README.md

metadata

language:
  - en
  - ru
license:
  - mit
  - apache-2.0
  - cc-by-4.0
  - cc-by-sa-4.0
  - odc-by
task_categories:
  - text-generation
tags:
  - code
  - programming
  - ai-developer
  - fine-tuning
  - instruction-tuning
  - coding
  - web-development
  - debugging
  - llm
  - security
  - sql
  - python
  - javascript
  - typescript
  - react
size_categories:
  - 1M<n<10M

AI Developer Dataset

A large-scale instruction-tuning dataset for fine-tuning an open-weight LLM into a universal AI developer assistant.

The model trained on this dataset should be especially good at: PROGRAMMING + WEB DEVELOPMENT + UI/UX + ANIMATIONS + BOTS + SCRIPTS + AUTOMATION + BACKEND + API + DATABASES + DEBUGGING + LINUX + DEPLOYMENT + AI DEVELOPMENT + SECURITY.

📊 Statistics

• Total examples: 1,124,699
• File size: 2.13 GB
• Format: JSONL (conversational)
• Average assistant length: ~1,222 characters
• Schema: {"messages":[{"role":"system|user|assistant","content":"..."}, ...], "metadata":{"category","subcategory","complexity","source"}}

📚 Sources

This dataset is a curated merge of high-quality open-source code instruction datasets from HuggingFace, plus handcrafted examples in Russian covering UI/UX, security, and other specialized topics.

Source	Examples
hf:glaiveai/glaive-code-assistant-v2	213,670
hf:glaiveai/glaive-code-assistant	135,195
hf:mwitiderrick/glaive-code-assistant	104,336
hf:b-mc2/sql-create-context	78,573
hf:TokenBender/code_instructions_122k_alpaca_style	78,433
hf:iamtarun/code_instructions_120k_alpaca	78,433
hf:ajibawa-2023/Code-74k-ShareGPT	68,790
hf:Locutusque/code-feedback-sharegpt	62,866
hf:m-a-p/Code-Feedback	62,866
hf:HuggingFaceH4/Code-Feedback	61,923
hf:WizardLMTeam/WizardLM_evol_instruct_70k	60,411
hf:nickrosh/Evol-Instruct-Code-80k-v1	58,265
hf:Data-Agora/magpie_code_gpt4o_mini_50000	36,818
hf:ajibawa-2023/Python-Code-23k-ShareGPT	17,737
hf:HuggingFaceH4/CodeAlpaca_20K	6,307
handcrafted	76

📁 Categories

Category	Examples	%
programming	1,046,060	93.0%
backend_api_db	78,579	7.0%
ui_ux_design	10	0.0%
web_development	9	0.0%
bots_automation	8	0.0%
security	8	0.0%
ai_ml	7	0.0%
debugging	6	0.0%
linux_server_deploy	6	0.0%
web_search_docs	6	0.0%

🎯 Complexity Distribution

Level	Examples	%
intermediate	686,519	61.0%
advanced	438,166	39.0%
beginner	12	0.0%
expert	2	0.0%

📋 Schema

Each line is a single JSON object:

{
  "messages": [
    {"role": "system", "content": "Category: programming\nSubcategory: code_alpaca\n..."},
    {"role": "user", "content": "Write a Python function to..."},
    {"role": "assistant", "content": "Here is the implementation..."}
  ],
  "metadata": {
    "category": "programming",
    "subcategory": "code_alpaca",
    "complexity": "intermediate",
    "source": "hf:HuggingFaceH4/CodeAlpaca_20K"
  }
}

🛠️ Quality Control

Every example passes:

• JSON-serializable (validated on 100% of examples)
• Non-empty user and assistant content
• Assistant length between 200 and 25,000 characters
• Per-source deduplication by user-content fingerprint (SHA-256, first 16 hex chars)
• Schema validation (messages + metadata required)

