423 lines
14 KiB
Markdown
423 lines
14 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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tags:
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- text-generation
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- qwen2
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- unsloth
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- lora
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- gguf
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- llama.cpp
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- reasoning
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- distillation
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- conversational
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pipeline_tag: text-generation
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library_name: transformers
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datasets:
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- EphAsad/QWENMillenium-SF
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- EphAsad/Phi4Millennium-SF
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- EphAsad/MistralMillenium-SF
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- Modotte/CodeX-2M-Thinking
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- Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned
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- WithinUsAI/MiniMax_M2.7_Distilled_5k
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- tuanha1305/DeepSeek-R1-Distill
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- open-r1/OpenThoughts-114k-math
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- flytech/python-codes-25k
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- FreedomIntelligence/medical-o1-reasoning-SFT
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model-index:
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- name: Atem v1
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: ARC-Challenge
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type: ai2_arc
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config: ARC-Challenge
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split: test
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metrics:
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- type: acc_norm
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value: 0.455
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name: Accuracy (normalised)
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verified: false
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8K
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type: gsm8k
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split: test
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metrics:
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- type: exact_match
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value: 0.530
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name: Exact Match (strict, zero-shot)
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verified: false
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag
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type: hellaswag
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split: validation
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metrics:
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- type: acc_norm
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value: 0.644
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name: Accuracy (normalised)
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verified: false
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---
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<p align="center">
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<img src="Logo.png" width="300" alt="Atem Logo"/>
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</p>
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<h1 align="center">Atem v1</h1>
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<p align="center">
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<em>Ancient logic. Modern intelligence.</em>
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</p>
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<p align="center">
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A 1.5B reasoning model trained via multi-source knowledge distillation from frontier teacher models.
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</p>
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<p align="center">
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<img src="https://img.shields.io/badge/Base-Qwen2.5--1.5B--Instruct-blue" alt="Base Model"/>
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<img src="https://img.shields.io/badge/Method-LoRA%20SFT-purple" alt="Method"/>
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<img src="https://img.shields.io/badge/Parameters-1.5B-orange" alt="Parameters"/>
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<img src="https://img.shields.io/badge/License-Apache%202.0-green" alt="License"/>
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</p>
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---
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## Overview
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Atem is a 1.5B parameter reasoning model built via supervised fine-tuning on a curated corpus of approximately 115,000 examples distilled from multiple frontier teacher models. Starting from Qwen2.5-1.5B-Instruct, Atem was trained using LoRA to preserve base model capabilities while improving performance on reasoning, mathematics, and coding tasks.
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This is **Stage 1** of a planned multi-stage training series. Stage 1 focuses on establishing strong general reasoning across domains. Stage 2 layers chain-of-thought thinking traces on top of this foundation.
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Stage 2 is [Atem-Wisdom](https://huggingface.co/EphAsad/Atem-Wisdom-1.5B) which builds on this foundation by adding explicit chain-of-thought reasoning — the model works through problems inside <think> tags before producing its final answer.
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---
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## Model Details
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| Property | Value |
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|----------|-------|
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| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
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| **Training method** | LoRA Supervised Fine-Tuning (Stage 1) |
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| **LoRA config** | r=32, alpha=64, dropout=0.05 |
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| **Target modules** | q, k, v, o, gate, up, down projections |
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| **Parameters** | ~1.54B |
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| **Training records** | ~114,932 |
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| **Epochs** | 1 |
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| **Effective batch size** | 64 (batch 8 × grad accum 8) |
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| **Learning rate** | 2e-4, cosine schedule, 5% warmup |
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| **Final train loss** | 0.940 |
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| **Final val loss** | 0.890 |
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| **Hardware** | NVIDIA A100-SXM4 80GB |
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| **Max sequence length** | 4,096 tokens |
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| **Precision** | bfloat16 |
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| **License** | Apache 2.0 |
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---
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## Intended Use
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Atem is designed for open-ended reasoning tasks where structured, accurate thinking adds value:
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- Code explanation, implementation, and debugging
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- Mathematical problem solving with working shown
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- Analytical reasoning and hypothesis evaluation
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- Concept explanation and comparative analysis
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- Logic, argument, and fallacy identification
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Atem is **not** designed for retrieval-heavy factual lookup, real-time information, or tasks requiring broad knowledge breadth beyond its training domains.
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---
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## Training Data
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Atem was trained on a corpus assembled from eleven sources, combining domain-specific generated datasets and publicly available distillation datasets from frontier models. All outputs containing `<think>` reasoning traces were stripped to clean final responses for Stage 1 training.
