adityawakharkar/AstraGPTCoder-7B

AstraGPT-7B 🚀

A 7-Billion Parameter Language Model — Built From Scratch

Custom Architecture · Custom BPE Tokenizer · Reasoning Fine-Tuned on Dual RTX 4090

Built by Aditya Wakharkar | Tantra AI Labs

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🧠 What is AstraGPT-7B?

AstraGPT-7B is a 7-billion parameter decoder-only language model designed for coding and chain-of-thought reasoning.

Unlike most open-source fine-tunes, every core component of AstraGPT was designed and implemented from scratch in PyTorch — including the transformer architecture, the BPE tokenizer, and the supervised fine-tuning pipeline.

The model was then fine-tuned on a reasoning dataset using LoRA on a private VPS equipped with dual NVIDIA RTX 4090 GPUs, giving it native support for <think>...</think> style reasoning output.

"Most people fine-tune models. We built one."

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🏗️ Built From Scratch — Architecture Overview

Every layer of AstraGPT-7B was implemented from first principles in PyTorch. No AutoModel, no copy-paste — pure custom code.

Input Token IDs
      │
      ▼
Token Embedding  [64,000 → 4,096]
      │
      ▼  ×32 Transformer Blocks
┌─────────────────────────────────────┐
│           AstraGPT Block            │
│                                     │
│  RMSNorm (Pre-norm)                 │
│  → Grouped Query Attention (GQA)    │
│    · 32 Query Heads                 │
│    · 8 Key-Value Heads              │
│    · RoPE (θ = 1,000,000)           │
│    · KV Cache for inference         │
│  → Residual Add                     │
│                                     │
│  RMSNorm (Pre-norm)                 │
│  → SwiGLU Feed-Forward Network      │
│    · gate_proj, up_proj, down_proj  │
│    · intermediate_size = 11,008     │
│  → Residual Add                     │
└─────────────────────────────────────┘
      │
      ▼
Final RMSNorm
      │
      ▼
LM Head  [4,096 → 64,000]
      │
      ▼
Logits → Next Token

Architecture Highlights

Component	Implementation	Why
Grouped Query Attention (GQA)	32Q / 8KV heads — built from scratch	4× less KV memory vs MHA. Same used in LLaMA-3, Mistral
Rotary Position Embeddings (RoPE)	Full RoPE math from scratch, θ=1M	Better long-context vs learned embeddings
SwiGLU FFN	gate × SiLU(up) through down_proj	Outperforms GELU/ReLU on LM benchmarks
RMSNorm	Pre-norm, no bias, no mean subtraction	~30% faster than LayerNorm
Flash Attention	PyTorch 2.0 scaled_dot_product_attention	Memory-efficient attention with O(n) space

Parameter Count (~7B)

Component	Parameters
Token Embedding (64K × 4096)	~262M
Attention × 32 layers	~2.15B
SwiGLU FFN × 32 layers	~4.32B
RMSNorm × 65	~267K
LM Head	~262M
Total	~7.0B

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🔤 Custom BPE Tokenizer — From Scratch

AstraGPT uses a custom Byte Pair Encoding tokenizer built entirely from scratch — no SentencePiece, no HuggingFace tokenizers library.

# Built from scratch
from tokenizer import BPETokenizer

tok = BPETokenizer(vocab_size=64_000)
tok.train(open("corpus.txt"), num_merges=60_000)

Tokenizer features:

• Byte-level base vocabulary — 256 raw bytes, handles any Unicode
• GPT-4 style pre-tokenization regex — smart word boundary splitting
• 64,000 vocab size — 60K BPE merges on top of byte base
• Built-in special tokens: <think>, </think>, <|im_start|>, <|im_end|>, BOS, EOS, PAD
• apply_chat_template() — custom chat format support
• Save/load — JSON-serializable merge rules

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⚡ Training — Dual RTX 4090 on Private VPS

Fine-tuning was performed on a private Linux VPS with 2× NVIDIA RTX 4090 GPUs (total 48GB VRAM).

