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--- |
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base_model: Spestly/Athena-1-0.5B |
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tags: |
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- text-generation-inference |
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- transformers |
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- unsloth |
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- qwen2 |
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- trl |
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- llama-cpp |
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- gguf-my-repo |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# Triangle104/Athena-1-0.5B-Q5_K_M-GGUF |
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This model was converted to GGUF format from [`Spestly/Athena-1-0.5B`](https://huggingface.co/Spestly/Athena-1-0.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co/Spestly/Athena-1-0.5B) for more details on the model. |
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--- |
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Model details: |
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Athena-1 0.5B is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-0.5B-Instruct. |
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Designed for ultra-lightweight applications, Athena-1 0.5B balances |
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compactness with robust performance, making it suitable for tasks with |
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limited computational resources. |
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Key Features |
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⚡ Ultra-Lightweight and Efficient |
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Compact Size: With just 500 million parameters, Athena-1 0.5B is ideal for edge devices and low-resource environments. |
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Instruction Following: Fine-tuned for reliable adherence to user instructions. |
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Coding and Mathematics: Capable of handling basic coding and mathematical tasks. |
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📖 Contextual Understanding |
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Context Length: Supports up to 16,384 tokens, enabling processing of moderately sized conversations or documents. |
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Token Generation: Can generate up to 4K tokens of coherent output. |
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🌍 Multilingual Support |
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Supports 20+ languages, including: |
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English, Chinese, French, Spanish, German, Italian, Russian |
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Japanese, Korean, Vietnamese, Thai, and more. |
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📊 Structured Data & Outputs |
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Structured Data Interpretation: Handles formats like tables and JSON effectively. |
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Structured Output Generation: Produces well-formatted outputs for data-specific tasks. |
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Model Details |
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Base Model: Qwen/Qwen2.5-0.5B-Instruct |
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Architecture: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings. |
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Parameters: 500M total. |
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Layers: (Adjust if different from the base model) |
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Attention Heads: (Adjust if different from the base model) |
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Context Length: Up to 16,384 tokens. |
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Applications |
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Athena-1 0.5B is optimized for: |
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Conversational AI: Power lightweight and responsive chatbots. |
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Code Assistance: Basic code generation, debugging, and explanations. |
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Mathematical Assistance: Solves fundamental math problems. |
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Document Processing: Summarizes and analyzes smaller documents effectively. |
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Multilingual Tasks: Supports global use cases with a compact model. |
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Structured Data: Reads and generates structured formats like JSON and tables. |
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Quickstart |
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Here’s how you can use Athena-1 0.5B for quick text generation: |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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messages = [ |
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{"role": "user", "content": "What can you do?"}, |
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] |
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pipe = pipeline("text-generation", model="Spestly/Athena-1-0.5B") # Update model name |
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print(pipe(messages)) |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-0.5B") # Update model name |
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model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-0.5B") # Update model name |
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--- |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo Triangle104/Athena-1-0.5B-Q5_K_M-GGUF --hf-file athena-1-0.5b-q5_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo Triangle104/Athena-1-0.5B-Q5_K_M-GGUF --hf-file athena-1-0.5b-q5_k_m.gguf -c 2048 |
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``` |
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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. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo Triangle104/Athena-1-0.5B-Q5_K_M-GGUF --hf-file athena-1-0.5b-q5_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo Triangle104/Athena-1-0.5B-Q5_K_M-GGUF --hf-file athena-1-0.5b-q5_k_m.gguf -c 2048 |
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``` |
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