llama-cpp-conv

llama.cpp (GGUF) conversion for pypi

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Project description

This is a fork of llama-cpp-python for the sole purpose of making gguf conversion binaries installable from pip.

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0.2.75

May 20, 2024

0.2.68

Apr 30, 2024

0.2.68a1 pre-release

Apr 30, 2024

This version

0.2.58

Apr 1, 2024

0.2.57

Mar 19, 2024

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Uploaded Apr 1, 2024 PyPy Windows x86-64

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Uploaded Apr 1, 2024 PyPy manylinux: glibc 2.17+ x86-64

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Uploaded Apr 1, 2024 PyPy manylinux: glibc 2.17+ i686

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Uploaded Apr 1, 2024 PyPy manylinux: glibc 2.17+ x86-64

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Uploaded Apr 1, 2024 PyPy manylinux: glibc 2.17+ i686

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Uploaded Apr 1, 2024 CPython 3.11 Windows x86

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Uploaded Apr 1, 2024 CPython 3.11 musllinux: musl 1.1+ x86-64

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Uploaded Apr 1, 2024 CPython 3.11 musllinux: musl 1.1+ i686

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Uploaded Apr 1, 2024 CPython 3.11 manylinux: glibc 2.17+ x86-64

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Uploaded Apr 1, 2024 CPython 3.11 manylinux: glibc 2.17+ i686

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Uploaded Apr 1, 2024 CPython 3.10 Windows x86

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Uploaded Apr 1, 2024 CPython 3.10 musllinux: musl 1.1+ x86-64

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Uploaded Apr 1, 2024 CPython 3.10 musllinux: musl 1.1+ i686

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Uploaded Apr 1, 2024 CPython 3.10 manylinux: glibc 2.17+ x86-64

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Uploaded Apr 1, 2024 CPython 3.10 manylinux: glibc 2.17+ i686

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Uploaded Apr 1, 2024 CPython 3.9 Windows x86-64

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Uploaded Apr 1, 2024 CPython 3.9 Windows x86

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Uploaded Apr 1, 2024 CPython 3.9 musllinux: musl 1.1+ x86-64

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Uploaded Apr 1, 2024 CPython 3.9 musllinux: musl 1.1+ i686

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Uploaded Apr 1, 2024 CPython 3.9 manylinux: glibc 2.17+ x86-64

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Uploaded Apr 1, 2024 CPython 3.9 manylinux: glibc 2.17+ i686

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Uploaded Apr 1, 2024 CPython 3.8 Windows x86-64

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Uploaded Apr 1, 2024 CPython 3.8 Windows x86

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Uploaded Apr 1, 2024 CPython 3.8 musllinux: musl 1.1+ x86-64

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Uploaded Apr 1, 2024 CPython 3.8 musllinux: musl 1.1+ i686

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Uploaded Apr 1, 2024 CPython 3.8 manylinux: glibc 2.17+ x86-64

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Uploaded Apr 1, 2024 CPython 3.8 manylinux: glibc 2.17+ i686

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Hashes for llama_cpp_conv-0.2.58.tar.gz
Algorithm	Hash digest
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MD5	`a1d2148cb94986aa9741382511ccba5a`
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Hashes for llama_cpp_conv-0.2.58-pp39-pypy39_pp73-win_amd64.whl

Hashes for llama_cpp_conv-0.2.58-pp39-pypy39_pp73-win_amd64.whl
Algorithm	Hash digest
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Hashes for llama_cpp_conv-0.2.58-pp39-pypy39_pp73-manylinux_2_17_x86_64.whl

Hashes for llama_cpp_conv-0.2.58-pp39-pypy39_pp73-manylinux_2_17_x86_64.whl
Algorithm	Hash digest
SHA256	`101edf49fb71dc1e96b4f534a8d0be3488f55db996994c784709e481429756cb`
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Hashes for llama_cpp_conv-0.2.58-pp39-pypy39_pp73-manylinux_2_17_i686.whl

Hashes for llama_cpp_conv-0.2.58-pp39-pypy39_pp73-manylinux_2_17_i686.whl
Algorithm	Hash digest
SHA256	`55384044c6dd4a31c5a5ca444ff0ccc234851dc112a4458ac114c679282fb5a7`
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Hashes for llama_cpp_conv-0.2.58-pp38-pypy38_pp73-win_amd64.whl

Hashes for llama_cpp_conv-0.2.58-pp38-pypy38_pp73-win_amd64.whl
Algorithm	Hash digest
SHA256	`f50f147de22efd4cc50f9597efd3ed7222428e058558a07458a2cd53cf7d9ebb`
MD5	`feaa64b2b755e2a54e4df1f8741377fb`
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Hashes for llama_cpp_conv-0.2.58-pp38-pypy38_pp73-manylinux_2_17_x86_64.whl

