Illegal instruction (core dumped)

[digitalw00t]

.env

Generic

TEXT_EMBEDDINGS_MODEL=sentence-transformers/all-MiniLM-L6-v2
TEXT_EMBEDDINGS_MODEL_TYPE=HF # LlamaCpp or HF
USE_MLOCK=false

Ingestion

PERSIST_DIRECTORY=db
DOCUMENTS_DIRECTORY=source_documents
INGEST_CHUNK_SIZE=500
INGEST_CHUNK_OVERLAP=50
INGEST_N_THREADS=3

Generation

MODEL_TYPE=LlamaCpp # GPT4All or LlamaCpp
MODEL_PATH=eachadea/ggml-vicuna-7b-1.1/ggml-vic7b-q5_1.bin
MODEL_TEMP=0.8
MODEL_N_CTX=1024 # Max total size of prompt+answer
MODEL_MAX_TOKENS=256 # Max size of answer
MODEL_STOP=[STOP]
CHAIN_TYPE=betterstuff
N_RETRIEVE_DOCUMENTS=100 # How many documents to retrieve from the db
N_FORWARD_DOCUMENTS=100 # How many documents to forward to the LLM, chosen among those retrieved
N_GPU_LAYERS=4

Python version

Python 3.11.3

System

Ubuntu 22.04

CASALIOY version

ee9a4e5

Information

• The official example scripts
• My own modified scripts

Related Components

• Document ingestion
• GUI
• Prompt answering

Reproduction

docker run -it -p 8501:8501 -v /home/draeician/docker_files/models:/srv/CASALIOY/models --shm-size=16gb su77ungr/casalioy:stable /bin/bash

Literally I just mapped the volume in so I wouldn't have to download the models again.

(casalioy-py3.11) root@75d53b1f3f77:/srv/CASALIOY# python casalioy/startLLM.py
found local model dir at models/sentence-transformers/all-MiniLM-L6-v2
found local model file at models/eachadea/ggml-vicuna-7b-1.1/ggml-vic7b-q5_1.bin
llama.cpp: loading model from models/eachadea/ggml-vicuna-7b-1.1/ggml-vic7b-q5_1.bin
Fatal Python error: Illegal instruction

Current thread 0x00007f0012660740 (most recent call first):
File "/srv/CASALIOY/.venv/lib/python3.11/site-packages/llama_cpp/llama_cpp.py", line 183 in llama_init_from_file
File "/srv/CASALIOY/.venv/lib/python3.11/site-packages/llama_cpp/llama.py", line 157 in init
File "/srv/CASALIOY/.venv/lib/python3.11/site-packages/langchain/llms/llamacpp.py", line 133 in validate_environment
File "/srv/CASALIOY/casalioy/startLLM.py", line 57 in init
File "/srv/CASALIOY/casalioy/startLLM.py", line 123 in main
File "/srv/CASALIOY/casalioy/startLLM.py", line 135 in

