transformers gguf text-generation-inference unsloth qwen2 trl llama-cpp gguf-my-repo en base_model:Spestly/Athena-1-7B base_model:quantized:Spestly/Athena-1-7B license:apache-2.0 endpoints_compatible region:us conversational
---
base_model: Spestly/Athena-1-7B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- llama-cpp
- gguf-my-repo
---

# Triangle104/Athena-1-7B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Spestly/Athena-1-7B`](https://huggingface.co/Spestly/Athena-1-7B) 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-1-7B) for more details on the model.

---
Model details:
-
Athena-1 is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-7B-Instruct.
 Designed to balance efficiency and performance, Athena 7B provides 
powerful text-generation capabilities, making it suitable for a variety 
of real-world applications, including conversational AI, content 
creation, and structured data processing.




	
		
	

		Key Features
	




	
		
	

		🚀 Enhanced Performance
	



Instruction Following: Fine-tuned for excellent adherence to user prompts and instructions.
Coding and Mathematics: Proficient in solving coding problems and mathematical reasoning.
Lightweight: At 7.62 billion parameters, Athena-1-7B offers powerful performance while maintaining efficiency.



	
		
	

		📖 Long-Context Understanding
	



Context Length: Supports up to 128K tokens, ensuring accurate handling of large documents or conversations.
Token Generation: Can generate up to 8K tokens of output.



	
		
	

		🌍 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 and other structured formats.




	
		
	

		Model Details
	



Base Model: Qwen/Qwen2.5-7B-Instruct
Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Parameters: 7.62B total (6.53B non-embedding).
Layers: 28
Attention Heads: 28 for Q, 4 for KV.
Context Length: Up to 131,072 tokens.




	
		
	

		Applications
	



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


Conversational AI: Create natural, human-like chatbot experiences.
Code Generation: Generate, debug, or explain code snippets.
Mathematical Problem Solving: Assist with complex calculations and reasoning.
Document Processing: Summarize or analyze large documents.
Multilingual Applications: Support for diverse languages for translation and global use cases.
Structured Data: Process and generate structured data, including tables and JSON.




	
		
	

		Quickstart
	



Here’s how you can use Athena 7B for quick text generation:


# 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-1-7B")
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-7B")
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-7B")

---
## 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-1-7B-Q4_K_M-GGUF --hf-file athena-1-7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Athena-1-7B-Q4_K_M-GGUF --hf-file athena-1-7b-q4_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-1-7B-Q4_K_M-GGUF --hf-file athena-1-7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Athena-1-7B-Q4_K_M-GGUF --hf-file athena-1-7b-q4_k_m.gguf -c 2048
```