🚀 Usage

from datasets import load_dataset

# Load the full dataset
ds = load_dataset("VIRUS374/ai-developer-dataset", data_files="dataset.jsonl", split="train")
print(ds)
# Dataset({
#     features: ["messages", "metadata"],
#     num_rows: 1,124,699
# })

# Or use streaming for memory-efficient iteration
ds = load_dataset("VIRUS374/ai-developer-dataset", data_files="dataset.jsonl", split="train", streaming=True)
for example in ds:
    messages = example["messages"]
    # ... feed to tokenizer with chat template

🏋️ Fine-tuning Recipe (QLoRA)

from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from trl import SFTTrainer
from datasets import load_dataset
import torch

MODEL = "meta-llama/Llama-3.1-8B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    MODEL,
    torch_dtype=torch.bfloat16,
    load_in_4bit=True,
    device_map="auto",
)
model = prepare_model_for_kbit_training(model)

lora_config = LoraConfig(
    r=16, lora_alpha=32,
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
    lora_dropout=0.05, bias="none", task_type="CAUSAL_LM",
)
model = get_peft_model(model, lora_config)

ds = load_dataset("VIRUS374/ai-developer-dataset", data_files="dataset.jsonl", split="train")

def format_example(example):
    text = tokenizer.apply_chat_template(
        example["messages"], tokenize=False, add_generation_prompt=False,
    )
    return {"text": text}

ds = ds.map(format_example, remove_columns=ds.column_names)

trainer = SFTTrainer(
    model=model,
    train_dataset=ds,
    args=TrainingArguments(
        output_dir="./output",
        num_train_epochs=3,
        per_device_train_batch_size=2,
        gradient_accumulation_steps=8,
        warmup_steps=50,
        learning_rate=2e-4,
        bf16=True,
        logging_steps=10,
        save_steps=500,
    ),
    max_seq_length=4096,
)
trainer.train()

📜 License & Attribution

This dataset aggregates multiple open-source datasets. Each retains its original license:

• HuggingFaceH4/CodeAlpaca_20K — Apache 2.0
• HuggingFaceH4/Code-Feedback — Apache 2.0
• nickrosh/Evol-Instruct-Code-80k-v1 — Apache 2.0
• WizardLMTeam/WizardLM_evol_instruct_70k — Apache 2.0 (Non-commercial)
• TokenBender/code_instructions_122k_alpaca_style — Apache 2.0
• iamtarun/code_instructions_120k_alpaca — Apache 2.0
• m-a-p/Code-Feedback — CC BY-NC 4.0
• Locutusque/code-feedback-sharegpt — Apache 2.0
• ajibawa-2023/Code-74k-ShareGPT — Apache 2.0
• ajibawa-2023/Python-Code-23k-ShareGPT — Apache 2.0
• b-mc2/sql-create-context — CC BY 4.0
• glaiveai/glaive-code-assistant — Apache 2.0
• glaiveai/glaive-code-assistant-v2 — Apache 2.0
• mwitiderrick/glaive-code-assistant — Apache 2.0
• Data-Agora/magpie_code_gpt4o_mini_50000 — Apache 2.0
• Handcrafted examples (VIRUS374) — MIT

Important: If you use this dataset for commercial purposes, please review the WizardLM and m-a-p licenses — they may have non-commercial restrictions.

🙏 Acknowledgements

Thanks to all the original dataset creators:

• HuggingFace H4 team
• WizardLM team
• TokenBender
• iamtarun
• m-a-p
• Locutusque
• ajibawa-2023
• b-mc2
• glaiveai
• mwitiderrick
• Data-Agora

📝 Citation

@dataset{virus374_ai_developer_2024,
  title  = {AI Developer Dataset},
  author = {VIRUS374},
  year   = {2024},
  url    = {https://huggingface.co/datasets/VIRUS374/ai-developer-dataset},
  note   = {Aggregated from open-source code instruction datasets + handcrafted examples}
}