344 lines
12 KiB
Markdown
344 lines
12 KiB
Markdown
---
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base_model: EphAsad/Atem-Wisdom-1.5B
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language:
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- en
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license: apache-2.0
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tags:
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- qwen2
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- unsloth
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- lora
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- llama.cpp
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- reasoning
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- chain-of-thought
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- thinking
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- mathematics
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- math
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- distillation
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- conversational
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- text-generation-inference
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datasets:
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- HuggingFaceH4/MATH-500
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- EleutherAI/hendrycks_math
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- qwedsacf/competition_math
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pipeline_tag: text-generation
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---
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# Atem-SageMaths
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*Ancient logic. Modern intelligence. Applied to mathematics.*
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A 1.5B mathematics model trained on competition-grade problems — covering algebra, geometry, number theory, combinatorics, and more.
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---
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## Overview
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Atem-SageMaths is a mathematics-specialised variant of [Atem-Wisdom-1.5B](https://huggingface.co/EphAsad/Atem-Wisdom-1.5B), fine-tuned on a curated corpus of competition and curriculum mathematics problems drawn from three complementary sources. It inherits Atem-Wisdom's reasoning capability and applies it to mathematical problem solving — working through problems step by step before arriving at a final answer.
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The training corpus spans the full difficulty range from pre-algebra through competition-level mathematics, with structured solution traces from MATH-500 providing the think-then-answer format on harder problems.
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**When to choose Atem-SageMaths over Atem-Wisdom:**
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- Mathematical problem solving across a broad difficulty range
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- Competition mathematics — algebra, geometry, number theory, combinatorics, probability
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- Situations where step-by-step mathematical working is required
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- Curriculum mathematics tasks at pre-algebra through precalculus level
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**When to choose Atem-Wisdom instead:**
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- General reasoning, analytical, and logic tasks outside mathematics
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- Mixed-domain workloads where maths is one of many task types
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- Coding and algorithm tasks
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---
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## The Atem Series
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| Model | Stage | Capability | Status |
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|---|---|---|---|
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| [Atem v1](https://huggingface.co/EphAsad/Atem-v1-1.5B) | Stage 1 — SFT | Fast, direct reasoning | ✅ |
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| [Atem-Wisdom](https://huggingface.co/EphAsad/Atem-Wisdom-1.5B) | Stage 2 — CoT | Explicit thinking traces | ✅ |
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| [Atem-SageCoder](https://huggingface.co/EphAsad/Atem-SageCoder-1.5B) | Specialisation — Code | Think-then-code on algorithms | ✅ |
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| **Atem-SageMaths** | Specialisation — Maths | Structured mathematical problem solving | ✅ |
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| Atem-Pharaoh *(planned)* | Stage 3 — DPO/IPO | Preference-aligned reasoning | 🔄 |
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Atem-SageMaths is a domain-specialised branch off Atem-Wisdom, parallel to Atem-SageCoder. Both share the same base and training framework; only the dataset domain differs.
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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** | EphAsad/Atem-Wisdom-1.5B |
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| **Root architecture** | Qwen/Qwen2.5-1.5B-Instruct |
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| **Training method** | LoRA SFT — Mathematics Specialisation |
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| **LoRA config** | r=32, alpha=64, dropout=0.05 |
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| **Parameters** | ~1.54B |
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| **Training records** | 16,940 (after filtering) |
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| **Think / no-think split** | ~3% / ~97% |
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| **Epochs** | 2 |
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| **Total steps** | 266 |
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| **Final train loss** | 0.5602 |
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| **Final val loss** | 0.6044 |
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| **Hardware** | NVIDIA A100-SXM4 80GB |
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| **Max sequence length** | 8,192 tokens |
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| **Precision** | bfloat16 |
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| **License** | Apache 2.0 |
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---
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## Output Format
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Atem-SageMaths produces responses in one of two formats:
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**With reasoning trace (MATH-500 derived examples):**
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```
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<think>
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[Step-by-step mathematical working — problem analysis, intermediate
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calculations, verification of results]
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</think>
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[Final answer]
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```
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**Direct answer (majority of responses):**
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```
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[Structured solution with working shown inline]
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```
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The think-then-answer format activates on harder problems where the training data included explicit reasoning traces. The majority of training data (97%) used direct solution format without a separate think block, reflecting the natural composition of the training corpus.
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---
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## Training Data
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Atem-SageMaths was trained on 16,940 examples assembled from three sources after token length filtering. camel-ai/math was initially included but removed due to streaming instability during data loading.
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| Dataset | Records | CoT | Domain |
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|---|---|---|---|
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| HuggingFaceH4/MATH-500 | 500 | ✅ solution→think, answer→final | Competition maths, full curriculum |
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| EleutherAI/hendrycks_math | ~10,500 (1,500 × 7 subsets) | ❌ | Algebra, geometry, number theory, counting & probability, intermediate algebra, prealgebra, precalculus |
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| qwedsacf/competition_math | 10,000 | ❌ | Competition mathematics |
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**MATH-500 handling:** The `solution` field contains full worked solutions and was used as the CoT trace (placed between `<think>` tags). The `answer` field (boxed final answer) was used as the response. This is the only source of think-trace training examples.