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| Dataset | Records | Source / Teacher |
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|---------|---------|-----------------|
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| EphAsad/QWENMillenium-SF | 5,000 | Qwen2.5-14B — Analytical & Scientific |
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| EphAsad/Phi4Millennium-SF | 2,932 | Phi-4 14B — Mathematical Reasoning |
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| EphAsad/MistralMillenium-SF | 5,000 | Mistral-Nemo-12B — Language & Comprehension |
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| Modotte/CodeX-2M-Thinking | 30,000 | Mixed — Coding |
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| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | 23,000 | Kimi K2.5 — General Distillation (English filtered) |
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| WithinUsAI/MiniMax_M2.7_Distilled_5k | 5,000 | MiniMax M2.7 |
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| tuanha1305/DeepSeek-R1-Distill | 9,000 | DeepSeek-R1 |
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| open-r1/OpenThoughts-114k-math | 10,000 | Mixed — Mathematics (correct answers only) |
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| flytech/python-codes-25k | 10,000 | Python coding |
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| FreedomIntelligence/medical-o1-reasoning-SFT | 10,000 | Medical reasoning (English config) |
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| Private dataset | 5,000 | Undisclosed |
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| **Total** | **~114,932** | |
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The QWENMillenium-SF, Phi4Millennium-SF, and MistralMillenium-SF datasets were generated specifically for this project via batched inference on Colab A100. OpenThoughts-114k-math was filtered to verified correct solutions only before sampling.
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---
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## Training Configuration
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```python
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# Key hyperparameters
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lora_r = 32
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lora_alpha = 64
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lora_dropout = 0.05
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max_seq_length = 4096
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learning_rate = 2e-4
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lr_scheduler = 'cosine'
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warmup_ratio = 0.05
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batch_size = 8
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grad_accumulation = 8 # effective batch size: 64
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num_epochs = 1
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dtype = bfloat16
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load_in_4bit = True # during training
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```
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Training used Unsloth with `train_on_responses_only` masking, ensuring loss was computed exclusively on assistant response tokens. A three-part pre-training validation was run before training: chat template replacement verification, think tag strip confirmation, and mask sanity check.
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After training, LoRA adapters were merged into the base weights and exported as a full merged model.
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**Loss curve:**
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| Step | Train Loss | Val Loss |
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|------|-----------|----------|
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| 500 | 0.990 | 0.920 |
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| 1000 | 1.020 | 0.900 |
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| 1500 | 0.960 | 0.890 |
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| Final | **0.940** | **0.890** |
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Validation loss converged at 0.890, with a final train/val gap of 0.050 — indicating no overfitting over the single epoch.
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---
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## Evaluation
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### Benchmark Results
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Evaluated against Qwen2.5-1.5B-Instruct (base model) using lm-evaluation-harness with identical conditions: 4-bit inference, batch size 16, zero-shot strict evaluation.
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| Task | Base (1.5B) | Atem v1 (1.5B) | Delta |
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|------|------------|----------------|-------|
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| ARC-Challenge | 43.7% | 45.5% | +1.8% ✓ |
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| GSM8K | 23.0% | **53.0%** | **+30.0%** ✓ |
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| HellaSwag | 66.8% | 64.4% | -2.4% |
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The GSM8K result is the primary finding. A +30 percentage point improvement on grade school mathematics reflects the targeted training on verified correct mathematical reasoning examples from multiple frontier teacher models.
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The HellaSwag regression of 2.4% is within normal benchmark variance and represents a significant improvement over a prior exploratory training run using full fine-tune, which produced a 16.2% regression on the same benchmark. LoRA preserved base model commonsense capabilities as intended.
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### Comparison vs Qwen2.5-7B-Instruct
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To contextualise the GSM8K result, Atem was benchmarked against Qwen2.5-7B-Instruct under the same zero-shot strict evaluation conditions.
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| Model | Parameters | GSM8K (zero-shot strict) |
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|-------|-----------|--------------------------|
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| Qwen2.5-1.5B-Instruct | 1.5B | 23.0% |
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| **Atem v1** | **1.5B** | **53.0%** |
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| Qwen2.5-7B-Instruct | 7B | 74.9% |
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At baseline, the 1.5B model sits 51.9 points below the 7B. After training, Atem sits 21.9 points below — closing approximately **58% of the capability gap** between 1.5B and 7B on mathematical reasoning. Atem achieves **71% of Qwen2.5-7B's GSM8K performance at 22% of its parameter count**.
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Note: Official Qwen2.5-7B-Instruct scores (91.6% GSM8K) use 4-shot chain-of-thought prompting. The 74.9% figure above reflects the same zero-shot strict evaluation format used for Atem, ensuring a fair direct comparison.
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### Qualitative Evaluation
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Atem was evaluated against Qwen2.5-1.5B-Instruct across 30 domain-representative questions using matched system prompts, ensuring differences in output reflect trained capability rather than prompt engineering.