Hashes for llama_cpp_conv-0.2.58-pp38-pypy38_pp73-manylinux_2_17_x86_64.whl
Algorithm	Hash digest
SHA256	`a4eb9a5e4343785c5a51407cc6d6060b1a44ee7f65269b183bfe9e697a90a5d4`
MD5	`df21d1ed38fd621231fad2c50e9ebf20`
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Hashes for llama_cpp_conv-0.2.58-pp38-pypy38_pp73-manylinux_2_17_i686.whl

Hashes for llama_cpp_conv-0.2.58-pp38-pypy38_pp73-manylinux_2_17_i686.whl
Algorithm	Hash digest
SHA256	`e4d8ee17cbc613ab738bdbce38a5d87c3508749bc38d7eadc6df08f4eced41b5`
MD5	`c35a6273209fc598b83711b86a01e06e`
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Hashes for llama_cpp_conv-0.2.58-cp311-cp311-win_amd64.whl

Hashes for llama_cpp_conv-0.2.58-cp311-cp311-win_amd64.whl
Algorithm	Hash digest
SHA256	`5c6a22fcb6eba261d9e9e1cfa978ba4a407db4819a1418ad6272e70ffe22ea4e`
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Hashes for llama_cpp_conv-0.2.58-cp311-cp311-win32.whl

Hashes for llama_cpp_conv-0.2.58-cp311-cp311-win32.whl
Algorithm	Hash digest
SHA256	`d576ec5dea3f8e2144ed1d4b7a353708190917a387139d179fac82d211a313ec`
MD5	`1687ded3289ab40ffed1875d84ad2437`
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Hashes for llama_cpp_conv-0.2.58-cp311-cp311-musllinux_1_1_x86_64.whl

Hashes for llama_cpp_conv-0.2.58-cp311-cp311-musllinux_1_1_x86_64.whl
Algorithm	Hash digest
SHA256	`f551f45fd5212536c0e652a3a372fbb757cb880306df3eb81db1dfad88f4423b`
MD5	`1ed25bb9e0a4fb42e755ad0c4d1639b7`
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Hashes for llama_cpp_conv-0.2.58-cp311-cp311-musllinux_1_1_i686.whl

Hashes for llama_cpp_conv-0.2.58-cp311-cp311-musllinux_1_1_i686.whl
Algorithm	Hash digest
SHA256	`b83f158413d8a4e80c893133fca61d6d88e697ecf0635e193a263eda5d9afe0f`
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Hashes for llama_cpp_conv-0.2.58-cp311-cp311-manylinux_2_17_x86_64.whl

Hashes for llama_cpp_conv-0.2.58-cp311-cp311-manylinux_2_17_x86_64.whl
Algorithm	Hash digest
SHA256	`4f5d85fe1d5212f096bc21c0bd57f48c74ce38c8d7262fb678a630185b3702b6`
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Hashes for llama_cpp_conv-0.2.58-cp311-cp311-manylinux_2_17_i686.whl

Hashes for llama_cpp_conv-0.2.58-cp311-cp311-manylinux_2_17_i686.whl
Algorithm	Hash digest
SHA256	`bfa5162d43d52567f8e51fcf674713debd23ce13e172bedafd42709130bc7aa4`
MD5	`a2cc9769671969ca89985d1874f7cf63`
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Hashes for llama_cpp_conv-0.2.58-cp310-cp310-win_amd64.whl

Hashes for llama_cpp_conv-0.2.58-cp310-cp310-win_amd64.whl
Algorithm	Hash digest
SHA256	`48d06e5c6f31d7ca40e0d0a15651d4204a745796ef8a4badf8b74b02a3a27c04`
MD5	`49f711dfef1c0a49e1a1b32ad16801e6`
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Hashes for llama_cpp_conv-0.2.58-cp310-cp310-win32.whl

Hashes for llama_cpp_conv-0.2.58-cp310-cp310-win32.whl
Algorithm	Hash digest
SHA256	`422edf811a72af3a98549d15456b875ba87652d1254f8e2f573f8d642fa5ef5c`
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Hashes for llama_cpp_conv-0.2.58-cp310-cp310-musllinux_1_1_x86_64.whl

Hashes for llama_cpp_conv-0.2.58-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm	Hash digest
SHA256	`3c4f144397c436aafc8494111e00458529c02e7ec8fbacdd62372aae5c43c9a9`
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Hashes for llama_cpp_conv-0.2.58-cp310-cp310-musllinux_1_1_i686.whl
Algorithm	Hash digest
SHA256	`d1477f73c4fdfacc68c4ad8b4758c18d14cfcfed4983dd3996a4dfecfca06425`
MD5	`20011b2dede7c2a71c556e2082e258c9`
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Hashes for llama_cpp_conv-0.2.58-cp310-cp310-manylinux_2_17_x86_64.whl
Algorithm	Hash digest
SHA256	`8a616678b573b7b535aaef52a0c12ca7645e644138d22a9787524fba006fb28c`
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Hashes for llama_cpp_conv-0.2.58-cp310-cp310-manylinux_2_17_i686.whl
Algorithm	Hash digest
SHA256	`c83de6e0be3a82a07748aec9aa1c3d6429a6fe3ab6578b4839cf3645cd7d8ee9`
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Hashes for llama_cpp_conv-0.2.58-cp39-cp39-win_amd64.whl

