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Model: EphAsad/Atem-SageCoder-1.5B Source: Original Platform
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README.md
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README.md
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---
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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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- coding
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- code-generation
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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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- nvidia/OpenCodeReasoning
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pipeline_tag: text-generation
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---
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# Atem-SageCoder
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*Ancient logic. Modern intelligence. Applied to code.*
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A 1.5B code reasoning model that thinks before it writes — trained on verified competitive programming traces from frontier models.
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---
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## Overview
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Atem-SageCoder is a code-specialised variant of [Atem-Wisdom-1.5B](https://huggingface.co/EphAsad/Atem-Wisdom-1.5B), fine-tuned on verified chain-of-thought coding traces from [nvidia/OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning). It inherits Atem-Wisdom's explicit reasoning capability and applies it specifically to programming tasks — working through algorithm choice, edge cases, and complexity analysis before producing an implementation.
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The core behaviour: when given a coding problem, the model reasons through it fully inside a `<think>` block before writing any code. This makes its reasoning auditable and reduces the frequency of structurally plausible but logically incorrect solutions.
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**When to choose Atem-SageCoder over Atem-Wisdom:**
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- Programming problems where reasoning about approach matters before implementation
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- Competitive programming and algorithmic tasks
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- Situations where you want to see the model's design decisions, not just its output
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- Code that requires edge case analysis or complexity reasoning
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**When to choose Atem-Wisdom instead:**
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- General reasoning, mathematics, and analytical tasks outside of coding
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- Mixed-domain workloads where code is one of many task types
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- Environments where output length is a constraint
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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** | Specialisation — Code | Think-then-code on algorithms | ✅ |
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| Atem-Pharaoh *(planned)* | Stage 3 — DPO/IPO | Preference-aligned reasoning | 🔄 |
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Atem-SageCoder is a domain-specialised branch off Atem-Wisdom, not a continuation of the main series progression toward Atem-Pharaoh.
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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 — Code Reasoning 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** | 15,427 (after filtering) |
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| **Think / no-think split** | 90% / 10% |
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| **Epochs** | 2 |
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| **Total steps** | 484 |
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| **Final train loss** | 0.8477 |
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| **Final val loss** | 0.8591 |
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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-SageCoder produces responses in one of two formats:
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**With reasoning trace (90% of training examples):**
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```
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<think>
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[Reasoning through the problem — algorithm selection, edge cases,
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complexity analysis, implementation approach]
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</think>
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[Final implementation — clean, correct code with explanation]
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```
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**Direct answer (simple queries):**
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```
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[Concise code response — no reasoning trace needed]
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```
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The 10% no-think training pool prevents the model from refusing to answer simple queries without extended reasoning. On straightforward questions it responds directly; the think trace activates proportionally to problem complexity.
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---
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## Training Data
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Atem-SageCoder was trained on 15,427 examples drawn from `nvidia/OpenCodeReasoning` (split_0), after streaming 40,000 candidates and applying two sequential filters.
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**Filter 1 — Truncation gate:** Records were rejected if `</think>` was absent from the output (CoT cut off mid-trace) or if fewer than 30 characters of code followed `</think>` (code truncated). This is the primary source of attrition — OpenCodeReasoning CoT traces are long, and 8,192 tokens captures roughly 38% of the raw stream.
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**Filter 2 — Bad input gate:** Records with `input` fields under 20 characters were rejected. A known data quality issue in split_1 caused that entire split to be excluded; all training data comes from split_0.
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**Filter 3 — Token length:** Examples exceeding 8,192 tokens after chat template application were removed rather than truncated.
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| Property | Value |
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|---|---|
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| Dataset | nvidia/OpenCodeReasoning (split_0) |
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| Streamed | 40,000 |
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| After truncation filter | ~24,000 |
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| After token length filter | 15,427 |
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| Train / Val split | 14,627 / 800 |
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| Domain | Competitive programming (algorithmic problems) |
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**CoT extraction:** The `output` column in OpenCodeReasoning contains `<think>...</think>code` format. CoT and code were extracted into separate fields before formatting. The `<think>` tags were removed from the raw output to avoid double-tag injection during chat template application, then manually reinserted during `build_text` construction with `enable_thinking=False`.
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**Loss curve:**
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| Step | Train Loss | Val Loss |
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| 250 | 0.8564 | 0.8757 |
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| 484 (final) | **0.8477** | **0.8591** |
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Train/val gap of 0.012 at completion — no overfitting signal. Loss values in the 0.85 range are expected for complex CoT+code targets; simple instruction SFT typically reaches 0.3–0.5, but verified reasoning traces carry genuine entropy.
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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 # doubled vs Atem-Wisdom — CoT traces are long
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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 = 4 # halved vs Atem-Wisdom to account for 2× seq length
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grad_accumulation = 16 # effective batch size: 64
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num_epochs = 2
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dtype = bfloat16
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load_in_4bit = True # during training
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nothink_ratio = 0.10 # 10% direct-answer training pool
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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. A three-part pre-training validation was run before training: identity confirmation, double `<think>` tag detection, and mask sanity check. All checks passed before training was confirmed.
