Text Generation
Transformers
English
llama
code
llama2
Inference Endpoints
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  ![image of llama engineer](https://i.imgur.com/JlhW0ri.png)
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- ## Llama-Engineer-Evol-7B
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  This is a version of Meta's chat instruction-tuned Llama 2 further fine-tuned on over 80,000 coding samples.
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- The dataset is a combination of [Evol-Instruct-Code-80k-v1](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) from [nikrosh](https://huggingface.co/nickrosh), a replication of the Evol-Instruct-Code as described in the WizardCoder Paper, and [Teknium](https://huggingface.co/teknium)'s [GPTeacher](https://github.com/teknium1/GPTeacher/blob/main/Codegen/codegen-instruct.json). Special thanks to these folks for putting these datasets together.
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- ### Prompt Format
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- The reccomended model usage is a variant of the standard Llama 2 format:
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  ```
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  [INST] <<SYS>>
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- You are a helpful programming assistant. Always answer as helpfully as possible. Be direct in your response and get to the answer right away. Responses should be short.
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  <</SYS>>
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  {your prompt}[/INST]
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  ```
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- ### Next Steps
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  - Prune the dataset and possibly fine-tune for longer.
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  - Run benchmarks.
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  - Provide GGML and GPTQ.
 
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  ![image of llama engineer](https://i.imgur.com/JlhW0ri.png)
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+ # Llama-Engineer-Evol-7B
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  This is a version of Meta's chat instruction-tuned Llama 2 further fine-tuned on over 80,000 coding samples.
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+ The dataset is a combination of [Evol-Instruct-Code-80k-v1](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) from [nikrosh](https://huggingface.co/nickrosh), a replication of the Evol-Instruct-Code as described in the [WizardCoder](https://arxiv.org/pdf/2306.08568.pdf) paper, and [Teknium](https://huggingface.co/teknium)'s [GPTeacher](https://github.com/teknium1/GPTeacher/blob/main/Codegen/codegen-instruct.json). Special thanks to these folks for putting these datasets together.
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+ ## Prompt Format
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+ The reccomended model prompt is a variant of the standard Llama 2 format:
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  ```
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  [INST] <<SYS>>
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+ You are a programming assistant. Always answer as helpfully as possible. Be direct in your response and get to the answer right away. Responses should be short.
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  <</SYS>>
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  {your prompt}[/INST]
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  ```
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+ ## Next Steps
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  - Prune the dataset and possibly fine-tune for longer.
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  - Run benchmarks.
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  - Provide GGML and GPTQ.