Extension modules: grpc._cython.cygrpc, numpy.core._multiarray_umath, numpy.core._multiarray_tests, numpy.linalg._umath_linalg, numpy.fft._pocketfft_internal, numpy.random._common, numpy.random.bit_generator, numpy.random._bounded_integers, numpy.random._mt19937, numpy.random.mtrand, numpy.random._philox, numpy.random._pcg64, numpy.random._sfc64, numpy.random._generator, pydantic.typing, pydantic.errors, pydantic.version, pydantic.utils, pydantic.class_validators, pydantic.config, pydantic.color, pydantic.datetime_parse, pydantic.validators, pydantic.networks, pydantic.types, pydantic.json, pydantic.error_wrappers, pydantic.fields, pydantic.parse, pydantic.schema, pydantic.main, pydantic.dataclasses, pydantic.annotated_types, pydantic.decorator, pydantic.env_settings, pydantic.tools, pydantic, yaml._yaml, multidict._multidict, yarl._quoting_c, aiohttp._helpers, aiohttp._http_writer, aiohttp._http_parser, aiohttp._websocket, frozenlist._frozenlist, tornado.speedups, sqlalchemy.cyextension.collections, sqlalchemy.cyextension.immutabledict, sqlalchemy.cyextension.processors, sqlalchemy.cyextension.resultproxy, sqlalchemy.cyextension.util, greenlet._greenlet, numexpr.interpreter, torch._C, torch._C._fft, torch._C._linalg, torch._C._nested, torch._C._nn, torch._C._sparse, torch._C._special, scipy._lib._ccallback_c, numpy.linalg.lapack_lite, scipy.sparse._sparsetools, _csparsetools, scipy.sparse._csparsetools, scipy.sparse.linalg._isolve._iterative, scipy.linalg._fblas, scipy.linalg._flapack, scipy.linalg._cythonized_array_utils, scipy.linalg._flinalg, scipy.linalg._solve_toeplitz, scipy.linalg._matfuncs_sqrtm_triu, scipy.linalg.cython_lapack, scipy.linalg.cython_blas, scipy.linalg._matfuncs_expm, scipy.linalg._decomp_update, scipy.sparse.linalg._dsolve._superlu, scipy.sparse.linalg._eigen.arpack._arpack, scipy.sparse.csgraph._tools, scipy.sparse.csgraph._shortest_path, scipy.sparse.csgraph._traversal, scipy.sparse.csgraph._min_spanning_tree, scipy.sparse.csgraph._flow, scipy.sparse.csgraph._matching, scipy.sparse.csgraph._reordering, scipy.spatial._ckdtree, scipy._lib.messagestream, scipy.spatial._qhull, scipy.spatial._voronoi, scipy.spatial._distance_wrap, scipy.spatial._hausdorff, scipy.special._ufuncs_cxx, scipy.special._ufuncs, scipy.special._specfun, scipy.special._comb, scipy.special._ellip_harm_2, scipy.spatial.transform._rotation, scipy.ndimage._nd_image, _ni_label, scipy.ndimage._ni_label, scipy.optimize._minpack2, scipy.optimize._group_columns, scipy.optimize._trlib._trlib, scipy.optimize._lbfgsb, _moduleTNC, scipy.optimize._moduleTNC, scipy.optimize._cobyla, scipy.optimize._slsqp, scipy.optimize._minpack, scipy.optimize._lsq.givens_elimination, scipy.optimize._zeros, scipy.optimize.__nnls, scipy.optimize._highs.cython.src._highs_wrapper, scipy.optimize._highs._highs_wrapper, scipy.optimize._highs.cython.src._highs_constants, scipy.optimize._highs._highs_constants, scipy.linalg._interpolative, scipy.optimize._bglu_dense, scipy.optimize._lsap, scipy.optimize._direct, scipy.integrate._odepack, scipy.integrate._quadpack, scipy.integrate._vode, scipy.integrate._dop, scipy.integrate._lsoda, scipy.special.cython_special, scipy.stats._stats, scipy.stats.beta_ufunc, scipy.stats._boost.beta_ufunc, scipy.stats.binom_ufunc, scipy.stats._boost.binom_ufunc, scipy.stats.nbinom_ufunc, scipy.stats._boost.nbinom_ufunc, scipy.stats.hypergeom_ufunc, scipy.stats._boost.hypergeom_ufunc, scipy.stats.ncf_ufunc, scipy.stats._boost.ncf_ufunc, scipy.stats.ncx2_ufunc, scipy.stats._boost.ncx2_ufunc, scipy.stats.nct_ufunc, scipy.stats._boost.nct_ufunc, scipy.stats.skewnorm_ufunc, scipy.stats._boost.skewnorm_ufunc, scipy.stats.invgauss_ufunc, scipy.stats._boost.invgauss_ufunc, scipy.interpolate._fitpack, scipy.interpolate.dfitpack, scipy.interpolate._bspl, scipy.interpolate._ppoly, scipy.interpolate.interpnd, scipy.interpolate._rbfinterp_pythran, scipy.interpolate._rgi_cython, scipy.stats._biasedurn, scipy.stats._levy_stable.levyst, scipy.stats._stats_pythran, scipy._lib._uarray._uarray, scipy.stats._statlib, scipy.stats._mvn, scipy.stats._sobol, scipy.stats._qmc_cy, scipy.stats._rcont.rcont, regex._regex, sklearn.__check_build._check_build, sklearn.utils.murmurhash, sklearn.utils._isfinite, sklearn.utils._openmp_helpers, sklearn.utils._vector_sentinel, sklearn.feature_extraction._hashing_fast, sklearn.utils._logistic_sigmoid, sklearn.utils.sparsefuncs_fast, sklearn.preprocessing._csr_polynomial_expansion, sklearn.utils._cython_blas, sklearn.svm._libsvm, sklearn.svm._liblinear, sklearn.svm._libsvm_sparse, sklearn.utils._random, sklearn.utils._seq_dataset, sklearn.utils.arrayfuncs, sklearn.utils._typedefs, sklearn.utils._readonly_array_wrapper, sklearn.metrics._dist_metrics, sklearn.metrics.cluster._expected_mutual_info_fast, sklearn.metrics._pairwise_distances_reduction._datasets_pair, sklearn.metrics._pairwise_distances_reduction._base, sklearn.metrics._pairwise_distances_reduction._middle_term_computer, sklearn.utils._heap, sklearn.utils._sorting, sklearn.metrics._pairwise_distances_reduction._argkmin, sklearn.metrics._pairwise_distances_reduction._radius_neighbors, sklearn.metrics._pairwise_fast, sklearn.linear_model._cd_fast, sklearn._loss._loss, sklearn.utils._weight_vector, sklearn.linear_model._sgd_fast, sklearn.linear_model._sag_fast, sklearn.datasets._svmlight_format_fast, scipy.io.matlab._mio_utils, scipy.io.matlab._streams, scipy.io.matlab._mio5_utils, sentencepiece._sentencepiece, PIL._imaging (total: 201)
Illegal instruction (core dumped)

