---
license: apache-2.0
language:
- en
tags:
- atlas-coder
- atlas-coder-2
- qwen2.5-coder
- sub-1b
- qlora
- code-generation
- code-completion
- code-debugging
- coding
- fine-tuned
- text-generation-inference
- flagship
base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
pipeline_tag: text-generation
library_name: transformers
datasets:
- bigcode/self-oss-instruct-sc2-exec-filter-50k
- Siddh07ETH/Atlas-Coder-50K-ChatML
model-index:
- name: Atlas-Coder-2-0.5B
  results: []
---

<div align="center">

# ⚡ Atlas-Coder-2-0.5B

<p align="center">
  <img src="./banner.png" width="100%">
</p>

**🛠️ Atlas-Coder-2-0.5B: The Top Sub-1B Coding Model**

**Ranked #Top 5 globally for strictly sub-1B parameter models on the EvalPlus (HumanEval+) strict benchmark.**

[![HuggingFace](https://img.shields.io/badge/🤗-HuggingFace-yellow)](https://huggingface.co/Siddh07ETH/Atlas-Coder-2-0.5B)
[![License](https://img.shields.io/badge/License-Apache%202.0-blue)](https://opensource.org/licenses/Apache-2.0)
[![Model Size](https://img.shields.io/badge/Parameters-494M-green)]()
[![Base Model](https://img.shields.io/badge/Base-Qwen2.5--Coder--0.5B--Instruct-orange)](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct)
[![GGUF](https://img.shields.io/badge/GGUF-Available-purple)](https://huggingface.co/Siddh07ETH/Atlas-Coder-2-0.5B-GGUF)
[![Version](https://img.shields.io/badge/Version-2.0-red)]()

</div>

---

## Model Description

**Atlas-Coder-2-0.5B** is the flagship model of the **Pluto AI** research project by Siddharth N.R. — the second generation of the Atlas-Coder series and the most focused coding model released under the Pluto AI brand to date.

Built on top of [Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct), Atlas-Coder-2 is trained exclusively on **50K execution-verified OSS-Instruct samples** — real open-source Python functions that have been independently verified to execute correctly. This single-source, high-purity data strategy maximizes alignment with HumanEval+ and MBPP+ benchmark formats while keeping the training signal clean and consistent.

Unlike V1 which trained from the base model and used a 4-source mixture, Atlas-Coder-2 starts from an instruction-tuned foundation and specializes it further on execution-verified code. The result is a sharper, more reliable code generator with a lower hallucination rate on self-contained Python tasks.

> **Research Goal:** Demonstrate that a sub-500M parameter model, fine-tuned exclusively on execution-verified code in a single Kaggle session, can match or exceed the coding performance of officially released instruct variants and outperform models up to 3× its parameter count.

---

## 📊 Benchmarks

<p align="center">
  <img src="./benchmark_atlas.png" width="100%">
</p>

*Competitor scores from official technical reports.*

---

>**⚡ Edge Performance:**
>Tested locally on an M2 MacBook Air (8GB RAM) using LM Studio with the **F16 GGUF**. Achieved 75 tokens/second generation speed. Because the model uses native **FP16** precision, it bypasses quantization overhead and fully utilizes Apple Silicon's Metal FP16 vector cores. Zero GPU required.



**GGUF Quantizations**

>https://huggingface.co/Siddh07ETH/Atlas-Coder-2-0.5B-GGUF

## What Changed from V1

| Property | Atlas-Coder-0.5B (V1) | Atlas-Coder-2-0.5B (V2) |
|----------|-----------------------|--------------------------|
| Base model | Qwen2.5-Coder-0.5B **Base** | Qwen2.5-Coder-0.5B **Instruct** |
| Data sources | 4 (Magicoder + OSS + CodeFeedback + TACO) | 1 (OSS-Instruct exec-verified only) |
| Training samples | ~80K (mixed quality) | 50K (100% exec-verified) |
| LoRA rank | r=64 | r=32 (faster, leaner) |
| Epochs | 3 | 1 (instruct base needs less) |
| Sequence length | 1024 | 1024 |
| Response masking | ✅ | Standard LM loss |
| Final loss | 0.0294 | **0.0596** (healthy — not overfit) |
| Training time | ~42h 44m | ~9.5h |
| Fits in 1 Kaggle session | ❌ (needed resume) | ✅ |

The key architectural insight of V2: starting from an instruct model means the model already knows how to follow instructions and stop generating. V2 doesn't need to re-learn conversation structure — it only needs to deepen its Python code generation capability. This allows a single clean epoch on a smaller, higher-quality dataset to outperform a longer multi-epoch run on a noisier mixture.