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**hendrycks_math subsets loaded:** algebra, counting_and_probability, geometry, intermediate_algebra, number_theory, prealgebra, precalculus — 1,500 records from the train split of each.
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**Token length filter:** Examples exceeding 8,192 tokens after chat template application were removed rather than truncated. This is the primary source of attrition from the ~31,000 raw records loaded to 16,940 retained.
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**Loss curve:**
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| Step | Train Loss | Val Loss |
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|---|---|---|
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| 100 | 0.6201 | 0.6310 |
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| 200 | 0.5362 | 0.6077 |
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| 266 (final) | **0.5602** | **0.6044** |
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Loss values are substantially lower than Atem-SageCoder (0.604 vs 0.859), consistent with mathematics Q&A being more formulaic and predictable than code+CoT reasoning traces. Train/val gap of ~0.04 throughout — no overfitting signal. Sharp improvement from steps 100→200 followed by stabilisation.
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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 = 8192
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learning_rate = 1e-4
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lr_scheduler = 'cosine'
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warmup_ratio = 0.05
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batch_size = 8 # auto-scaled by Unsloth from config 4;
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# shorter math examples left VRAM headroom
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grad_accumulation = 16 # effective batch size: 128
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num_epochs = 2
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dtype = bfloat16
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load_in_4bit = True
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nothink_ratio = 0.97 # reflects natural dataset composition
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```
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Training used Unsloth (`unsloth==2026.5.5`, `unsloth_zoo==2026.5.5`) with `train_on_responses_only` masking. Loss was computed exclusively on assistant response tokens. Pre-training validation confirmed identity, think tag format, and mask correctness before training was confirmed.
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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-SageMaths-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": "Find all integer solutions to x² - 7x + 12 = 0."
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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=1024,
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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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print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))
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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-SageMaths-1.5B",
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max_seq_length=8192,
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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": "In how many ways can 8 people be seated around a circular table?"
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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=1024,
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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(output[0][inputs.shape[1]:], skip_special_tokens=True))
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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-SageMaths-1.5B:Q4_K_M
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# Higher quality
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ollama run hf.co/EphAsad/Atem-SageMaths-1.5B:Q5_K_M
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# Near-lossless
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ollama run hf.co/EphAsad/Atem-SageMaths-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-SageMaths-1.5B:Q4_K_M
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```
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### System Prompt
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Atem-SageMaths's identity and mathematical focus are baked into the chat template. To override manually:
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```
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You are Atem-SageMaths, a precise mathematical reasoning assistant built
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on the Atem foundation. You approach problems methodically — working through
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each step carefully, verifying intermediate results, and arriving at
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well-supported solutions. Your answers are rigorous, clearly structured,
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and mathematically correct.
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```
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### Available Files
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| File | Size | Description |
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| `model.safetensors` | ~3.1 GB | Full bfloat16 merged weights |
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| `Atem-SageMaths-1.5B.Q4_K_M.gguf` | ~986 MB | 4-bit quantised — recommended |
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| `Atem-SageMaths-1.5B.Q5_K_M.gguf` | ~1.1 GB | 5-bit quantised |
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| `Atem-SageMaths-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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**Thin CoT coverage.** Only MATH-500 (500 records, ~3% of training data) contributes explicit think traces. The model's think-then-answer behaviour is therefore limited to problems structurally similar to MATH-500 content. On novel problem types, it defaults to the direct solution format.
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**Arithmetic precision.** Multi-step numerical calculations remain a known weakness across the Atem series. Intermediate arithmetic slips can occur on problems requiring many sequential operations. Final answers to precision-critical calculations should be independently verified.
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**Competition scope.** Training data is heavily weighted toward competition-style problems with clean closed-form answers. Open-ended applied mathematics, calculus, linear algebra, and statistics are not represented in the training corpus.
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---
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## Roadmap
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| Stage | Status | Description |
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| Stage 1 — SFT | ✅ Complete | Atem v1 — direct reasoning foundation |
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| Stage 2 — CoT SFT | ✅ Complete | Atem-Wisdom — thinking traces |
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| Specialisation — Code | ✅ Complete | Atem-SageCoder |
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| Specialisation — Maths | ✅ Complete | **Atem-SageMaths — this model** |
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| Stage 3 — DPO/IPO | 🔄 Planned | Atem-Pharaoh — preference-aligned reasoning |
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---
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## Citation
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```bibtex
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@misc{atem_sagemaths_2026,
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author = {Asad, Zain},
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title = {Atem-SageMaths: A 1.5B Mathematics Model
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via Competition Problem Distillation},
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year = {2026},
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publisher = {HuggingFace},
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howpublished = {\url{https://huggingface.co/EphAsad/Atem-SageMaths-1.5B}},
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}
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```
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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 chain (Qwen2.5-1.5B-Instruct → Atem v1 → Atem-Wisdom → Atem-SageMaths).
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---
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Built independently by [EphAsad](https://huggingface.co/EphAsad) |