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Atem-3B

Ancient logic. Modern intelligence.

The 3B foundation model of the Atem series — direct reasoning at scale.

👁 Base Model
👁 Stage
👁 Parameters
👁 License

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Overview

Atem-3B is the first release in the 3B branch of the Atem model series — a Stage 1 supervised fine-tune on Qwen2.5-3B-Instruct across approximately 120,000 training examples spanning mathematics, code, reasoning, and general instruction following.

Where the 1.5B Atem line demonstrated that a small model could be meaningfully improved through careful data curation, Atem-3B applies the same methodology at twice the parameter count. The 3B base provides a stronger foundation — particularly for mathematical reasoning and structured generation — while the training corpus prioritises quality and diversity over volume.

Design philosophy: Think tags were stripped from all training data during preprocessing. Atem-3B is a direct-answer model — it does not produce <think> traces. The reasoning capacity of the 3B base is channelled into producing well-structured, considered responses rather than visible chain-of-thought. A CoT variant is planned for Stage 2.

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The Atem Series

1.5B Series

3B Series

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Model Details

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Output Format

Atem-3B produces direct, structured responses. Think tags were stripped from all training data during preprocessing — the model was trained exclusively on clean outputs with no chain-of-thought traces.

[Direct response — reasoned, structured, no <think> tags]

This is a deliberate Stage 1 design choice. A chain-of-thought variant exposing explicit reasoning traces is planned as Stage 2.

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Training Data

Stage 1 training used approximately 120,000 examples drawn from eleven sources. All reasoning traces (<think>...</think> blocks) were stripped prior to training. Records shorter than 20 characters after stripping were excluded.

Chinese-language records from Kimi K2.5 were filtered using an ASCII character ratio threshold before inclusion. OpenThoughts-114k-math was filtered to correct == True examples only.

Loss curve:

Validation loss descends steadily throughout the full run with no overfitting signal.

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Evaluation

Benchmark Results

Evaluated using lm-evaluation-harness via the Python API under identical conditions for both models. ARC-Challenge and HellaSwag use zero-shot normalised accuracy; GSM8K uses 5-shot. Both models evaluated at 4-bit quantisation on the same A100-SXM4-80GB in torch.float16.

Note on GSM8K: lm_eval's strict-match filter uses a #### number regex that only fires when the model produces that exact token sequence. The base Qwen2.5-3B-Instruct solves problems correctly but formats answers conversationally, yielding 2.1% strict-match against a 62.4% flexible-extract — the latter being the accurate measure of base model mathematical capability. Atem-3B's training on math distillation datasets reinforced structured answer termination, producing 37.1% strict-match. The meaningful comparison is flexible-extract: 62.4% → 64.7% (+2.3%) — a genuine but modest improvement. The strict-match delta is a formatting artefact, not a 35-point gain in mathematical reasoning ability.

Note on HellaSwag: The -3.0% regression is a common pattern when fine-tuning instruct models on structured reasoning and task-completion data. HellaSwag tests commonsense sentence completion in a multiple-choice format; training on problem-solving corpora shifts the model's distribution away from the casual, predictive register that HellaSwag measures. This is a known trade-off, not an indicator of general capability loss.

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Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "EphAsad/Atem-3B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
 model_name,
 torch_dtype=torch.bfloat16,
 device_map="auto"
)

messages = [
 {
 "role": "user",
 "content": "Explain the difference between a process and a thread."
 }
]

inputs = tokenizer.apply_chat_template(
 messages,
 tokenize=True,
 add_generation_prompt=True,
 return_tensors="pt"
).to(model.device)

with torch.no_grad():
 output = model.generate(
 input_ids=inputs,
 max_new_tokens=1024,
 temperature=0.7,
 top_p=0.9,
 repetition_penalty=1.1,
 do_sample=True,
 )

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

Unsloth (faster inference)

from unsloth import FastLanguageModel
import torch

model, tokenizer = FastLanguageModel.from_pretrained(
 model_name="EphAsad/Atem-3B",
 max_seq_length=4096,
 dtype=torch.bfloat16,
 load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

messages = [
 {
 "role": "user",
 "content": "Write a Python function to find all prime numbers up to n."
 }
]

inputs = tokenizer.apply_chat_template(
 messages,
 tokenize=True,
 add_generation_prompt=True,
 return_tensors="pt"
).to("cuda")

with torch.no_grad():
 output = model.generate(
 input_ids=inputs,
 max_new_tokens=1024,
 temperature=0.7,
 top_p=0.9,
 do_sample=True,
 )

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

Ollama

# Recommended — best speed/quality balance
ollama run hf.co/EphAsad/Atem-3B:Q4_K_M

# Higher quality
ollama run hf.co/EphAsad/Atem-3B:Q5_K_M

# Near-lossless
ollama run hf.co/EphAsad/Atem-3B:Q8_0

llama.cpp

llama-server -hf EphAsad/Atem-3B:Q4_K_M

Available Files

System Prompt

Atem-3B's identity is baked into the chat template and activates without an explicit system message. To override manually:

You are Atem, a precise and analytical reasoning assistant. You approach
every problem methodically — identifying core concepts, reasoning step by
step, and arriving at well-supported conclusions. You show your thinking
clearly and are thorough, direct, and intellectually honest.

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Roadmap

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Citation

@misc{atem_3b_2026,
 author = {Asad, Zain},
 title = {Atem-3B: A 3B Direct-Reasoning Model via Stage 1 SFT},
 year = {2026},
 publisher = {HuggingFace},
 howpublished = {\url{https://huggingface.co/EphAsad/Atem-3B}},
}

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License

Released under the Apache 2.0 License, consistent with the base model (Qwen2.5-3B-Instruct).

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Built independently by EphAsad

Base model

Qwen/Qwen2.5-3B

Qwen/Qwen2.5-3B-Instruct

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