Datasets:

VIRUS374
/

ai-developer-dataset

---
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:
```json
{
  "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

```python
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)

```python
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
```bibtex
@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}
}
```

	---
	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:

	```json
	{
	"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

	```python
	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)

	```python
	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

	```bibtex
	@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}
	}
	```