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  - llm-rs
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  - ggml
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  pipeline_tag: text-generation
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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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:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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-
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- ## Uses
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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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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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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 (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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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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- [More Information Needed]
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- ## Training Details
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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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- #### 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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- #### Hardware
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- #### Software
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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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- **APA:**
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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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- ## Model Card Authors [optional]
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- ## Model Card Contact
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-
 
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  - llm-rs
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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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  ---
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+ # GGML converted versions of [EleutherAI](https://huggingface.co/EleutherAI)'s Pythia models
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+
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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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+
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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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+
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+ ## Converted Models:
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+
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+ | Name | Based on | Type | Container |
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+ |-|-|-|-|
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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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+
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+ ### Python via [llm-rs](https://github.com/LLukas22/llm-rs-python):
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+
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+ #### Installation
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+ Via pip: `pip install llm-rs`
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+
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+ #### Run inference
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+ ```python
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+ from llm_rs import AutoModel
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+
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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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+
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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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+
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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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+ ```