|
--- |
|
base_model: Spestly/AwA-1.5B |
|
tags: |
|
- text-generation-inference |
|
- transformers |
|
- unsloth |
|
- qwen2 |
|
- trl |
|
- llama-cpp |
|
- gguf-my-repo |
|
license: apache-2.0 |
|
language: |
|
- en |
|
library_name: transformers |
|
--- |
|
|
|
# Triangle104/AwA-1.5B-Q4_K_S-GGUF |
|
This model was converted to GGUF format from [`Spestly/AwA-1.5B`](https://huggingface.co/Spestly/AwA-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/AwA-1.5B) for more details on the model. |
|
|
|
--- |
|
Model details: |
|
- |
|
AwA (Answers with Athena) is my portfolio project, showcasing a cutting-edge Chain-of-Thought (CoT) reasoning model. I created AwA to excel in providing detailed, step-by-step answers to complex questions across diverse domains. This model represents my dedication to advancing AI’s capability for enhanced comprehension, problem-solving, and knowledge synthesis. |
|
Key Features |
|
|
|
Chain-of-Thought Reasoning: AwA delivers step-by-step breakdowns of solutions, mimicking logical human thought processes. |
|
|
|
Domain Versatility: Performs exceptionally across a wide range of domains, including mathematics, science, literature, and more. |
|
|
|
Adaptive Responses: Adjusts answer depth and complexity based on input queries, catering to both novices and experts. |
|
|
|
Interactive Design: Designed for educational tools, research assistants, and decision-making systems. |
|
|
|
Intended Use Cases |
|
|
|
Educational Applications: Supports learning by breaking down complex problems into manageable steps. |
|
|
|
Research Assistance: Generates structured insights and explanations in academic or professional research. |
|
|
|
Decision Support: Enhances understanding in business, engineering, and scientific contexts. |
|
|
|
General Inquiry: Provides coherent, in-depth answers to everyday questions. |
|
|
|
Type: Chain-of-Thought (CoT) Reasoning Model |
|
|
|
Base Architecture: Adapted from [qwen2] |
|
|
|
Parameters: [1.54B] |
|
|
|
Fine-tuning: Specialized fine-tuning on Chain-of-Thought reasoning datasets to enhance step-by-step explanatory capabilities. |
|
|
|
Ethical Considerations |
|
|
|
Bias Mitigation: I have taken steps to minimise biases in the training data. However, users are encouraged to cross-verify outputs in sensitive contexts. |
|
|
|
Limitations: May not provide exhaustive answers for niche topics or domains outside its training scope. |
|
|
|
User Responsibility: Designed as an assistive tool, not a replacement for expert human judgment. |
|
|
|
Usage |
|
Option A: Local |
|
|
|
Using locally with the Transformers library |
|
|
|
# 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/AwA-1.5B") |
|
pipe(messages) |
|
|
|
Option B: API & Space |
|
|
|
You can use the AwA HuggingFace space or the AwA API (Coming soon!) |
|
Roadmap |
|
|
|
More AwA model sizes e.g 7B and 14B |
|
Create AwA API via spestly package |
|
|
|
--- |
|
## 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/AwA-1.5B-Q4_K_S-GGUF --hf-file awa-1.5b-q4_k_s.gguf -p "The meaning to life and the universe is" |
|
``` |
|
|
|
### Server: |
|
```bash |
|
llama-server --hf-repo Triangle104/AwA-1.5B-Q4_K_S-GGUF --hf-file awa-1.5b-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/AwA-1.5B-Q4_K_S-GGUF --hf-file awa-1.5b-q4_k_s.gguf -p "The meaning to life and the universe is" |
|
``` |
|
or |
|
``` |
|
./llama-server --hf-repo Triangle104/AwA-1.5B-Q4_K_S-GGUF --hf-file awa-1.5b-q4_k_s.gguf -c 2048 |
|
``` |
|
|
