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LLaMA-30B-ggml

localmodels / LLaMA-30B-ggml

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Model's Last Updated: July 17 2023

Introduction of LLaMA-30B-ggml

Model Details of LLaMA-30B-ggml

LLaMA 30B ggml

From Meta: https://ai.meta.com/blog/large-language-model-llama-meta-ai

Original llama.cpp quant methods: q4_0, q4_1, q5_0, q5_1, q8_0

Quantized using an older version of llama.cpp and compatible with llama.cpp from May 19, commit 2d5db48.

k-quant methods: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K

Quantization methods compatible with latest llama.cpp from June 6, commit 2d43387.

Provided files
Name Quant method Bits Size Max RAM required Use case
llama-30b.ggmlv3.q2_K.bin q2_K 2 13.60 GB 16.10 GB New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
llama-30b.ggmlv3.q3_K_L.bin q3_K_L 3 17.20 GB 19.70 GB New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
llama-30b.ggmlv3.q3_K_M.bin q3_K_M 3 15.64 GB 18.14 GB New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K
llama-30b.ggmlv3.q3_K_S.bin q3_K_S 3 13.98 GB 16.48 GB New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
llama-30b.ggmlv3.q4_0.bin q4_0 4 18.30 GB 20.80 GB Original quant method, 4-bit.
llama-30b.ggmlv3.q4_1.bin q4_1 4 20.33 GB 22.83 GB Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
llama-30b.ggmlv3.q4_K_M.bin q4_K_M 4 19.57 GB 22.07 GB New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K
llama-30b.ggmlv3.q4_K_S.bin q4_K_S 4 18.30 GB 20.80 GB New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
llama-30b.ggmlv3.q5_0.bin q5_0 5 22.37 GB 24.87 GB Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
llama-30b.ggmlv3.q5_1.bin q5_1 5 24.40 GB 26.90 GB Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
llama-30b.ggmlv3.q5_K_M.bin q5_K_M 5 23.02 GB 25.52 GB New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K
llama-30b.ggmlv3.q5_K_S.bin q5_K_S 5 22.37 GB 24.87 GB New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
llama-30b.ggmlv3.q6_K.bin q6_K 6 26.69 GB 29.19 GB New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization
llama-30b.ggmlv3.q8_0.bin q8_0 8 34.56 GB 37.06 GB Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

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LLaMA-30B-ggml huggingface.co is an AI model on huggingface.co that provides LLaMA-30B-ggml's model effect (), which can be used instantly with this localmodels LLaMA-30B-ggml model. huggingface.co supports a free trial of the LLaMA-30B-ggml model, and also provides paid use of the LLaMA-30B-ggml. Support call LLaMA-30B-ggml model through api, including Node.js, Python, http.

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https://huggingface.co/localmodels/LLaMA-30B-ggml

localmodels LLaMA-30B-ggml online free

LLaMA-30B-ggml huggingface.co is an online trial and call api platform, which integrates LLaMA-30B-ggml's modeling effects, including api services, and provides a free online trial of LLaMA-30B-ggml, you can try LLaMA-30B-ggml online for free by clicking the link below.

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LLaMA-30B-ggml install

LLaMA-30B-ggml is an open source model from GitHub that offers a free installation service, and any user can find LLaMA-30B-ggml on GitHub to install. At the same time, huggingface.co provides the effect of LLaMA-30B-ggml install, users can directly use LLaMA-30B-ggml installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

LLaMA-30B-ggml install url in huggingface.co:

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Url of LLaMA-30B-ggml

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