Hashes for llama_cpp_conv-0.2.58-cp39-cp39-win_amd64.whl
Algorithm	Hash digest
SHA256	`a2ac74e6bb62c0b0d334dee4229a341d5269e4f53701ccb82e64cfda27af3ecc`
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Hashes for llama_cpp_conv-0.2.58-cp39-cp39-win32.whl

Hashes for llama_cpp_conv-0.2.58-cp39-cp39-win32.whl
Algorithm	Hash digest
SHA256	`a9ac128802194f6feef9145a4462fb4d8cfa9d4ee4bbb0b0ad484568c6b92695`
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Hashes for llama_cpp_conv-0.2.58-cp39-cp39-musllinux_1_1_x86_64.whl

Hashes for llama_cpp_conv-0.2.58-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm	Hash digest
SHA256	`536d5f638977f2d3161ab59766738be2ce4a2cdac3344686f798c9c78f616ebc`
MD5	`d06a84a3be679ba5149eb939f32d3c0e`
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Hashes for llama_cpp_conv-0.2.58-cp39-cp39-musllinux_1_1_i686.whl

Hashes for llama_cpp_conv-0.2.58-cp39-cp39-musllinux_1_1_i686.whl
Algorithm	Hash digest
SHA256	`00806441b4441f3ad2bf77af080f21119ad7555b489237cc6f9014dedfe4407a`
MD5	`6806d770b4b3cfc770048a797d793de1`
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Hashes for llama_cpp_conv-0.2.58-cp39-cp39-manylinux_2_17_x86_64.whl

Hashes for llama_cpp_conv-0.2.58-cp39-cp39-manylinux_2_17_x86_64.whl
Algorithm	Hash digest
SHA256	`4c6fc379d87978800e3d321f1a5668d75ef1613e226425123617015c04c2201d`
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Hashes for llama_cpp_conv-0.2.58-cp39-cp39-manylinux_2_17_i686.whl

Hashes for llama_cpp_conv-0.2.58-cp39-cp39-manylinux_2_17_i686.whl
Algorithm	Hash digest
SHA256	`0a0840fc298cc07a909de352e28d59ee47ebb26e8550caabe346f7bd864991d1`
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Hashes for llama_cpp_conv-0.2.58-cp38-cp38-win_amd64.whl

Hashes for llama_cpp_conv-0.2.58-cp38-cp38-win_amd64.whl
Algorithm	Hash digest
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Hashes for llama_cpp_conv-0.2.58-cp38-cp38-win32.whl

Hashes for llama_cpp_conv-0.2.58-cp38-cp38-win32.whl
Algorithm	Hash digest
SHA256	`8bfc13d86545d4ef32e9b6206c49378314aeebfd5a545cc4da7f35ff72a393af`
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Hashes for llama_cpp_conv-0.2.58-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm	Hash digest
SHA256	`7438e55cea2781cc98809d0443b330df195b54736fe00adc46149e8d9e269188`
MD5	`2e9c52c75ca838e0229904f545968537`
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Hashes for llama_cpp_conv-0.2.58-cp38-cp38-musllinux_1_1_i686.whl

Hashes for llama_cpp_conv-0.2.58-cp38-cp38-musllinux_1_1_i686.whl
Algorithm	Hash digest
SHA256	`7026d0448706044257e96134ec3b405b4eb0bddf55692cc1abee792370bcf11b`
MD5	`70f845d619271efe6b33843e5707cda2`
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Hashes for llama_cpp_conv-0.2.58-cp38-cp38-manylinux_2_17_x86_64.whl
Algorithm	Hash digest
SHA256	`b50bc474e2452761272626f7e71fd345e42ebcfaa90d11646d743f2b184be495`
MD5	`1198587ac1907fd89f2f774df9e30d5d`
BLAKE2b-256	`a5d8d4925410137f175c6f5bcf82ad10464f1182a25d1cc481c2f52f9473837f`

Hashes for llama_cpp_conv-0.2.58-cp38-cp38-manylinux_2_17_i686.whl

Hashes for llama_cpp_conv-0.2.58-cp38-cp38-manylinux_2_17_i686.whl
Algorithm	Hash digest
SHA256	`b1bfc3232a162cf9d0d47275fce63c18ed18c8d7aeda272d48d37beaa9342b4c`
MD5	`2f4af4a8eda1de107eb529f49ca6b3ff`
BLAKE2b-256	`f04794dddc0c0c681b64d5cbc1899ab421a6985f6c3e374ed8fea9309e2115a0`