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- llm-rs
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pipeline_tag: text-generation
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---
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#
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### Training Data
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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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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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Data Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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- ggml
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pipeline_tag: text-generation
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license: apache-2.0
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language:
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- en
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# GGML converted versions of [EleutherAI](https://huggingface.co/EleutherAI)'s Pythia models
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## Description:
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The *Pythia Scaling Suite* is a collection of models developed to facilitate
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interpretability research. It contains two sets of eight models of sizes
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70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two
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models: one trained on the Pile, and one trained on the Pile after the dataset
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has been globally deduplicated. All 8 model sizes are trained on the exact
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same data, in the exact same order. We also provide 154 intermediate
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checkpoints per model, hosted on Hugging Face as branches.
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The Pythia model suite was deliberately designed to promote scientific
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research on large language models, especially interpretability research.
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Despite not centering downstream performance as a design goal, we find the
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models match or exceed the performance of
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similar and same-sized models, such as those in the OPT and GPT-Neo suites.
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## Converted Models:
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| Name | Based on | Type | Container |
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| [pythia-70m-f16.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-70m-f16.bin) | [Pythia-70M](https://huggingface.co/EleutherAI/pythia-70m) | fp16 | GGML |
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| [pythia-70m-q4_0-ggjt.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-70m-q4_0-ggjt.bin) | [Pythia-70M](https://huggingface.co/EleutherAI/pythia-70m) | int4 | GGJT |
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| [pythia-70m-q4_0.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-70m-q4_0.bin) | [Pythia-70M](https://huggingface.co/EleutherAI/pythia-70m) | int4 | GGML |
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| [pythia-160m-f16.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-160m-f16.bin) | [Pythia-160M](https://huggingface.co/EleutherAI/pythia-160m) | fp16 | GGML |
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| [pythia-160m-q4_0-ggjt.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-160m-q4_0-ggjt.bin) | [Pythia-160M](https://huggingface.co/EleutherAI/pythia-160m) | int4 | GGJT |
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| [pythia-160m-q4_0.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-160m-q4_0.bin) | [Pythia-160M](https://huggingface.co/EleutherAI/pythia-160m) | int4 | GGML |
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| [pythia-410m-f16.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-410m-f16.bin) | [Pythia-410M](https://huggingface.co/EleutherAI/pythia-410m) | fp16 | GGML |
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| [pythia-410m-q4_0-ggjt.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-410m-q4_0-ggjt.bin) | [Pythia-410M](https://huggingface.co/EleutherAI/pythia-410m) | int4 | GGJT |
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| [pythia-410m-q4_0.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-410m-q4_0.bin) | [Pythia-410M](https://huggingface.co/EleutherAI/pythia-410m) | int4 | GGML |
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| [pythia-1b-f16.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-1b-f16.bin) | [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) | fp16 | GGML |
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| [pythia-1b-q4_0-ggjt.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-1b-q4_0-ggjt.bin) | [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) | int4 | GGJT |
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| [pythia-1b-q4_0.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-1b-q4_0.bin) | [Pythia-1B](https://huggingface.co/EleutherAI/pythia-1b) | int4 | GGML |
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| [pythia-1.4b-f16.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-1.4b-f16.bin) | [Pythia-1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) | fp16 | GGML |
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| [pythia-1.4b-q4_0-ggjt.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-1.4b-q4_0-ggjt.bin) | [Pythia-1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) | int4 | GGJT |
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| [pythia-1.4b-q4_0.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-1.4b-q4_0.bin) | [Pythia-1.4B](https://huggingface.co/EleutherAI/pythia-1.4b) | int4 | GGML |
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| [pythia-2.8b-f16.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-2.8b-f16.bin) | [Pythia-2.8B](https://huggingface.co/EleutherAI/pythia-2.8b) | fp16 | GGML |
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| [pythia-2.8b-q4_0-ggjt.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-2.8b-q4_0-ggjt.bin) | [Pythia-2.8B](https://huggingface.co/EleutherAI/pythia-2.8b) | int4 | GGJT |
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| [pythia-2.8b-q4_0.bin](https://huggingface.co/Rustformers/pythia-ggml/blob/main/pythia-2.8b-q4_0.bin) | [Pythia-2.8B](https://huggingface.co/EleutherAI/pythia-2.8b) | int4 | GGML |
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## Usage
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### Python via [llm-rs](https://github.com/LLukas22/llm-rs-python):
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#### Installation
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Via pip: `pip install llm-rs`
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#### Run inference
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```python
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from llm_rs import AutoModel
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#Load the model, define any model you like from the list above as the `model_file`
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model = AutoModel.from_pretrained("Rustformers/pythia-ggml",model_file="pythia-70m-q4_0-ggjt.bin")
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#Generate
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print(model.generate("The meaning of life is"))
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```
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### Rust via [Rustformers/llm](https://github.com/rustformers/llm):
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#### Installation
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```
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git clone --recurse-submodules [email protected]:rustformers/llm.git
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cargo build --release
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```
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#### Run inference
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```
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cargo run --release -- gptneox infer -m path/to/model.bin -p "Tell me how cool the Rust programming language is:"
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```
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