---

## Training Details

| Property | Value |
|----------|-------|
| **Base Model** | Qwen/Qwen2.5-Coder-0.5B-Instruct |
| **Parameters** | ~494 Million |
| **Method** | QLoRA (4-bit NF4 + LoRA) |
| **LoRA Rank** | r=32, α=64 |
| **LoRA Dropout** | 0.05 |
| **LoRA Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| **Trainable Parameters** | 17,596,416 |
| **Training Epochs** | 1 |
| **Training Steps** | ~1,568 (resumed from checkpoint 250) |
| **Final Training Loss** | 0.0596 |
| **Precision** | FP16 (forced — T4 sm_75 does not support BF16) |
| **Optimizer** | AdamW 8-bit (Paged) |
| **Learning Rate** | 1e-4 (cosine schedule) |
| **Warmup Steps** | 100 |
| **Effective Batch Size** | 32 (2 × 16 grad accum) |
| **Sequence Length** | 1024 tokens |
| **Hardware** | Tesla T4 (16 GB VRAM) — Kaggle free tier |
| **Training Time** | ~9.5 hours (single session) |
| **Framework** | Transformers 4.52.4 + PEFT 0.17.0 + TRL 0.19.1 |
| **Chat Template** | ChatML (inherited from base instruct model) |

---

## Training Data

| Dataset | Samples | Why This Dataset |
|---------|---------|-----------------|
| [bigcode/self-oss-instruct-sc2-exec-filter-50k](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k) | **50,154** (train) + 508 (eval) | 100% execution-verified. Generated from real open-source Python. Single-function format directly mirrors HumanEval+ problem structure. No hallucinated solutions — every completion has been independently run and confirmed correct. |

**Why single-source?** The V1 multi-dataset mixture introduced noise from TACO (competitive programming verbosity) and CodeFeedback (multi-turn debug style), both of which poorly align with HumanEval+ single-function completion format. V2 eliminates this noise entirely. The OSS-Instruct exec-filtered dataset is already the highest-ROI data source for HumanEval+ performance — using 50K samples of it exclusively produces a cleaner gradient signal than mixing 80K samples of heterogeneous quality.

---

## Key Design Decisions

**1. Instruct base = faster convergence**
Starting from `Qwen2.5-Coder-0.5B-Instruct` means the ChatML format, stop-token behavior, and instruction-following discipline are already in place. The model only needs to deepen code generation quality — not learn conversation structure from scratch. This makes 1 epoch sufficient where V1 needed 3.

**2. Execution-verified data only**
Every training sample in OSS-Instruct exec-filter-50k has been independently run and verified to produce correct output. This eliminates a significant noise source that affects most open-source fine-tuning datasets: plausible-looking but incorrect code completions that silently degrade model performance on pass@1 metrics.

**3. r=32 LoRA for speed without sacrificing quality**
At the 0.5B parameter scale, r=64 provides diminishing returns over r=32 while doubling the LoRA parameter count and training time. The r=32 configuration trains ~40% faster on the T4, allowing full training within a single Kaggle 9-hour session without checkpoint recovery.

**4. Single Kaggle session design**
The entire pipeline — install → load → data → train → merge → upload → GGUF — is designed to complete within a single 9-hour Kaggle session. The 3-layer checkpoint recovery system (local → HuggingFace Hub → fresh start) handles session interruptions automatically when they occur.

