AnuLM-Base-2K-400M

Model card: AnuLM-Base-2K-400M (ckpt_ctx2k.pt)

The three-language base with a 2,048-token context: AnuLM-Base-400M continued for 10,000 steps at block 2,048 with YaRN, on 46M tokens of the same Hindi / English / Python proportions it was originally trained on. Five hours on one RTX 5070 Ti. Full log and the honest reading of what it bought: docs/RESULTS.md §28, with the zero-shot measurement it is compared against in §27.

Licence: CC BY-SA 4.0, the same as the base and for the same reason: Hindi and English Wikipedia are CC BY-SA, C4 is ODC-BY, and the Python slice is codeparrot-clean, de-duplicated GitHub Python with mixed licences. Not affiliated with Sarvam AI, AI4Bharat, BharatGen or the Government of India.

Why this exists

Every other checkpoint in this project is trained at 512 tokens. This one answers what happens if you train at four times that with the architecture's own long-context mechanism switched on — and the answer, stated plainly because it is the interesting part, is less than you would hope:

A model that gets worse the further into the window it goes is failing to extend; the sign of that last column is the whole question. Zero-shot YaRN already flips it, for free. Five GPU-hours of training moved it from −0.068 to −0.085. If all you want is sane behaviour at 2,048 tokens, apply YaRN to AnuLM-Base-400M and skip this model.

What the training did buy is general quality, because it is 82M tokens on top of the base's 148M — 55% more compute:

Nothing regressed in any language, which is the usual risk of a continuation. Python gains most because the base saw the least of it.

What it does

Continues text in Hindi, English or Python, in the register it is given, over a window four times longer than any other checkpoint here. It is a base model: not tuned to answer questions, follow instructions, chat or translate. Everything factual in its output is invented.

What it sounds like

Better compression did not make it a better writer. Six prompts across the three languages, temperature 0.8, top-k 50, 120 tokens each:

More varied by one measure, the same by the other. Both degenerate: a third of their 4-grams are repeats, which is what a 398M base model does when asked to free-run. Greedy decoding is worse for both. Samples from this model, at temperature 0.8:

मुंबई          -> बिग साइड (Red Dock) एक ऐसा एक प्रकार के साइड है, जो एक ही साइड के
                  एक साथ काम करता है। यह साइड सबसे पहले 1880 में सामने आया।
The history of -> microscopic computers, which are part of the CuOi and are used for
computing         the development of a complex network of computers.
def quicksort  -> x = x - 1 / y = y - 1 / y = X - y - 1 / y = X - y - 1 / ...
(arr):

Fluent-shaped, factually invented, and prone to looping — the English holds together longest, the Python loops fastest. Use a repetition penalty and a short generation if you want something readable; serve.py does.

Data

46.0M tokens (306 MB) mixed in the base's own proportions by mix_corpus.py:

mix_corpus.py holds out data/bench_{hindi,english,python}.txt, which is what every number above is measured on. No data is redistributed; docs/DATASETS.md lists each source with its licence, and the exact commands are in docs/RESULTS.md §28.

Training

yarn_original_context is pinned to 512 rather than left at the block size. That is the field docs/ARCHITECTURE.md §4 calls the most-often-mis-set part of a rope_scaling block: the NTK-by-parts correction range has to be measured against the length the model was trained at, not the one it is being extended to.

The selected checkpoint is step 7,499, not 10,000, and the difference is 0.0004 against a standard error of 0.0289 — best-val selection choosing between numbers inside their own noise, exactly as docs/RESULTS.md §26 describes. Treat them as the same model.

Architecture

Identical to every 400M AnuLM checkpoint except the context: 20 layers, hidden 1,024, GQA with 16 query / 4 key-value heads and QK-norm, RoPE θ = 1,000,000, sliding window 256 on layers 0–9, a dense SwiGLU MLP on layer 0 and 24 routed experts of 192 with top-4 routing on layers 1–19, aux-loss-free bias balancing, no shared expert, 32,768-entry vocabulary, untied embeddings. 397.7M parameters, 173.5M active per token. Context 2,048 with YaRN enabled in the config, where the others are 512 without it. Tokenizer multi32k, unchanged from the base.

How to load

With transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("toonist/AnuLM-Base-2K-400M",
                                             trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("toonist/AnuLM-Base-2K-400M")

ids = tok("भारत की राजधानी", return_tensors="pt")
print(tok.decode(model.generate(**ids, max_new_tokens=80, do_sample=False)[0]))

Load it in float32, which the config asks for by default. Do not force dtype=torch.bfloat16: the router keeps a per-expert bias whose differences decide which experts fire, and rounding it to 16 bits changes the routing — the output collapses into repeated tokens rather than degrading. Use torch.autocast over float32 weights for speed.

With the AnuLM repository

hf download toonist/AnuLM-Base-2K-400M --local-dir AnuLM-Base-2K-400M
python serve.py  --ckpt AnuLM-Base-2K-400M
python sample.py --ckpt AnuLM-Base-2K-400M --prompt "हिन्दी साहित्य

"

Limitations

• A base model. No instruction, chat, QA or translation ability. For those, AnuLM-Hindi-QA-400M and AnuLM-Translate-400M.
• The long-context gain is mostly YaRN, not this training. See the table at the top; the honest version of the claim is in docs/RESULTS.md §28.
• Benchmarks flatter it slightly. Its held-out benches are a slice of its own corpus, and a fresh out-of-sample draw for the base it is compared against, though both come from the same public sources.
• It invents facts. Every date, name and number in its output is fiction.
• Trained on a single consumer GPU for a total of about 9 GPU-hours across both stages. It is a 398M model, and reads like one.

How to load

With transformers (the modelling code travels with the weights, so trust_remote_code=True is required and there is nothing to clone):

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("toonist/AnuLM-Base-2K-400M", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("toonist/AnuLM-Base-2K-400M")
ids = tok("def is_prime(n):", return_tensors="pt")
print(tok.decode(model.generate(**ids, max_new_tokens=60)[0]))

Greedy output is identical to this repository's own generate, cached or not; test_model.py holds the two paths together. Batches must be unpadded, and beam search is not supported.

Or with the AnuLM repository, which is what every script here expects:

hf download toonist/AnuLM-Base-2K-400M --local-dir AnuLM-Base-2K-400M
python serve.py  --ckpt AnuLM-Base-2K-400M          # web page at http://127.0.0.1:8000
python sample.py --ckpt AnuLM-Base-2K-400M --prompt "def is_prime(n):"

from model import load_checkpoint, AnuLM
ck = load_checkpoint("AnuLM-Base-2K-400M")
m = AnuLM(ck["cfg"]).eval(); m.load_state_dict(ck["model"])

Weights: model.safetensors, bfloat16, 397.7M parameters. Tokenizer: tokenizer.multi32k.json. Byte-level BPE in this repository's own JSON format (bpe.py), not the tokenizers library format. Load it with bpe.BPE.load(path).