MAISAAI
/

gemma-2b-coder

@@ -1,201 +1,143 @@
 ---
-library_name: transformers
-tags: []
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-# Model Card for Model ID
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 ---
+tags:
+- generated_from_trainer
+- code
+- coding
+- llama
+model-index:
+- name: gemma-2b-coder
+  results: []
+license: apache-2.0
+language:
+- code
+thumbnail: https://huggingface.co/mrm8488/gemma-2b-coder/resolve/main/logo.png
+datasets:
+- HuggingFaceH4/CodeAlpaca_20K
+pipeline_tag: text-generation
 ---
+<div style="text-align:center;width:250px;height:250px;">
+    <img src="https://huggingface.co/mrm8488/gemma-2b-coder/resolve/main/logo.png" alt="gemma coder logo"">
+</div>
+# Gemma Coder 🦙👩‍💻
+**Gemma 2B** fine-tuned on the **CodeAlpaca 20k instructions dataset** by using the method **QLoRA** with [PEFT](https://github.com/huggingface/peft) library.
+## Model description 🧠
+[Gemma-2b](https://huggingface.co/google/gemma-2b)
+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.
+## Training and evaluation data 📚
+[CodeAlpaca_20K](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K): contains 20K instruction-following data used for fine-tuning the Code Alpaca model.
+### Training hyperparameters ⚙
+Training took 1h 40 min on Free Colab T4 GPU (16GB VRAM) with the following params:
+```py
+num_train_epochs=2,
+per_device_train_batch_size=2,
+per_device_eval_batch_size=1,
+gradient_accumulation_steps=32
+learning_rate=2.5e-5,
+optim="paged_adamw_8bit",
+logging_steps=5,
+seed=66,
+load_best_model_at_end=True,
+save_strategy="steps",
+save_steps=50,
+evaluation_strategy="steps",
+eval_steps=50,
+save_total_limit=2,
+remove_unused_columns=True,
+fp16=True,
+bf16=False
+```
+### Training results 🗒️
+| Step | Training Loss | Validation Loss |
+|------|---------------|-----------------|
+| 50   | 1.467800      | 1.450770        |
+| 100  | 1.060000      | 1.064840        |
+| 150  | 0.900200      | 0.922290        |
+| 200  | 0.848400      | 0.879911        |
+| 250  | 0.838100      | 0.867354        |
+### Eval results 📊
+WIP
+### Example of usage 👩‍💻
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+model_id = "mrm8488/llama-2-coder-7b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda")
+def create_prompt(instruction):
+  system = "You are a coding assistant that will help the user to resolve the following instruction:"
+  instruction = "### Instruction: " + instruction
+  return system + "\n" + instruction + "\n\n" + "### Solution:" + "\n"
+def generate(
+        instruction,
+        max_new_tokens=128,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        **kwargs,
+):
+    prompt = create_prompt(instruction)
+    print(prompt)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to("cuda")
+    attention_mask = inputs["attention_mask"].to("cuda")
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+            early_stopping=True
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Solution:")[1].lstrip("\n")
+instruction = """
+Edit the following XML code to add a navigation bar to the top of a web page
+<html>
+<head>
+  <title>Maisa</title>
+</head>
+"""
+print(generate(instruction))
+```
+### Citation
+WIP