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---
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## Evaluation
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### Qualitative Coding Evaluation (8 / 30 questions shown)
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Atem-SageCoder was evaluated against a (Qwen/Qwen2.5-1.5B-Instruct) baseline across 30 coding questions covering implementation tasks, concept explanations, and algorithm design. The 8 coding-domain questions from that evaluation are shown below.
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| # | Question | Base | SageCoder | Notes |
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| 1 | `is_even(n)` function | ✓ No think | ✓ Think | Both correct |
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| 2 | Count vowels in string | ✓ No think | ✓ Think | SageCoder more Pythonic (generator expression) |
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| 3 | List vs tuple differences | ✓ No think | ⚠ Think | SageCoder error: claims tuples cannot contain duplicates (incorrect) |
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| 4 | Sum list without `sum()` | ✓ No think | ✓ Think | SageCoder more thorough, both correct |
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| 5 | Reverse a string | ✓ No think | ✓ Think | Both correct; SageCoder more verbose |
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| 6 | `if` / `elif` / `else` | ⚠ No think | ✓ Think | Base error: predicts wrong output for age=25 example |
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| 7 | `find_max()` with empty list | ✓ No think | ✓ Think | SageCoder provides two implementations |
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| 8 | `for` vs `while` loop | ✓ No think | ✓ Think | SageCoder more structured |
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**Summary across 8 questions:**
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| Metric | Baseline | Atem-SageCoder |
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| Think traces | 0 / 8 | 8 / 8 |
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| Avg response (words) | ~177 | ~470 |
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| Factual errors observed | 1 (Q6 output prediction) | 1 (Q3 tuple claim) |
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| Code correctness | 7 / 8 correct | 7 / 8 correct |
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The think-then-code pattern activates consistently on all coding questions. Response depth increases significantly — SageCoder examines edge cases, considers multiple approaches, and explains implementation choices that the baseline omits. Overall correctness is comparable across these 8 questions; the error types differ (baseline: incorrect output prediction; SageCoder: incorrect concept claim about tuples).
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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-SageCoder-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 finds all prime numbers up to n using the Sieve of Eratosthenes."
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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=2048,
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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-SageCoder-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": "Given an array of integers, find the two numbers that sum to a target value. Return their indices."
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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=2048,
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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-SageCoder-1.5B:Q4_K_M
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# Higher quality
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ollama run hf.co/EphAsad/Atem-SageCoder-1.5B:Q5_K_M
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# Near-lossless
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ollama run hf.co/EphAsad/Atem-SageCoder-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-SageCoder-1.5B:Q4_K_M
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```
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### System Prompt
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Atem-SageCoder's identity and coding focus are baked into the chat template. To override manually:
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```
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You are Atem-SageCoder, a thoughtful programming assistant built on the
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Atem foundation. You reason carefully through problems before writing code
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— considering edge cases, algorithm choice, complexity, and implementation
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details — then provide clean, correct, and well-structured implementations.
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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-SageCoder-1.5B.Q4_K_M.gguf` | ~986 MB | 4-bit quantised — recommended |
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| `Atem-SageCoder-1.5B.Q5_K_M.gguf` | ~1.1 GB | 5-bit quantised |
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| `Atem-SageCoder-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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**Training data scope.** All 15,427 training examples come from competitive programming problems in `nvidia/OpenCodeReasoning`. The model is strongest on algorithmic and data structure problems; general software engineering tasks (web APIs, OOP design, framework-specific code) were not represented in training and may produce lower quality output.
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**Factual concept errors.** The qualitative evaluation identified an incorrect claim about tuples (Q3: stated tuples cannot contain duplicates — they can). Concept explanation accuracy should be independently verified for correctness-critical applications.
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**Response length.** Think traces substantially increase output length. This is a fundamental property of the think-then-code design, not a fixable defect. For latency-constrained environments, Atem-Wisdom-1.5B with direct prompting may be preferable.
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**Single language bias.** OpenCodeReasoning solutions are predominantly Python. Performance on other languages has not been formally evaluated.
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**Small training set.** 15,427 examples is a focused dataset. Coverage of less common algorithmic patterns may be shallow. The high filter attrition rate (40k streamed → 15.4k retained) reflects the strict quality bar applied, not a shortage of data — the full split_0 contains substantially more examples at lower sequence lengths.
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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 — 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_sagecoder_2026,
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author = {Asad, Zain},
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title = {Atem-SageCoder: A 1.5B Think-Then-Code Model
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via Competitive Programming Trace Distillation},
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year = {2026},
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publisher = {HuggingFace},
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howpublished = {\url{https://huggingface.co/EphAsad/Atem-SageCoder-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-SageCoder).
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---
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Built independently by [EphAsad](https://huggingface.co/EphAsad)
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