Instructions to use AjayRangoji/AR-4B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AjayRangoji/AR-4B-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AjayRangoji/AR-4B-base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("AjayRangoji/AR-4B-base", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AjayRangoji/AR-4B-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AjayRangoji/AR-4B-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AjayRangoji/AR-4B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AjayRangoji/AR-4B-base

SGLang

How to use AjayRangoji/AR-4B-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AjayRangoji/AR-4B-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AjayRangoji/AR-4B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AjayRangoji/AR-4B-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AjayRangoji/AR-4B-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AjayRangoji/AR-4B-base with Docker Model Runner:
```
docker model run hf.co/AjayRangoji/AR-4B-base
```

AR-4B (base)

AR-4B is a 4.02B-parameter decoder-only transformer, pretrained from scratch on about 325B tokens of English, code and math. It's a base model: no instruction tuning or RLHF, so it continues text rather than following chat instructions.

Parameters: 4.02B (36 layers, hidden size 2,560, grouped-query attention, SwiGLU MLP)
Context length: 4,096 during pretraining (position encoding supports longer)
Pretraining tokens: ~325B (Stage 1 ~300B general + Stage 2 ~25B math/code anneal)
Precision: bf16

Load the model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

name = "AjayRangoji/AR-4B-base"
tok = AutoTokenizer.from_pretrained(name)
model = AutoModelForCausalLM.from_pretrained(
    name, torch_dtype=torch.bfloat16, device_map="auto",
    trust_remote_code=True,                                   # loads modeling_ar.py from this repo
)

prompt = "The quickest way to sort a list in Python is"
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tok.decode(out[0], skip_special_tokens=True))

trust_remote_code=True is required because the repo ships its own small modeling_ar.py.

Architecture


Layers	36 decoder blocks, pre-norm, residual
Hidden size	2,560
Attention	Grouped-query: 32 query heads, 8 key/value heads, head dim 128
Attention stability	RMSNorm on each head's Q and K (QK-norm), before RoPE
Positions	RoPE, base theta = 1,000,000
MLP	SwiGLU, inner size 9,728
Normalization	RMSNorm (eps = 1e-6), computed in fp32
Embeddings	Input embedding tied to output head (saves 389M parameters)
Vocabulary	151,936 BPE tokens
Document separator	`<

Forward pass: token embedding -> 36 x [RMSNorm -> GQA (QK-norm, RoPE) -> add residual -> RMSNorm -> SwiGLU -> add residual] -> final RMSNorm -> tied output head -> 151,936 next-token scores per position.

Training

Pure data parallelism with PyTorch FSDP2, bf16 compute / fp32 reductions, activation checkpointing on every block, FlexAttention with a causal + same-document mask.

	Stage 1: general	Stage 2: reasoning anneal
Steps	0 - 572,000	572,000 - 619,699
Tokens	~300B	~25B
English	65% FineWeb-Edu (191B unique)	20% FineWeb-Edu, int_score >= 4 only
Textbooks	-	10% Cosmopedia v2
Code	20% The Stack (dedup), 9 languages	30% same code
Math	15% FineMath 4+ / 3+	40% FineMath 4+, InfiWebMath 4+, FineMath 3+
LR	warmup 2,000 -> 3e-4 -> cosine -> 3e-5	warmup 1,000 -> 1e-4 -> cosine -> 1e-5

Batch: 524,288 tokens per optimizer step (64 GPUs x 2 sequences x 4,096 tokens).
Optimizer: AdamW, beta = (0.9, 0.95), weight decay 0.1, gradient clip 1.0.
Hardware: Up to 8 nodes x 8 H100 80GB (64 GPUs) on an AWS cluster, EFA network, Lustre storage.
Throughput: about 500K tokens/s on 64 GPUs (~22% MFU).

Evaluation (base model, no instruction tuning)

Held-out loss on data the model never trained on; HumanEval and GSM8K are standard benchmarks for 4B base models.

Metric	Stage 1 final	Stage 2 final
Python held-out loss (lower is better)	0.966	0.929
English held-out loss (lower is better)	2.220	2.300
HumanEval pass@1 (greedy)	18.9% (31/164)	25.0% (41/164)
GSM8K 8-shot exact match	31.6% (79/250)	43.6% (109/250)

As expected from the Stage 2 mix (40% math, 30% code), reasoning benchmarks improved substantially while general English loss moved slightly up.

Tokenizer

Uses the Qwen3 BPE tokenizer (Qwen/Qwen3-4B) - 151,936 vocab, strong on English, code and math. Credit to the tokenizer's authors. Document separator in training: <|im_end|> (id 151645).

Intended use and limitations

Base model for continued pretraining, SFT, RLHF, or distillation. Not a chat model.
No safety training. Outputs may be factually wrong, biased or unsafe. Review and filter before any downstream use.
English-dominant data. Behavior in other languages is likely weaker, even where the tokenizer handles them.
4,096-token context in pretraining; longer contexts work mechanically through RoPE but have not been trained.
Not evaluated on standard safety benchmarks.

Data sources

FineWeb-Edu - HuggingFaceFW/fineweb-edu
FineMath 4+ / 3+ and InfiWebMath 4+ - HuggingFaceTB/finemath
Cosmopedia v2 - HuggingFaceTB/smollm-corpus
The Stack (dedup) - bigcode/the-stack-dedup (Python, JavaScript, Java, C++, TypeScript, Go, Rust, Shell, SQL)

Each dataset keeps its original license; see the links above.

License

Model weights and code: Apache 2.0. The tokenizer's license is the one it ships with.

Downloads last month: 212

Safetensors

Model size

4B params

Tensor type

BF16

AjayRangoji
/

AR-4B-base