> ## Documentation Index
> Fetch the complete documentation index at: https://docs.arc.computer/llms.txt
> Use this file to discover all available pages before exploring further.

# Models

> Pre-trained ATLAS teacher models available on Hugging Face

## Available Models

ATLAS provides pre-trained teacher models optimized for different tasks. All models are 8B parameters and trained using the GRPO algorithm with adaptive dual-agent objectives.

## Teacher Models

### ATLAS-8B-Thinking

<Card title="View on Hugging Face" icon="hugging-face" href="https://huggingface.co/Arc-Intelligence/ATLAS-8B-Thinking">
  Optimized for mathematical and logical reasoning tasks
</Card>

**Best for:**

* Mathematical problem solving
* Logical reasoning
* Abstract thinking tasks
* Scientific analysis

**Usage:**

```python theme={null}
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Arc-Intelligence/ATLAS-8B-Thinking",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
    "Arc-Intelligence/ATLAS-8B-Thinking"
)
```

**Training:**

* Base model: Qwen2.5-7B-Instruct
* Training method: SFT → GRPO
* Specialization: Reasoning-heavy tasks

### ATLAS-8B-Instruct

<Card title="View on Hugging Face" icon="hugging-face" href="https://huggingface.co/Arc-Intelligence/ATLAS-8B-Instruct">
  Optimized for code generation and technical instruction
</Card>

**Best for:**

* Code generation
* Technical documentation
* System administration
* API integration

**Usage:**

```python theme={null}
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Arc-Intelligence/ATLAS-8B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(
    "Arc-Intelligence/ATLAS-8B-Instruct"
)
```

**Training:**

* Base model: Qwen2.5-7B-Instruct
* Training method: SFT → GRPO
* Specialization: Instruction-following and coding

## Model Selection Guide

Choose the appropriate teacher model based on your task:

| Task Type       | Recommended Model | Reasoning                             |
| --------------- | ----------------- | ------------------------------------- |
| Math problems   | ATLAS-8B-Thinking | Specialized in step-by-step reasoning |
| Debugging       | ATLAS-8B-Instruct | Better at code understanding          |
| Data analysis   | ATLAS-8B-Thinking | Strong analytical capabilities        |
| API development | ATLAS-8B-Instruct | Trained on technical documentation    |
| Logic puzzles   | ATLAS-8B-Thinking | Abstract reasoning focus              |
| DevOps tasks    | ATLAS-8B-Instruct | System administration expertise       |

## Compatible Student Models

ATLAS teachers can enhance any instruction-following LLM:

**Tested Student Models:**

* Qwen/Qwen3-4B-Instruct (4B)
* meta-llama/Llama-3.2-8B-Instruct (8B)
* mistralai/Mixtral-8x7B-Instruct-v0.1 (47B)
* OpenAI GPT-4 (API)
* Anthropic Claude (API)

**Requirements:**

* Instruction-following capability
* Context window ≥4K tokens
* Support for system prompts (preferred)

## Memory Requirements

Estimated VRAM usage for inference:

| Configuration           | VRAM Required | Recommended Hardware |
| ----------------------- | ------------- | -------------------- |
| Teacher only (FP16)     | 16GB          | RTX 4080, A5000      |
| Teacher + Small Student | 24GB          | RTX 4090, A6000      |
| Teacher + Large Student | 40GB+         | A100, H100           |
| Quantized (INT8)        | 8GB           | RTX 3080, A4000      |
| Quantized (INT4)        | 4GB           | RTX 3070, T4         |

## Model Versioning

All models follow semantic versioning:

* **Latest stable**: No suffix (recommended for production)
* **Experimental**: `-experimental` suffix
* **Specific versions**: `-v1.0`, `-v1.1`, etc.

Check model cards on Hugging Face for:

* Training data details
* Performance benchmarks
* Known limitations
* Update changelog

## Custom Model Training

To train custom teacher models, see:

* [GKD Training](/training/offline/gkd-training) - Fast distillation (4-8 hours)
* [GRPO Training](/training/offline/grpo-training) - Full RL training (24-48 hours)
* [Training Configuration](/training/configuration) - Parameter reference

## License and Usage

All ATLAS models are released under Apache 2.0 license for both research and commercial use.

**Responsible Use:**

* Verify outputs for critical applications
* Monitor for potential biases
* Respect base model licenses
* Cite ATLAS in publications

## Next Steps

<CardGroup cols="2">
  <Card title="Quickstart" icon="rocket" href="/sdk/quickstart">
    Start using ATLAS models
  </Card>

  <Card title="Datasets" icon="database" href="/reference/datasets">
    Training and evaluation data
  </Card>

  <Card title="Custom Training" icon="dumbbell" href="/training/offline/grpo-training">
    Train your own models
  </Card>
</CardGroup>