Update README.md

README.md

@@ -1,3 +1,198 @@
 ---
 license: llama2
+datasets:
+- cerebras/SlimPajama-627B
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- Deci AI
+- DeciLM
+model-index:
+- name: DeciLM 6B
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: ai2/arc
+      name: ai2_arc
+    metrics:
+    - name: ARC Challenge
+      type: ARC Challenge
+      value: 42.06
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: ai2/arc
+      name: ai2_arc
+    metrics:
+    - name: ARC Easy
+      type: ARC Easy
+      value: 70.02
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: boolq
+      name: boolq
+    metrics:
+    - name: BoolQ
+      type: BoolQ
+      value: 71.01
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: hellaswag
+      name: hellaswag
+    metrics:
+    - name: HellaSwag
+      type: HellaSwag
+      value: 74.58
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: LAMBDA
+      name: OpenAI LAMBDA
+    metrics:
+    - name: LAMBDA
+      type: LAMBDA
+      value: 69.78
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: OpenBookQA
+      name: openbookqa
+    metrics:
+    - name: OpenBookQA
+      type: OpenBookQA
+      value: 34
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: PIQA
+      name: piqa
+    metrics:
+    - name: PIQA
+      type: PIQA
+      value: 77.09
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: truthful_qa
+      name: truthful_qa
+    metrics:
+    - name: TruthfulQA
+      type: TruthfulQA
+      value: 36.19
+      verified: false
+  - task:
+      type: text-generation
+    dataset:
+      type: winogrande
+      name: winogrande
+    metrics:
+    - name: Winogrande
+      type: Winogrande
+      value: 68.03
+      verified: false
 ---
+# DeciLM 6B
+
+DeciLM 6B is a 5.7 billion parameter decoder-only text generation model. With a context window of 4096 tokens, the highly efficient model uses variable Grouped-Query Attention (GQA) to achieve an optimal balance between performance and computational efficiency. The model's architecture was generated using Deci's proprietary Neural Architecture Search-based technology, AutoNAC. DeciLM 6B underwent training utilizing the SlimPajamas dataset, leveraging advanced proprietary methodologies allowing for fast training.
+
+## Model Details
+
+### Model Description
+
+Deci developed and publically released the DeciLM 6B large language model (LLM), a pretrained, high-efficiency generative text model with 5.7 billion parameters. DeciLM 6B outpaces pretrained models in its class, with a throughput that's up to 15 times that of Llama 2 7B's. DeciLM-6B was further LoRA fine-tuned for instruction following on a subset of the OpenOrca dataset, creating DeciLM 6B Instruct
+
+- **Developed by:** Deci
+- **Model type:** DeciLM is an auto-regressive language model using an optimized transformer decoder architecture that includes variable Grouped-Query Attention.
+- **Language(s) (NLP):** English
+- **License:**  [Llama 2 Community License Agreement](https://huggingface.co/Deci/DeciLM-6b/blob/main/LICENSE.md)
+
+## Model Architecture
+
+| Parameters | Layers | Heads  | Sequence Length  | GQA num_key_value_heads*  | Hidden Size  |
+|:----------|:----------|:----------|:----------|:----------|:----------|
+| 5.7B    | 32    | 32    | 4096   | Variable  | 4096 |  |
+
+*AutoNAC was employed to optimize the selection of the GQA num_key_value_heads for each layer of the model.
+
+- **Decoder layer:** Varible Grouped Query Attention. Grouped Query Attention (GQA) was introduced in [Ainslie et al., 2023](https://arxiv.org/abs/2305.13245)
+- **Position Embeddings:** Dynamic NTK Scaling Rotary Position Embeddings [Su et al., 2021](https://arxiv.org/abs/2104.09864)
+
+
+### Model Sources
+
+- **Paper:** [DeciLM Technical Blog](https://deci.ai/blog/decilm-15-times-faster-than-llama2-nas-generated-llm-with-variable-gqa/)
+- **Demo:** [DeciLM 6B Instruct Demo](https://huggingface.co/spaces/Deci/DeciLM-6b-instruct-Demo/)
+- **Notebook:** [DeciLM 6B Notebook](https://huggingface.co/Deci/DeciLM-6b-instruct)
+
+## Uses
+
+The model is intended for commercial and research use in English and can be fine-tuned for use in other languages.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```bibtex
+# pip install -q transformers
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+checkpoint = "Deci/DeciLM-6b"
+device = "cuda" # for GPU usage or "cpu" for CPU usage
+
+tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device)
+
+inputs = tokenizer.encode("In a shocking finding, scientists discovered a herd of unicorns living in", return_tensors="pt").to(device)
+outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Training Details
+
+DeciLM 6B underwent training utilizing a subset of the SlimPajamas dataset, leveraging advanced proprietary methodologies allowing for fast training.
+
+## Evaluation
+
+Below are DeciLM's 6B evaluation results.
+
+| Average | ARC Challenge* | ARC Easy* | BoolQ | HellaSwag* | LAMBDA OpenAI | OpenBookQA | PIQA | TruthfulQA | Winogrande |
+|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|
+| 60.33    | 42.06    | 70.02    | 71.01    | 74.58    | 69.78    | 34    | 77.09    |36.19    | 68.03    | 
+Accuracy-norm score*
+
+
+### Runtime Benchmarks
+
+|Inference Tool/Hardware | A10 (tokens/sec) |
+|:----------|:----------|
+| HF Inference Endpoints  | 652.49 | 
+| Infery LLM | 2,029.6  | 
+
+- Throughput (tokens/sec) - Measured with optimal batch - HF Inference Endpoints BS 64, Infery LLM BS 128
+
+
+## How to Cite
+
+Please cite this model using this format.
+
+```bibtex
+@misc{DeciFoundationModels,
+title = {DeciLM},
+author = {DeciAI Research Team},
+year = {2023}
+url={[https://huggingface.co/Deci/DeciLM-6b](https://huggingface.co/Deci/DeciLM-6b)},
+}
+```