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Model: EphAsad/Atem-v1-1.5B Source: Original Platform
2026-08-01 07:59:20 +08:00
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+---
+language:
+- en
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+tags:
+- text-generation
+- qwen2
+- unsloth
+- lora
+- gguf
+- llama.cpp
+- reasoning
+- distillation
+- conversational
+pipeline_tag: text-generation
+library_name: transformers
+datasets:
+- EphAsad/QWENMillenium-SF
+- EphAsad/Phi4Millennium-SF
+- EphAsad/MistralMillenium-SF
+- Modotte/CodeX-2M-Thinking
+- Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned
+- WithinUsAI/MiniMax_M2.7_Distilled_5k
+- tuanha1305/DeepSeek-R1-Distill
+- open-r1/OpenThoughts-114k-math
+- flytech/python-codes-25k
+- FreedomIntelligence/medical-o1-reasoning-SFT
+model-index:
+- name: Atem v1
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: ARC-Challenge
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+    metrics:
+    - type: acc_norm
+      value: 0.455
+      name: Accuracy (normalised)
+      verified: false
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8K
+      type: gsm8k
+      split: test
+    metrics:
+    - type: exact_match
+      value: 0.530
+      name: Exact Match (strict, zero-shot)
+      verified: false
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag
+      type: hellaswag
+      split: validation
+    metrics:
+    - type: acc_norm
+      value: 0.644
+      name: Accuracy (normalised)
+      verified: false
+---
+<p align="center">
+  <img src="Logo.png" width="300" alt="Atem Logo"/>
+</p>
+
+<h1 align="center">Atem v1</h1>
+
+<p align="center">
+  <em>Ancient logic. Modern intelligence.</em>
+</p>
+
+<p align="center">
+  A 1.5B reasoning model trained via multi-source knowledge distillation from frontier teacher models.
+</p>
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Base-Qwen2.5--1.5B--Instruct-blue" alt="Base Model"/>
+  <img src="https://img.shields.io/badge/Method-LoRA%20SFT-purple" alt="Method"/>
+  <img src="https://img.shields.io/badge/Parameters-1.5B-orange" alt="Parameters"/>
+  <img src="https://img.shields.io/badge/License-Apache%202.0-green" alt="License"/>
+</p>
+
+---
+
+## Overview
+
+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.
+
+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.
+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.
+
+---
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| **Base model** | Qwen/Qwen2.5-1.5B-Instruct |
+| **Training method** | LoRA Supervised Fine-Tuning (Stage 1) |
+| **LoRA config** | r=32, alpha=64, dropout=0.05 |
+| **Target modules** | q, k, v, o, gate, up, down projections |
+| **Parameters** | ~1.54B |
+| **Training records** | ~114,932 |
+| **Epochs** | 1 |
+| **Effective batch size** | 64 (batch 8 × grad accum 8) |
+| **Learning rate** | 2e-4, cosine schedule, 5% warmup |
+| **Final train loss** | 0.940 |
+| **Final val loss** | 0.890 |
+| **Hardware** | NVIDIA A100-SXM4 80GB |
+| **Max sequence length** | 4,096 tokens |
+| **Precision** | bfloat16 |
+| **License** | Apache 2.0 |
+
+---
+
+## Intended Use
+
+Atem is designed for open-ended reasoning tasks where structured, accurate thinking adds value:
+
+- Code explanation, implementation, and debugging
+- Mathematical problem solving with working shown
+- Analytical reasoning and hypothesis evaluation
+- Concept explanation and comparative analysis
+- Logic, argument, and fallacy identification
+
+Atem is **not** designed for retrieval-heavy factual lookup, real-time information, or tasks requiring broad knowledge breadth beyond its training domains.
+
+---
+
+## Training Data
+
+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.
+
+| Dataset | Records | Source / Teacher |
+|---------|---------|-----------------|
+| EphAsad/QWENMillenium-SF | 5,000 | Qwen2.5-14B — Analytical & Scientific |
+| EphAsad/Phi4Millennium-SF | 2,932 | Phi-4 14B — Mathematical Reasoning |
+| EphAsad/MistralMillenium-SF | 5,000 | Mistral-Nemo-12B — Language & Comprehension |
+| Modotte/CodeX-2M-Thinking | 30,000 | Mixed — Coding |
+| Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned | 23,000 | Kimi K2.5 — General Distillation (English filtered) |
+| WithinUsAI/MiniMax_M2.7_Distilled_5k | 5,000 | MiniMax M2.7 |
+| tuanha1305/DeepSeek-R1-Distill | 9,000 | DeepSeek-R1 |
+| open-r1/OpenThoughts-114k-math | 10,000 | Mixed — Mathematics (correct answers only) |
+| flytech/python-codes-25k | 10,000 | Python coding |
+| FreedomIntelligence/medical-o1-reasoning-SFT | 10,000 | Medical reasoning (English config) |
+| Private dataset | 5,000 | Undisclosed |
+| **Total** | **~114,932** | |
+
+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.
+
+---
+
+## Training Configuration
+
+```python
+# Key hyperparameters
+lora_r            = 32
+lora_alpha        = 64
+lora_dropout      = 0.05
+max_seq_length    = 4096
+learning_rate     = 2e-4
+lr_scheduler      = 'cosine'
+warmup_ratio      = 0.05
+batch_size        = 8
+grad_accumulation = 8       # effective batch size: 64
+num_epochs        = 1
+dtype             = bfloat16
+load_in_4bit      = True    # during training
+```
+
+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.
