sfanm

d24-sft-v5-nogsm8k

README

License: other

Lineage

ftajwar/d24-climbmix-100b: pretrained from scratch on about 100B tokens of ClimbMix.
ftajwar/d24-climbmix-dolmino-midtrain-100b: continued-pretrained for another approximately 100B tokens on 80% OLMo-3 Dolmino plus 20% ClimbMix replay.
This checkpoint: one epoch of SFT on the d24 conversation mixture after removing the GSM8K component, formatted with the simple ChatML template.

The model has seen approximately 200B pretraining/midtraining tokens before SFT. The v5 label identifies this training lineage; it is not a parameter-count label.

What was removed

The no-GSM8K corpus was derived from the standard sfanm/d24-sft-mixture source mixture. The standard and filtered simple-ChatML preparation summaries differ by exactly 7,473 source conversations, the size of the GSM8K training split used by the standard mixture:

Table with columns: Preparation count, Standard mixture, No-GSM8K mixture, Difference
Preparation count	Standard mixture	No-GSM8K mixture	Difference
Training source conversations	821,884	814,644	7,240
Validation source conversations	26,514	26,281	233
Total source conversations	848,398	840,925	7,473
Packed training sequences	227,443	226,879	564

Both preparations used sequence length 2,048, first_fit_shuffle, seed 42, the GPT-2 tokenizer, and the same simple ChatML template. Removing source conversations changes packing boundaries, so packed-sequence differences are not expected to equal the source-conversation difference.

Architecture

Table with columns: Field, Value
Field	Value
Parameters	756,819,456
Layers	24
Hidden size	1,536
Attention heads	12 (MHA)
FFN size	4,096 (SwiGLU/SiLU)
Position encoding	RoPE, theta 10,000
Normalization	RMSNorm
Embeddings	Tied
Tokenizer	GPT-2 BPE, vocabulary padded to 50,304

The Hugging Face architecture is LlamaForCausalLM, but the model uses the GPT-2 tokenizer and the d24 architecture above. It is not a Llama-family pretrained checkpoint.

SFT training

Table with columns: Hyperparameter, Value
Hyperparameter	Value
Epochs / steps	1 / 1,773
Global / micro batch	128 / 1
Sequence length	2,048
Optimizer	AdamW
Peak / minimum LR	1e-4 / 1e-5
LR schedule	Cosine, 50-step warmup
Adam betas / epsilon	(0.9, 0.95) / 1e-8
Weight decay	0.1
Gradient clip	1.0

The final SFT validation language-model loss was 0.797625 (perplexity 2.220262). This is an in-distribution SFT validation metric, not a downstream capability or safety evaluation.

Chat format and stopping

The template is:

text
<|im_start|>user
...<|im_end|>
<|im_start|>assistant
...

<|im_end|> is a literal string represented by ordinary GPT-2 BPE tokens. It is not eos_token_id=50256 and is not a registered special token. Generation must stop on the string <|im_end|>; otherwise the model can continue into another turn until max_new_tokens.

Transformers usage

python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "sfanm/d24-sft-v5-nogsm8k"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
).eval()

messages = [
    {
        "role": "user",
        "content": "Natalia sold clips to 48 friends in April and half as many in May. How many total?",
    }
]
prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=False,
    stop_strings=["<|im_end|>"],
    tokenizer=tokenizer,
)
response = tokenizer.decode(
    output[0, inputs.input_ids.shape[1]:],
    skip_special_tokens=False,
)
print(response)

For vLLM, use SamplingParams(stop=["<|im_end|>"]).

Intended use

controlled studies of learning GSM8K through RLVR without explicit GSM8K examples during SFT;
comparisons against the GSM8K-exposed v5 simplechat sibling;
research on GRPO, RLOO, maxRL, SFT, and small-model behavior.

Limitations

The no-GSM8K designation applies only to the explicit SFT mixture component; it does not prove global benchmark decontamination across pretraining and midtraining.
This is a small experimental research model and has not undergone production safety alignment or a comprehensive capability/safety evaluation.
It can produce incorrect, incoherent, biased, or unsafe text. Verify outputs independently and do not use it for high-stakes decisions.
The context window is 2,048 tokens.
Chat generation is incorrect unless the caller stops on the literal <|im_end|> string.
The model and SFT mixture derive from multiple upstream datasets. The Hub metadata therefore uses license: other; review source licenses and terms before redistribution or downstream use.

Reproducibility

SFT checkpoint: sft-d24-v5-nogsm8k/iter_0001773
HF export: d24-sft-v5-nogsm8k-hf
Packed corpus: sft-pack-d24-nogsm8k
Chat template: src/nemotron/data_prep/templates/simple_chatml.jinja
Training repository: yudasong/unified-training

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Model Details

Model Provider

sfanm

Model Tree

Base

ftajwar/d24-climbmix-dolmino-midtrain-100b

Fine-tuned

this model

Input Modalities

Text

Output Modalities