[WIP] Upload folder using huggingface_hub (multi-commit 776e029ac2e171ec92097c011f9aab9f738e101019578fcc0ccecd1b3703eda1)
#1
by
jayr014
- opened
- LICENSE +0 -176
- README.md +0 -167
- config.json +0 -42
- merges.txt +0 -0
- pytorch_model-00001-of-00006.bin +0 -3
- pytorch_model-00002-of-00006.bin +0 -3
- pytorch_model-00003-of-00006.bin +0 -3
- pytorch_model-00004-of-00006.bin +0 -3
- pytorch_model-00005-of-00006.bin +0 -3
- pytorch_model-00006-of-00006.bin +0 -3
- pytorch_model.bin.index.json +0 -571
- special_tokens_map.json +0 -5
- tokenizer.json +0 -0
- tokenizer_config.json +0 -10
- vocab.json +0 -0
LICENSE
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README.md
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---
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# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
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# Doc / guide: https://huggingface.co/docs/hub/model-cards
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license: apache-2.0
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inference: false
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---
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# SN-13B-8k-Instruct
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<!-- Provide a quick summary of what the model is/does. -->
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SN-13B-8k-Instruct is a 13 billion parameter model. It was pretrained as well as instruction tuned on
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[SambaNova DataScale systems](https://sambanova.ai/products/datascale/). This model is meant to be used for tasks requiring long sequence understanding.
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [SambaNova Systems](https://sambanova.ai/)
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- **Model type:** Language Model
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- **Language(s):** English
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- **License:** Apache 2.0
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### Basic Information
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<!-- Provide the basic links for the model. -->
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- **Blog Post**: [Link](https://sambanova.ai/blog/training-long-sequence-size-models-on-sambanova/)
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- **Discord**: [Link](https://discord.com/invite/8z2Pe7cpRv)
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<!-- - **Github**: [Link](https://github.com/sambanova/bloomchat) -->
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### Licensing
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To increase accessibility and to support the open-source community, SambaNova is releasing SN-13B-8k-Instruct under an Apache 2.0 license. [Please review SambaNova’s SN-13B-8k-Instruct License](LICENSE)
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## Uses
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<details>
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<summary>Click to expand</summary>
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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This model is intended for commercial and research use.
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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SN-13B-8k-Instruct should NOT be used for:
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- Mission-critical applications
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- Applications that involve the safety of others
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- Making highly important decisions
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- Important automated pipelines
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This model is still in early development and can be prone to mistakes and hallucinations, there is still room for improvement. This model is intended to provide the community with a multilingual chat LLM baseline.
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users should be made aware of the risks, biases, limitations, and restrictions of the model, which are listed down at the bottom of the page.
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</details>
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---
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## Running the model
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SN-13B-8k-Instruct")
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model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SN-13B-8k-Instruct")
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prompt = 'Define Machine Learning.'
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inputs = tokenizer(prompt, return_tensors='pt')
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# SN-13B-8k-Instruct occasionally repeats itself when do_sample=False.
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# Set do_sample=True when using the model to avoid this.
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outputs = model.generate(**inputs, use_cache=True, max_new_tokens=50, do_sample=False)
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print(tokenizer.batch_decode(outputs))
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```
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---
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## Training Details
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<details>
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<summary>Click to expand</summary>
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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We trained SN-13B-8k-Instruct with [SambaNova DataScale systems](https://sambanova.ai/products/datascale/) with
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SambaNova's in-house Reconfigurable Dataflow Unit (RDU). We started from random weights, and pretrained for 300 Billion
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tokens on sequences of size 2048. We then pretrained for another 250 Billion tokens on sequences of size 8192.
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During this phase of training, we curated a dataset that had a large proportion of long sequence articles, with
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30% of our articles consisting of greater than 6000 words.
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We applied instruction tuning on a variety of tasks derived from datasets such as FLANv2, P3, Natural Instructions, etc.
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### Hyperparameters
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**Pretraining on 8k SS**
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- Hardware: SambaNova Reconfigurable Dataflow Unit (RDU)
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- Optimizer: AdamW
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- Steps: 60000
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- Global Batch size: 1024
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- Learning Rate: 1e-5
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- Learning Rate Scheduler: Fixed
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- Warmup Steps: 0
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- Weight decay: 0.1
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**Instruction-tuned Training**
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- Hardware: SambaNova Reconfigurable Dataflow Unit (RDU)
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- Optimizer: AdamW
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- Steps: 35000
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- Global Batch size: 64
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- Learning Rate: 1e-5
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- Learning Rate Scheduler: Fixed
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- Warmup Steps: 0
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- Weight decay: 0.1
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</details>
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---
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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Like all LLMs, SN-13B-8k-Instruct has certain limitations:
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- Hallucination: SN-13B-8k-Instruct may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.
|
143 |
-
- Repetition: SN-13B-8k-Instruct may produce repetitive phrases or sentences, leading to less engaging and informative responses.
|
144 |
-
- Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.
|
145 |
-
- Toxicity: SN-13B-8k-Instruct may inadvertently generate responses containing inappropriate or harmful content.
|
146 |
-
|
147 |
-
## Acknowledgment
|
148 |
-
|
149 |
-
We appreciate [Scrolls](https://www.scrolls-benchmark.com/) and [ZeroScrolls](https://www.zero.scrolls-benchmark.com/) for their contributions in creating effective benchmarks to test the long sequence understanding of Large Language Models.
|
150 |
-
We appreciate [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness) and [HELM](https://crfm.stanford.edu/helm/latest/) for their essential benchmarking contributions,
|
151 |
-
which were both very helpful in evaluating SN-13B-8k-Instruct's performance. We appreciate the inspiration from the wave of various recent open-source long sequence models,
|
152 |
-
including [XGen](https://blog.salesforceairesearch.com/xgen/), [MPT](https://www.mosaicml.com/blog/long-context-mpt-7b-8k), and
|
153 |
-
[Llama-2](https://ai.meta.com/llama/) and so on. We look forward to witnessing the continued growth and success of open-source long sequence models.
|
154 |
-
|
155 |
-
We highly appreciate the hard work and dedication of these researchers and organizations towards the advancement of the open-source community. Their contributions were invaluable in the development of SN-13B-8k-Instruct, and we hope that our model can contribute to further advancements in the field.
|
156 |
-
|
157 |
-
## Cite SN-13B-8k-Instruct
|
158 |
-
```
|
159 |
-
@software{sn-13b-8k-instruct,
|
160 |
-
title = {SN-13B-8k-Instruct: training long sequence size models with SambaNova},
|
161 |
-
author = {SambaNova Systems},
|
162 |
-
url = {https://huggingface.co/sambanovasystems/SN-13B-8k-Instruct}
|
163 |
-
month = {8},
|
164 |
-
year = {2023},
|
165 |
-
version = {1.0},
|
166 |
-
}
|
167 |
-
```
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special_tokens_map.json
DELETED
@@ -1,5 +0,0 @@
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1 |
-
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2 |
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tokenizer.json
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tokenizer_config.json
DELETED
@@ -1,10 +0,0 @@
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1 |
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{
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vocab.json
DELETED
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