+
+After training, LoRA adapters were merged into the base weights and exported as a full merged model.
+
+**Loss curve:**
+
+| Step | Train Loss | Val Loss |
+|------|-----------|----------|
+| 500 | 0.990 | 0.920 |
+| 1000 | 1.020 | 0.900 |
+| 1500 | 0.960 | 0.890 |
+| Final | **0.940** | **0.890** |
+
+Validation loss converged at 0.890, with a final train/val gap of 0.050 — indicating no overfitting over the single epoch.
+
+---
+
+## Evaluation
+
+### Benchmark Results
+
+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.
+
+| Task | Base (1.5B) | Atem v1 (1.5B) | Delta |
+|------|------------|----------------|-------|
+| ARC-Challenge | 43.7% | 45.5% | +1.8% ✓ |
+| GSM8K | 23.0% | **53.0%** | **+30.0%** ✓ |
+| HellaSwag | 66.8% | 64.4% | -2.4% |
+
+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.
+
+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.
+
+### Comparison vs Qwen2.5-7B-Instruct
+
+To contextualise the GSM8K result, Atem was benchmarked against Qwen2.5-7B-Instruct under the same zero-shot strict evaluation conditions.
+
+| Model | Parameters | GSM8K (zero-shot strict) |
+|-------|-----------|--------------------------|
+| Qwen2.5-1.5B-Instruct | 1.5B | 23.0% |
+| **Atem v1** | **1.5B** | **53.0%** |
+| Qwen2.5-7B-Instruct | 7B | 74.9% |
+
+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**.
+
+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.
+
+### Qualitative Evaluation
+
+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.
+
+| Domain | Questions | Outcome |
+|--------|-----------|---------|
+| Coding | 8 | Atem stronger — more thorough, better structured, catches edge cases |
+| Mathematics | 6 | Comparable — both accurate on standard problems |
+| Analytical Reasoning | 6 | Atem stronger — better structured arguments |
+| General Knowledge | 5 | Comparable |
+| Language & Logic | 5 | Atem stronger — correct fallacy identification, greater depth |
+
+---
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "EphAsad/Atem-v1-1.5B"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Write a Python function that checks whether a number is prime."
+    }
+]
+
+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=1000,
+        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)
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="EphAsad/Atem-v1-1.5B",
+    max_seq_length=4096,
+    dtype=torch.bfloat16,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+
+messages = [
+    {
+        "role": "user",
+        "content": "Explain the difference between a stack and a queue, with examples."
+    }
+]
+
+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=1000,
+        temperature=0.7,
+        top_p=0.9,
+        do_sample=True,
+    )
+
+print(tokenizer.decode(
+    output[0][inputs.shape[1]:],
+    skip_special_tokens=True
+))
+```
+
+### Ollama
+
+```bash
+# Recommended — best speed/quality balance
+ollama run hf.co/EphAsad/Atem-v1-1.5B:Q4_K_M
+
+# Higher quality
+ollama run hf.co/EphAsad/Atem-v1-1.5B:Q5_K_M
+
+# Near-lossless
+ollama run hf.co/EphAsad/Atem-v1-1.5B:Q8_0
+```
+
+### llama.cpp
+
+```bash
+llama-server -hf EphAsad/Atem-v1-1.5B:Q4_K_M
+```
+
+### System Prompt
+
+Atem's identity is baked into the chat template and activates automatically when no system message is provided. For manual override:
+
+```
+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.
+```
+
+### Available Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `model.safetensors` | ~3.1 GB | Full bfloat16 merged weights |
+| `Atem-1.5b.Q4_K_M.gguf` | ~986 MB | 4-bit quantised — recommended |
+| `Atem-1.5b.Q5_K_M.gguf` | ~1.1 GB | 5-bit quantised |
+| `Atem-1.5b.Q8_0.gguf` | ~1.6 GB | 8-bit quantised — near-lossless |
+
+---
+
+## Known Limitations
+
+**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.
+
+**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.
+
+**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.
+
+---
+
+## Roadmap
+
+Atem v1 establishes the Stage 1 foundation. Planned next steps:
+
+- **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
+- **Extended benchmarks:** MMLU, BBH, IFEval, WinoGrande, MBPP post-Stage 2
+- **Atem v2:** Expanded corpus, further domain coverage
+
+---
+
+## Citation
+
+```bibtex
+@misc{atem_v1_2026,
+  author       = {Asad, Zain},
+  title        = {Atem v1: A 1.5B Reasoning Model via 
+                  Multi-Source Knowledge Distillation},
+  year         = {2026},
+  publisher    = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/EphAsad/Atem-v1-1.5B}},
+}
+```
+---
+
+## Support
+
+If you find this model useful for your research or projects, 
+you can support further development of my datasets and models here:  
+☕ [ko-fi.com/ephraim123](https://ko-fi.com/ephraim123)
+
+---
+
+## License
+
+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.
+
+---
+
+<p align="center">
+  Built independently by <a href="https://huggingface.co/EphAsad">EphAsad</a>
+</p>