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--- |
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language: |
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- code |
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license: llama2 |
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tags: |
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- llama-2 |
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model_name: CodeLlama 13B Instruct |
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base_model: codellama/CodeLlama-13b-Instruct-hf |
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inference: false |
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model_creator: Meta |
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model_type: llama |
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pipeline_tag: text-generation |
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quantized_by: Second State Inc. |
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--- |
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<div style="width: auto; margin-left: auto; margin-right: auto"> |
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<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> |
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</div> |
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> |
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<!-- header end --> |
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# CodeLlama-13B-Instruct |
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## Original Model |
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[codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) |
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## Run with LlamaEdge |
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- LlamaEdge version: [v0.2.8](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.2.8) and above |
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- Prompt template |
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- Prompt type: `codellama-instruct` |
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- Prompt string |
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```text |
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<s>[INST] <<SYS>> |
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Write code to solve the following coding problem that obeys the constraints and passes the example test cases. Please wrap your code answer using ```: <</SYS>> |
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{prompt} [/INST] |
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``` |
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- Context size: `5120` |
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- Run as LlamaEdge command app |
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```bash |
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wasmedge --dir .:. --nn-preload default:GGML:AUTO:CodeLlama-13b-Instruct-hf-Q5_K_M.gguf llama-chat.wasm -p codellama-instruct |
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``` |
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## Quantized GGUF Models |
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| Name | Quant method | Bits | Size | Use case | |
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| ---- | ---- | ---- | ---- | ----- | |
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| [CodeLlama-13b-Instruct-hf-Q2_K.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q2_K.gguf) | Q2_K | 2 | 5.43 GB| smallest, significant quality loss - not recommended for most purposes | |
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| [CodeLlama-13b-Instruct-hf-Q3_K_L.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| small, substantial quality loss | |
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| [CodeLlama-13b-Instruct-hf-Q3_K_M.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| very small, high quality loss | |
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| [CodeLlama-13b-Instruct-hf-Q3_K_S.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| very small, high quality loss | |
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| [CodeLlama-13b-Instruct-hf-Q4_0.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| legacy; small, very high quality loss - prefer using Q3_K_M | |
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| [CodeLlama-13b-Instruct-hf-Q4_K_M.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| medium, balanced quality - recommended | |
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| [CodeLlama-13b-Instruct-hf-Q4_K_S.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| small, greater quality loss | |
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| [CodeLlama-13b-Instruct-hf-Q5_0.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| legacy; medium, balanced quality - prefer using Q4_K_M | |
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| [CodeLlama-13b-Instruct-hf-Q5_K_M.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| large, very low quality loss - recommended | |
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| [CodeLlama-13b-Instruct-hf-Q5_K_S.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| large, low quality loss - recommended | |
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| [CodeLlama-13b-Instruct-hf-Q6_K.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q6_K.gguf) | Q6_K | 6 | 10.7 GB| very large, extremely low quality loss | |
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| [CodeLlama-13b-Instruct-hf-Q8_0.gguf](https://huggingface.co/second-state/CodeLlama-13B-Instruct-GGUF/blob/main/CodeLlama-13b-Instruct-hf-Q8_0.gguf) | Q8_0 | 8 | 13.8 GB| very large, extremely low quality loss - not recommended | |
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