Expected behavior

Since this it the first time running this application, I was expecting a text interface to query the documents.

[hippalectryon-0]

What specs ? "core dumped" is a RAM issue

[digitalw00t]

Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 24
On-line CPU(s) list: 0-23
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU E5-2640 0 @ 2.50GHz
CPU family: 6
Model: 45
Thread(s) per core: 2
Core(s) per socket: 6
Socket(s): 2
Stepping: 7
CPU max MHz: 2500.0000
CPU min MHz: 1200.0000
BogoMIPS: 5000.45
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperf
mperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic popcnt tsc_deadline_timer aes xsave avx lahf_lm pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid xsaveopt dt
herm arat pln pts md_clear flush_l1d
Virtualization features:
Virtualization: VT-x
Caches (sum of all):
L1d: 384 KiB (12 instances)
L1i: 384 KiB (12 instances)
L2: 3 MiB (12 instances)
L3: 30 MiB (2 instances)
NUMA:
NUMA node(s): 2
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23
Vulnerabilities:
Itlb multihit: KVM: Mitigation: VMX disabled
L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Mds: Mitigation; Clear CPU buffers; SMT vulnerable
Meltdown: Mitigation; PTI
Mmio stale data: Unknown: No mitigations
Retbleed: Not affected
Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Srbds: Not affected
Tsx async abort: Not affected

             total        used        free      shared  buff/cache   available

Mem: 196Gi 107Gi 81Gi 5.0Mi 7.5Gi 87Gi
Swap: 2.0Gi 0B 2.0Gi

Product Name: PowerEdge T620

Running Linux Mint 20 in the docker container.

[su77ungr]

Did you try enabling mlock?

Does the source install work for you? Hopefully we get you running again soon. Thanks for your patience.

[digitalw00t]

Well the T620 isn't goping to work I don't think as it doesn't support the avx2 instruction set. I had the same issue with privateGPT, and debugging the two projects is pointing to this issue. I've got a bunch of older server, lots of power, but older and it's starting to show it's age. My RL issues should be resolved within a month I'm hoping, then I can pull a second R930 out of mothballs, and try to get a proper GPU installed so I can have a linux machine to test on.
I've been doing things on my windows gaming rig, but WSL seems to lock up during the install process for most software related to ML. Why? Don't know I have to reboot to clear it up and it really doesn't leave much in the way of evidence.