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-Q5_K_M-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.

		Model 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-Q5_K_M-GGUF --hf-file athena-1-14b-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Athena-1-14B-Q5_K_M-GGUF --hf-file athena-1-14b-q5_k_m.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-Q5_K_M-GGUF --hf-file athena-1-14b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Athena-1-14B-Q5_K_M-GGUF --hf-file athena-1-14b-q5_k_m.gguf -c 2048
```
