Adding readme

README.md

@@ -6,5 +6,45 @@ colorTo: blue
 sdk: static
 pinned: false
 ---
+<h1 style="line-height: 50px;"> Spectra Suite
+
+We release the Spectra Suite consisting of 54 models ranging from 99M to 3.9B parameters across different bitwidths:
+
+FloatLM: LLMs pretrained in FP16 (Half-Precision).
+TriLM: LLMs pretrained with effective ternary bitwidth.
+QuantLM 8-bit: FloatLM LLMs Quantized to 8-bits.
+QuantLM 6-bit: FloatLM LLMs Quantized to 6-bits.
+QuantLM 4-bit: FloatLM LLMs Quantized to 4-bits.
+QuantLM 3-bit: FloatLM LLMs Quantized to 3-bits.
+All models are released in unpacked (FP16 format) - compatible with FP16 GEMMs across any library supporting the LLaMa architecture.
+
+## Usage:
+
+```python
+import transformers as tf, torch
+
+# Please select the model you wish to run.
+model_name = "SpectraSuite/TriLM_3.9B_Unpacked"
+
+# Please adjust the temperature, repetition penalty, top_k, top_p and other sampling parameters according to your needs.
+pipeline = tf.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.float16}, device_map="auto")
+
+# These are base (pretrained) LLMs that are not instruction and chat tuned. You may need to adjust your prompt accordingly.
+pipeline("Once upon a time")
+```
+
+## Citation
+If you find these models or the associated paper useful, please cite the paper:
+
+```bibtex
+@misc{kaushal2024spectracomprehensivestudyternary,
+      title={Spectra: A Comprehensive Study of Ternary, Quantized, and FP16 Language Models}, 
+      author={Ayush Kaushal and Tejas Pandey and Tejas Vaidhya and Aaryan Bhagat and Irina Rish},
+      year={2024},
+      eprint={2407.12327},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2407.12327}, 
+}
+```
 
-Edit this `README.md` markdown file to author your organization card.