Add professional model card and metadata

README.md

@@ -1,14 +1,72 @@
 ---
 license: apache-2.0
-datasets:
-- tiiuae/falcon-refinedweb
-- HuggingFaceTB/cosmopedia
 language:
 - en
+base_model: []
 pipeline_tag: text-generation
+datasets:
+- HuggingFaceTB/cosmopedia
+- tiiuae/falcon-refinedweb
+library_name: gguf
 tags:
+- text-generation
 - gguf
 - quantized
-- 1B
+- 1b
 - llama-cpp
----
+---
+
+# PT1S-1B-Q8.gguf
+
+This model is a 1-billion parameter text generation model trained on a high-quality mixture of synthetic and web-crawled data. It is optimized for efficiency and performance in a small footprint.
+
+## Model Details
+
+- **Model Type:** Text Generation
+- **Parameters:** 1B
+- **Quantization:** Q8_0 (8-bit quantization for high precision with reduced memory)
+- **Training Data:** 
+  - [HuggingFaceTB/cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
+  - [tiiuae/falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
+- **Language(s):** English
+- **License:** Apache 2.0
+
+## Training Information
+
+The model was trained on a curated blend of:
+1. **Cosmopedia**: A large-scale synthetic dataset designed to provide high-quality educational content across various domains.
+2. **Falcon RefinedWeb**: A massive, filtered web dataset that provides broad world knowledge and linguistic diversity.
+
+This combination allows the model to have both structured knowledge from synthetic sources and a natural "web-aware" conversational style.
+
+## Usage
+
+### llama.cpp
+
+You can use this model with [llama.cpp](https://github.com/ggerganov/llama.cpp) by running:
+
+```bash
+./main -m PT1S-1B-Q8.gguf -p "Once upon a time," -n 128
+```
+
+### Python (via llama-cpp-python)
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(model_path="./PT1S-1B-Q8.gguf")
+output = llm("Q: What is the importance of cosmopedia dataset? A:", max_tokens=100)
+print(output)
+```
+
+## Intended Use
+
+This model is ideal for:
+- Lightweight text generation tasks.
+- Educational applications.
+- On-device inference where memory is limited.
+- Research into small language models (SLMs).
+
+## Limitations and Bias
+
+While trained on filtered data, small models may still exhibit biases or generate incorrect information (hallucinations). Users should always verify the output of the model for critical applications.

TM-1B-Q80.gguf

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