transformers gguf text-generation-inference unsloth qwen2 trl llama-cpp gguf-my-repo en base_model:Spestly/Athena-1-14B base_model:quantized:Spestly/Athena-1-14B license:apache-2.0 endpoints_compatible region:us conversational
---
base_model: Spestly/Athena-1-14B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- llama-cpp
- gguf-my-repo
---

# Triangle104/Athena-1-14B-Q4_K_S-GGUF
This model was converted to GGUF format from [`Spestly/Athena-1-14B`](https://huggingface.co/Spestly/Athena-1-14B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Spestly/Athena-1-14B) for more details on the model.

---
Model details:
-
Athena 1 is a state-of-the-art language model fine-tuned from Qwen/Qwen2.5-14B-Instruct.
 Designed to excel in instruction-following tasks, Athena 1 delivers 
advanced capabilities in text generation, coding, mathematics, and 
long-context understanding. It is optimized for a wide variety of use 
cases, including conversational AI, structured data interpretation, and 
multilingual applications. It outperforms Ava 1.5 in many aspects making
Athena-1 the superior model.

Key Features
-

🚀 Enhanced Capabilities
-
Instruction Following: Athena 1 has been fine-tuned for superior adherence to user prompts, making it ideal for chatbots, virtual assistants, and guided workflows. Coding and Mathematics: Specialized fine-tuning enhances coding problem-solving and mathematical reasoning. Long-Context Understanding: Handles input contexts up to 128K tokens and generates up to 8K tokens.

🌐 Multilingual Support
-
Supports 29+ languages, including:

English, Chinese, French, Spanish, Portuguese, German, Italian, Russian
Japanese, Korean, Vietnamese, Thai, Arabic, and more.

📊 Structured Data & Outputs
-
Structured Data Interpretation: Understands and processes structured formats like tables and JSON.
Structured Output Generation: Generates well-formatted outputs, including JSON, XML, and other structured formats.

Details
-
Base Model: Qwen/Qwen2.5-14B-Instruct
Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Parameters: 14.7B total (13.1B non-embedding).
Layers: 48
Attention Heads: 40 for Q, 8 for KV.
Context Length: Up to 131,072 tokens.

Applications
-
Athena 1 is designed for a wide range of use cases:

Conversational AI and chatbots.
Code generation, debugging, and explanation.
Mathematical problem-solving.
Large-document summarization and analysis.
Multilingual text generation and translation.
Structured data processing (e.g., tables, JSON).

Quickstart
-	
Below is an example of how to use Athena 1 for text generation:

huggingface-cli login

# Use a pipeline as a high-level helper
from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Spestly/Athena-1-14B")
pipe(messages)  

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-14B")
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-14B")

Performance
-	
Athena 1 has been optimized for efficiency and performance on modern 
GPUs. For detailed evaluation metrics (e.g., throughput, accuracy, and 
memory requirements), refer to the Qwen2.5 performance benchmarks.

Requirements
-	
To use Athena 1, ensure the following:

Python >= 3.8
Transformers >= 4.37.0 (to support Qwen models)
PyTorch >= 2.0
GPU with BF16 support for optimal performance.

Citation
-	

If you use Athena 1 in your research or projects, please cite its base model Qwen2.5 as follows:

@misc{qwen2.5,
    title = {Qwen2.5: A Party of Foundation Models},
    url = {https://qwenlm.github.io/blog/qwen2.5/},
    author = {Qwen Team},
    month = {September},
    year = {2024}
}

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/Athena-1-14B-Q4_K_S-GGUF --hf-file athena-1-14b-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Athena-1-14B-Q4_K_S-GGUF --hf-file athena-1-14b-q4_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Athena-1-14B-Q4_K_S-GGUF --hf-file athena-1-14b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Athena-1-14B-Q4_K_S-GGUF --hf-file athena-1-14b-q4_k_s.gguf -c 2048
```