---

## Usage

### Basic Inference

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "Siddh07ETH/Atlas-Coder-2-0.5B",
    torch_dtype=torch.float16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Siddh07ETH/Atlas-Coder-2-0.5B")

messages = [
    {
        "role": "system",
        "content": "You are a helpful coding assistant."
    },
    {
        "role": "user",
        "content": "Write a Python function to find all prime numbers up to n using the Sieve of Eratosthenes."
    }
]

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

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.2,
        do_sample=True,
        top_p=0.9,
        repetition_penalty=1.1,
    )

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

### Low Memory Inference (4-bit)

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

quant_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)
model = AutoModelForCausalLM.from_pretrained(
    "Siddh07ETH/Atlas-Coder-2-0.5B",
    quantization_config=quant_config,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("Siddh07ETH/Atlas-Coder-2-0.5B")
```

### GGUF (Ollama / LM Studio / llama.cpp)

GGUF quantizations for CPU inference are available at:

> **[Siddh07ETH/Atlas-Coder-2-0.5B-GGUF](https://huggingface.co/Siddh07ETH/Atlas-Coder-2-0.5B-GGUF)**

Runs at 40+ tokens/second on a laptop CPU. No GPU required.

---

## Recommended Generation Settings

| Setting | Value | Reason |
|---------|-------|--------|
| `temperature` | 0.1–0.3 | Low temperature for precise code generation |
| `top_p` | 0.9 | Focused vocabulary sampling |
| `repetition_penalty` | 1.1 | Prevents repeated patterns |
| `max_new_tokens` | 256–512 | Sufficient for most single-function tasks |
| `do_sample` | `True` | Required when temperature < 1.0 |

---

## Important: Benchmark Evaluation Setup

If you are evaluating this model with EvalPlus, you **must** pass `--model_type instruct`:

```bash
python -m evalplus.evaluate   --model Siddh07ETH/Atlas-Coder-2-0.5B   --dataset humaneval   --backend hf   --model_type instruct   --greedy
```

Without `--model_type instruct`, EvalPlus sends raw function signatures without the ChatML wrapper. This causes the model to score near 0% — which is an evaluation configuration error, not a reflection of model quality. The model was trained exclusively on ChatML-formatted prompts and will not respond meaningfully to bare code signatures.

---

## Model Lineage

Atlas-Coder-2 is the second release in the Atlas-Coder series under Pluto AI. Each version refines the strategy based on lessons from the previous run.

| Version | Base | Strategy | Status |
|---------|------|----------|--------|
| [Atlas-Coder-0.5B (V1)](https://huggingface.co/Siddh07ETH/Atlas-Coder-0.5B) | Qwen2.5-Coder-0.5B Base | 4-source 80K mixture, 3 epochs, r=64 | Published |
| **Atlas-Coder-2-0.5B (V2)** | Qwen2.5-Coder-0.5B-Instruct | 50K exec-verified, 1 epoch, r=32 | **Flagship — this model** |

---

## Related Models

| Model | Parameters | Description |
|-------|-----------|-------------|
| **Atlas-Coder-2-0.5B (this)** | 494M | Flagship — exec-verified, instruct base |
| [Atlas-Coder-0.5B (V1)](https://huggingface.co/Siddh07ETH/Atlas-Coder-0.5B) | 494M | First generation, base model fine-tune |
| [Pluto-Genesis-0.6B](https://huggingface.co/Siddh07ETH/Pluto-Genesis-0.6B) | 596M | General reasoning, math, and code |

---

## Limitations

- **Size:** At ~494M parameters this model will make mistakes on complex multi-file tasks and deeply nested logic. Always verify generated code before running it in production.
- **Context length:** Trained on sequences up to 1024 tokens. Performance may degrade on prompts or completions requiring longer context.
- **Language bias:** Optimized primarily for Python. Other languages will work but with lower reliability than a multilingual fine-tune.
- **Single-domain training:** Trained entirely on OSS-Instruct data. May underperform on highly domain-specific code (e.g., embedded systems, CUDA kernels) that differs from typical open-source Python patterns.
- **Research only:** Not intended for production deployment without further evaluation and safety testing.

---

## Author

**Siddharth N.R**
Graduated B.Tech — AI & Data Science
Pluto AI Research

[![HuggingFace](https://img.shields.io/badge/🤗-Siddh07ETH-yellow)](https://huggingface.co/Siddh07ETH)

---

## Citation

```bibtex
@misc{atlascoder2_2026,
  author    = {Siddharth N.R.},
  title     = {Atlas-Coder-2-0.5B: Execution-Verified QLoRA Fine-Tuning from an Instruct Base for Sub-1B Code Generation},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/Siddh07ETH/Atlas-Coder-2-0.5B}
}
```

---

## License

Apache 2.0 — see [LICENSE](https://opensource.org/licenses/Apache-2.0).
Base model [Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) is also Apache 2.0.