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| Domain | Questions | Outcome |
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|--------|-----------|---------|
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| Coding | 8 | Atem stronger — more thorough, better structured, catches edge cases |
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| Mathematics | 6 | Comparable — both accurate on standard problems |
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| Analytical Reasoning | 6 | Atem stronger — better structured arguments |
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| General Knowledge | 5 | Comparable |
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| Language & Logic | 5 | Atem stronger — correct fallacy identification, greater depth |
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---
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## Usage
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "EphAsad/Atem-v1-1.5B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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messages = [
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{
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"role": "user",
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"content": "Write a Python function that checks whether a number is prime."
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}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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with torch.no_grad():
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output = model.generate(
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input_ids=inputs,
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max_new_tokens=1000,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.1,
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do_sample=True,
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)
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response = tokenizer.decode(
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output[0][inputs.shape[1]:],
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skip_special_tokens=True
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)
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print(response)
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```
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### Unsloth (faster inference)
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```python
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from unsloth import FastLanguageModel
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import torch
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="EphAsad/Atem-v1-1.5B",
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max_seq_length=4096,
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dtype=torch.bfloat16,
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load_in_4bit=True,
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)
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FastLanguageModel.for_inference(model)
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messages = [
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{
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"role": "user",
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"content": "Explain the difference between a stack and a queue, with examples."
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}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt"
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).to("cuda")
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with torch.no_grad():
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output = model.generate(
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input_ids=inputs,
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max_new_tokens=1000,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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)
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print(tokenizer.decode(
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output[0][inputs.shape[1]:],
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skip_special_tokens=True
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))
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```
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### Ollama
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```bash
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# Recommended — best speed/quality balance
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ollama run hf.co/EphAsad/Atem-v1-1.5B:Q4_K_M
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# Higher quality
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ollama run hf.co/EphAsad/Atem-v1-1.5B:Q5_K_M
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# Near-lossless
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ollama run hf.co/EphAsad/Atem-v1-1.5B:Q8_0
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```
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### llama.cpp
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```bash
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llama-server -hf EphAsad/Atem-v1-1.5B:Q4_K_M
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```
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### System Prompt
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Atem's identity is baked into the chat template and activates automatically when no system message is provided. For manual override:
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```
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You are Atem, a precise and analytical reasoning assistant. You approach
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every problem methodically — identifying core concepts, reasoning step by
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step, and arriving at well-supported conclusions. You show your thinking
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clearly and are thorough, direct, and intellectually honest.
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```
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### Available Files
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| File | Size | Description |
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|------|------|-------------|
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| `model.safetensors` | ~3.1 GB | Full bfloat16 merged weights |
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| `Atem-1.5b.Q4_K_M.gguf` | ~986 MB | 4-bit quantised — recommended |
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| `Atem-1.5b.Q5_K_M.gguf` | ~1.1 GB | 5-bit quantised |
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| `Atem-1.5b.Q8_0.gguf` | ~1.6 GB | 8-bit quantised — near-lossless |
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---
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## Known Limitations
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**No thinking traces (Stage 1 by design).** Think tags were stripped from all training data for Stage 1. The model does not produce extended `<think>` reasoning traces. Stage 2 training will layer this capability on top of the Stage 1 foundation.
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**Mathematical precision on complex problems.** On multi-step calculations, the model may make arithmetic slips in intermediate steps while arriving at a structurally correct approach. Answers to high-stakes mathematical problems should be independently verified.
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**HellaSwag regression.** A 2.4% regression on HellaSwag commonsense completion is observed. This is minor and substantially better than the 16.2% regression produced by the earlier exploratory full fine-tune run, confirming that LoRA preserved base commonsense capability effectively.
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---
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## Roadmap
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Atem v1 establishes the Stage 1 foundation. Planned next steps:
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- **Stage 2:** LoRA SFT on curated chain-of-thought data to add thinking trace capability — using `Complex_CoT`, `inverted_reasoning`, and reasoning trace columns held out from Stage 1 training
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- **Extended benchmarks:** MMLU, BBH, IFEval, WinoGrande, MBPP post-Stage 2
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- **Atem v2:** Expanded corpus, further domain coverage
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---
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## Citation
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```bibtex
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@misc{atem_v1_2026,
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author = {Asad, Zain},
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title = {Atem v1: A 1.5B Reasoning Model via
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Multi-Source Knowledge Distillation},
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year = {2026},
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publisher = {HuggingFace},
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howpublished = {\url{https://huggingface.co/EphAsad/Atem-v1-1.5B}},
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}
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```
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---
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## Support
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If you find this model useful for your research or projects,
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you can support further development of my datasets and models here:
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☕ [ko-fi.com/ephraim123](https://ko-fi.com/ephraim123)
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---
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## License
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Released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), consistent with the base model Qwen2.5-1.5B-Instruct.
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---
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<p align="center">
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Built independently by <a href="https://huggingface.co/EphAsad">EphAsad</a>
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</p> |