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doi = { 10.57967/hf/2208 },
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publisher = { Hugging Face }
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}
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```
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config.json
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{
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"_name_or_path": "google/gemma-2b",
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"architectures": [
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"GemmaForCausalLM"
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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": 2,
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"eos_token_id": 1,
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"head_dim": 256,
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"hidden_act": "gelu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"max_position_embeddings": 8192,
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"model_type": "gemma",
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"num_attention_heads": 8,
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"num_hidden_layers": 18,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.38.0",
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"use_cache": true,
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"vocab_size": 256000
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}
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generation_config.json
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"_from_model_config": true,
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"eos_token_id": 1,
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"pad_token_id": 0,
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"transformers_version": "4.38.0"
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}
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30
special_tokens_map.json
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30
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Normal file
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3
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3
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}
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||||
Reference in New Issue
Block a user