Hardware Setup

Spec	Value
GPUs	2× NVIDIA RTX 4090 (24GB VRAM each)
Total VRAM	48 GB
CPU	High-core count server CPU
Infrastructure	Private VPS (bare metal)
OS	Ubuntu 22.04 LTS
CUDA	12.x

Training Pipeline — Also Built From Scratch

The SFT (Supervised Fine-Tuning) training loop was implemented from scratch with production-grade features:

# Full custom training loop
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    dataset=dataset,
    # Dual GPU via DDP
    use_bf16=True,
    grad_accumulation=8,
    learning_rate=2e-4,
    use_wandb=True,
)
trainer.train()

Training loop features:

• ✅ Gradient accumulation — effective large batch training
• ✅ Mixed precision (BF16) — full RTX 4090 tensor core utilization
• ✅ Cosine LR schedule with warmup — smooth convergence
• ✅ Gradient clipping — stable training
• ✅ W&B logging — real-time loss/LR tracking
• ✅ Checkpoint saving — best model tracking by loss

Fine-Tuning Hyperparameters

Parameter	Value
Method	LoRA (PEFT) via Unsloth
LoRA Rank	16
LoRA Alpha	32
Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Max Sequence Length	2,048 tokens
Effective Batch Size	16 (2 × grad_accum 8)
Learning Rate	2e-4
LR Scheduler	Cosine with warmup
Warmup Ratio	5%
Epochs	3
Precision	BF16 mixed precision
Optimizer	AdamW 8-bit

Post-Training

After fine-tuning, the LoRA adapter was merged back into base model weights — resulting in a single, self-contained model with no external adapter dependency.

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🤔 Thinking / Reasoning Support

AstraGPT-7B natively generates <think> tag reasoning when triggered. This was trained in via the fine-tuning dataset, which used structured chain-of-thought formatting.

Example:

Input:

What is 15 * 47?

Output:

<think>
The multiplication involves multiplying 15 by 47.
  15 × 47 = 15 × 40 + 15 × 7
          = 600 + 105
          = 705
</think>
705

Trigger thinking mode:

# Append this to your prompt to force reasoning
prompt = tokenizer.apply_chat_template(messages, ...) + "<think>\n"

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⚡ Quick Start

Install

pip install transformers torch bitsandbytes accelerate

Basic Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "adityawakharkar/AstraGPT-7B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [
    {
        "role": "system",
        "content": "You are AstraGPT, a helpful coding AI built by Tantra AI Labs. Think carefully using <think>...</think> tags before answering."
    },
    {
        "role": "user",
        "content": "Write a Python function to reverse a linked list."
    }
]

prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
) + "<think>\n"   # ← triggers reasoning

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=1024,
        temperature=0.3,
        do_sample=True,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(
    output[0][inputs["input_ids"].shape[1]:],
    skip_special_tokens=True
)
print(response)

4-bit Quantized (Runs on ~6GB VRAM)

from transformers import BitsAndBytesConfig

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    "adityawakharkar/AstraGPT-7B",
    quantization_config=bnb,
    device_map="auto"
)

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📁 Codebase

The full from-scratch implementation is open-source:

AstraGPT-7B-scratch/
├── model/
│   ├── config.py              ← AstraGPTConfig (7B hyperparams, 1B/3B presets)
│   ├── rotary_embedding.py    ← RoPE from scratch (precompute + apply)
│   ├── attention.py           ← GQA from scratch (32Q / 8KV + KV cache)
│   ├── feedforward.py         ← SwiGLU + RMSNorm + TransformerBlock
│   └── transformer.py         ← Full model + generate() + save/load
├── tokenizer/
│   ├── bpe_tokenizer.py       ← Full BPE tokenizer (train, encode, decode)
│   └── train_tokenizer.py     ← Train on any text corpus
└── training/
    └── sft_trainer.py         ← Complete SFT loop (grad accum, bf16, cosine LR)

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Bias, Risks, and Limitations

• Hallucination: Can produce confident but incorrect answers — always verify
• Math limits: Complex multi-step math may fail — 7B is a small model
• English-primary: Best performance in English
• Reasoning trigger: <think> tags work most reliably with explicit <think>\n prefix in prompt

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Environmental Impact

• Hardware: 2× NVIDIA RTX 4090 (48GB combined VRAM)
• Infrastructure: Private bare-metal VPS
• Training Duration: ~3–4 hours
• Carbon Emitted: Estimated ~2–3 kgCO2eq

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Citation

@misc{astragpt7b2026,
  author       = {Aditya Wakharkar},
  title        = {AstraGPT-7B: A 7B LLM Built From Scratch with Chain-of-Thought Reasoning},
  year         = {2026},
  publisher    = {HuggingFace},
  organization = {Tantra AI Labs},
  url          = {https://huggingface.co/adityawakharkar/AstraGPT-7B},
  note         = {Custom architecture, custom BPE tokenizer, trained on 2× RTX 4090}
}

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Model Card Authors

Aditya Wakharkar — @adityawakharkar | GitHub @codewith-aditya

Contact

• 🐙 GitHub: github.com/codewith-aditya
• 🤗 HuggingFace: @adityawakharkar

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Built from scratch with ❤️ by Tantra AI Labs
Every layer. Every weight. Every line of code.