README.md

---
base_model: Spestly/Athena-1-1.5B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---

# Triangle104/Athena-1-1.5B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Spestly/Athena-1-1.5B`](https://huggingface.co/Spestly/Athena-1-1.5B) 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-1-1.5B) for more details on the model.

---
Model details:
-
Athena-1 1.5B is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-1.5B-Instruct.
 Designed for efficiency and high-quality text generation, Athena-1 1.5B
 maintains a compact size, making it ideal for real-world applications 
where performance and resource efficiency are critical, such as 
lightweight applications, conversational AI, and structured data tasks.

		Key Features
	
		⚡ Lightweight and Efficient
	
Compact Size: At just 1.5 billion parameters, Athena-1 1.5B offers excellent performance with reduced computational requirements.
Instruction Following: Fine-tuned for precise and reliable adherence to user prompts.
Coding and Mathematics: Proficient in solving coding challenges and handling mathematical tasks.

		📖 Long-Context Understanding
	
Context Length: Supports up to 32,768 tokens, enabling the processing of moderately lengthy 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: 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/Qwen2.5-1.5B-Instruct
Architecture: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings.
Parameters: 1.5B total (Adjust non-embedding count if you have it).
Layers: (Adjust if different from the 3B model)
Attention Heads: (Adjust if different from the 3B model)
Context Length: Up to 32,768 tokens.

		Applications

Athena 1.5B is designed for a variety of real-world applications:

Conversational AI: Build fast, responsive, and lightweight chatbots.
Code Generation: Generate, debug, or explain code snippets.
Mathematical Problem Solving: Assist with calculations and reasoning.
Document Processing: Summarize and analyze moderately large documents.
Multilingual Applications: Support for global use cases with diverse language requirements.
Structured Data: Process and generate structured data, such as tables and JSON.

		Quickstart

Here’s how you can use Athena 1.5B 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-1-1.5B") # Update model name
print(pipe(messages))

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-1.5B") # Update model name
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-1.5B") # Update model name

---
## 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-1-1.5B-Q4_K_M-GGUF --hf-file athena-1-1.5b-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/Athena-1-1.5B-Q4_K_M-GGUF --hf-file athena-1-1.5b-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-1-1.5B-Q4_K_M-GGUF --hf-file athena-1-1.5b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/Athena-1-1.5B-Q4_K_M-GGUF --hf-file athena-1-1.5b-q4_k_m.gguf -c 2048
```