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--- |
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license: [llama2, other] |
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datasets: |
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- cerebras/SlimPajama-627B |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- Deci AI |
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- DeciLM |
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model-index: |
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- name: DeciLM 6B |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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type: ai2/arc |
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name: ai2_arc |
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metrics: |
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- name: ARC Challenge |
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type: ARC Challenge |
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value: 42.06 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: ai2/arc |
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name: ai2_arc |
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metrics: |
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- name: ARC Easy |
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type: ARC Easy |
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value: 70.02 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: boolq |
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name: boolq |
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metrics: |
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- name: BoolQ |
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type: BoolQ |
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value: 71.01 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: hellaswag |
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name: hellaswag |
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metrics: |
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- name: HellaSwag |
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type: HellaSwag |
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value: 74.58 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: LAMBDA |
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name: OpenAI LAMBDA |
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metrics: |
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- name: LAMBDA |
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type: LAMBDA |
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value: 69.78 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: OpenBookQA |
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name: openbookqa |
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metrics: |
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- name: OpenBookQA |
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type: OpenBookQA |
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value: 34 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: PIQA |
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name: piqa |
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metrics: |
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- name: PIQA |
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type: PIQA |
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value: 77.09 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: truthful_qa |
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name: truthful_qa |
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metrics: |
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- name: TruthfulQA |
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type: TruthfulQA |
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value: 36.19 |
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verified: false |
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- task: |
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type: text-generation |
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dataset: |
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type: winogrande |
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name: winogrande |
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metrics: |
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- name: Winogrande |
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type: Winogrande |
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value: 68.03 |
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verified: false |
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--- |
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# DeciLM 6B |
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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. |
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## Model Details |
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### Model Description |
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Deci developed and publically released the DeciLM 6B large language model, 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 fine-tuned using [LoRA ](https://arxiv.org/pdf/2106.09685.pdf) for instruction following on a subset of the OpenOrca dataset, creating [DeciLM 6B-Instruct](https://huggingface.co/Deci/DeciLM-6b-instruct) |
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- **Developed by:** Deci |
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- **Model type:** DeciLM is an auto-regressive language model using an optimized transformer decoder architecture that includes variable Grouped-Query Attention. |
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- **Language(s) (NLP):** English |
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- **License:** [Llama 2 Community License Agreement](https://huggingface.co/Deci/DeciLM-6b/blob/main/LICENSE.md) with an extention of Deci regarding hosting service providers. |
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## Model Architecture |
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| Parameters | Layers | Heads | Sequence Length | GQA num_key_value_heads* | Hidden Size | |
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|:----------|:----------|:----------|:----------|:----------|:----------| |
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| 5.7B | 32 | 32 | 4096 | Variable | 4096 | | |
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*AutoNAC was employed to optimize the selection of the GQA num_key_value_heads for each layer of the model. |
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- **Decoder layer:** Varible Grouped Query Attention. Grouped Query Attention (GQA) was introduced in [Ainslie et al., 2023](https://arxiv.org/abs/2305.13245) |
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- **Position Embeddings:** Dynamic NTK Scaling Rotary Position Embeddings [Su et al., 2021](https://arxiv.org/abs/2104.09864) |
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### Model Sources |
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- **Paper:** [DeciLM Technical Blog](https://deci.ai/blog/decilm-15-times-faster-than-llama2-nas-generated-llm-with-variable-gqa/?utm_campaign=repos&utm_source=hugging-face&utm_medium=model-card&utm_content=decilm-6b) |
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- **Demo:** [DeciLM 6B Instruct Demo](https://huggingface.co/spaces/Deci/DeciLM-6b-instruct) |
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- **Notebook:** [DeciLM 6B Notebook](https://colab.research.google.com/drive/1LugJCifOv0L426ukRHjOblBRWwUImAit) |
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## Uses |
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The model is intended for commercial and research use in English and can be fine-tuned for use in other languages. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```bibtex |
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# pip install -q transformers |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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checkpoint = "Deci/DeciLM-6b" |
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device = "cuda" # for GPU usage or "cpu" for CPU usage |
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tokenizer = AutoTokenizer.from_pretrained(checkpoint) |
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model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device) |
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inputs = tokenizer.encode("In a shocking finding, scientists discovered a herd of unicorns living in", return_tensors="pt").to(device) |
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outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Training Details |
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DeciLM 6B underwent training utilizing a subset of the SlimPajamas dataset, leveraging advanced proprietary methodologies allowing for fast training. |
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## Evaluation |
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Below are DeciLM's 6B evaluation results. |
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| Average | ARC Challenge* | ARC Easy* | BoolQ | HellaSwag* | LAMBDA OpenAI | OpenBookQA | PIQA | TruthfulQA | Winogrande | |
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|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------|:----------| |
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| 60.33 | 42.06 | 70.02 | 71.01 | 74.58 | 69.78 | 34 | 77.09 |36.19 | 68.03 | |
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Accuracy-norm score* |
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### Runtime Benchmarks |
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|Inference Tool/Hardware | A10 (tokens/sec) | |
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|:----------|:----------| |
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| PyTorch | 652.49 | |
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| Infery LLM | 2,029.6 | |
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- Throughput (tokens/sec) - Measured with optimal batch - PyTorch BS 64, Infery LLM BS 128 |
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- In order to replicate the results of the PyTorch benchmark, use this [code example](https://huggingface.co/Deci/DeciLM-6b/blob/main/hf_benchmark_example.py) |
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## How to Cite |
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Please cite this model using this format. |
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```bibtex |
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@misc{DeciFoundationModels, |
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title = {DeciLM 6B}, |
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author = {DeciAI Research Team}, |
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year = {2023} |
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url={[https://huggingface.co/Deci/DeciLM-6b](https://huggingface.co/Deci/DeciLM-6b)}, |
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} |
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``` |
