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README.md
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pipeline_tag: text-generation
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# leafspark/
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This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-4194k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew.
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CLI:
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```bash
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llama-cli --hf-repo leafspark/
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```
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Server:
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```bash
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llama-server --hf-repo leafspark/
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```
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
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pipeline_tag: text-generation
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# leafspark/llama-3-8b-instruct-gradient-4194k.Q8_0-GGUF
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This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-4194k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-4194k) for more details on the model.
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| Filename | Quant | Size | Description |
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| -------- | ---------- | --------- | ----------- |
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| [llama-3-8b-instruct-gradient-4194k.Q8_0.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality |
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| [llama-3-8b-instruct-gradient-4194k.Q6_K.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q6_K.gguf) | Q6_K | 6.60GB | Very high quality, near perfect, *recommended*. |
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| [llama-3-8b-instruct-gradient-4194k.Q5_K_M.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality |
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| [llama-3-8b-instruct-gradient-4194k.Q5_K_S.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q5_K_S.gguf) | Q5_K_S | 5.60GB | Even higher quality |
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| [llama-3-8b-instruct-gradient-4194k.Q4_K_M.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q4_K_M.gguf) | Q4_K_M | 4.92GB | Recommended, medium-high quality |
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| [llama-3-8b-instruct-gradient-4194k.Q4_K_S.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q4_K_S.gguf) | Q4_K_S | N/A | Recommended, medium quality |
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| [llama-3-8b-instruct-gradient-4194k.Q3_K_L.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q3_K_L.gguf) | Q3_K_L | 4.32GB | Usable |
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| [llama-3-8b-instruct-gradient-4194k.Q3_K_M.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q3_K_M.gguf) | Q3_K_M | 4.02GB | Bad quality, use Q4 |
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| [llama-3-8b-instruct-gradient-4194k.Q3_K_S.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q3_K_S.gguf) | Q3_K_S | 3.66GB | Not recommended |
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| [llama-3-8b-instruct-gradient-4194k.Q2_K.gguf](https://huggingface.co/leafspark/Llama-3-8B-Instruct-Gradient-4194k-GGUF/blob/main/llama-3-8b-instruct-gradient-4194k.Q2_K.gguf) | Q2_K | 2.95GB | Very low quality, would not use on 8b models |
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## Use with llama.cpp
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Install llama.cpp through brew.
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CLI:
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```bash
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llama-cli --hf-repo leafspark/llama-3-8b-instruct-gradient-4194k.Q8_0-GGUF --model llama-3-8b-instruct-gradient-4194k.Q8_0.gguf -p "The meaning to life and the universe is"
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```
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Server:
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```bash
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llama-server --hf-repo leafspark/llama-3-8b-instruct-gradient-4194k.Q8_0-GGUF --model llama-3-8b-instruct-gradient-4194k.Q8_0.gguf -c 2048
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```
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
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