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Update issue templates
2023-03-21 17:47:27 +00:00
---
name: Custom issue template
about: Used to report user-related issues with the software
title: "[User] I encountered a problem .."
labels: ''
assignees: ''
---
# Prerequisites
Please answer the following questions for yourself before submitting an issue.
- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
# Expected Behavior
Please provide a detailed written description of what you were trying to do, and what you expected `lamma.cpp` to do.
# Current Behavior
Please provide a detailed written description of what `lamma.cpp` did, instead.
# Environment and Context
Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.
* Physical (or virtual) hardware you are using, e.g. for Linux:
`$ lscpu`
* Operating System, e.g. for Linux:
`$ uname -a`
* SDK version, e.g. for Linux:
```
$ python3 --version
$ make --version
$ g++ --version
```
# Models
* The LLaMA models are officially distributed by Facebook and will never be provided through this repository. See this [pull request in Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to obtain access to the model data.
* If your issue is with model conversion please verify the `sha256sum` of each of your `consolidated*.pth` and `ggml-model-XXX.bin` files to confirm that you have the correct model data files before logging an issue. [Latest sha256 sums for your reference](https://github.com/ggerganov/llama.cpp/issues/238).
* If your issue is with model generation quality then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
* LLaMA:
* [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
* [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
* GPT-3
* [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
* GPT-3.5 / InstructGPT / ChatGPT:
* [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
* [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
# Failure Information (for bugs)
Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.
# Steps to Reproduce
Please provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better.
1. step 1
2. step 2
3. step 3
4. etc.
# Failure Logs
Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.
Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability. e.g.
```
llama.cpp$ git log | head -1
commit 2af23d30434a677c6416812eea52ccc0af65119c
llama.cpp$ lscpu | egrep "AMD|Flags"
Vendor ID: AuthenticAMD
Model name: AMD Ryzen Threadripper 1950X 16-Core Processor
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev
Virtualization: AMD-V
llama.cpp$ python3 --version
Python 3.10.9
llama.cpp$ pip list | egrep "torch|numpy|sentencepiece"
numpy 1.24.2
numpydoc 1.5.0
sentencepiece 0.1.97
torch 1.13.1
torchvision 0.14.1
llama.cpp$ make --version | head -1
GNU Make 4.3
$ md5sum ./models/65B/ggml-model-q4_0.bin
dbdd682cce80e2d6e93cefc7449df487 ./models/65B/ggml-model-q4_0.bin
```
Here's a run with the Linux command [perf](https://www.brendangregg.com/perf.html)
```
llama.cpp$ perf stat ./main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p "Please close your issue when it has been answered."
main: seed = 1679149377
llama_model_load: loading model from './models/65B/ggml-model-q4_0.bin' - please wait ...
llama_model_load: n_vocab = 32000
llama_model_load: n_ctx = 512
llama_model_load: n_embd = 8192
llama_model_load: n_mult = 256
llama_model_load: n_head = 64
llama_model_load: n_layer = 80
llama_model_load: n_rot = 128
llama_model_load: f16 = 2
llama_model_load: n_ff = 22016
llama_model_load: n_parts = 8
llama_model_load: ggml ctx size = 41477.73 MB
llama_model_load: memory_size = 2560.00 MB, n_mem = 40960
llama_model_load: loading model part 1/8 from './models/65B/ggml-model-q4_0.bin'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 2/8 from './models/65B/ggml-model-q4_0.bin.1'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 3/8 from './models/65B/ggml-model-q4_0.bin.2'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 4/8 from './models/65B/ggml-model-q4_0.bin.3'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 5/8 from './models/65B/ggml-model-q4_0.bin.4'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 6/8 from './models/65B/ggml-model-q4_0.bin.5'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 7/8 from './models/65B/ggml-model-q4_0.bin.6'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 8/8 from './models/65B/ggml-model-q4_0.bin.7'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |
main: prompt: 'Please close your issue when it has been answered.'
main: number of tokens in prompt = 11
1 -> ''
12148 -> 'Please'
3802 -> ' close'
596 -> ' your'
2228 -> ' issue'
746 -> ' when'
372 -> ' it'
756 -> ' has'
1063 -> ' been'
7699 -> ' answered'
29889 -> '.'
sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000
Please close your issue when it has been answered.
@duncan-donut: I'm trying to figure out what kind of "support" you need for this script and why, exactly? Is there a question about how the code works that hasn't already been addressed in one or more comments below this ticket, or are we talking something else entirely like some sorta bugfixing job because your server setup is different from mine??
I can understand if your site needs to be running smoothly and you need help with a fix of sorts but there should really be nothing wrong here that the code itself could not handle. And given that I'm getting reports about how it works perfectly well on some other servers, what exactly are we talking? A detailed report will do wonders in helping us get this resolved for ya quickly so please take your time and describe the issue(s) you see as clearly & concisely as possible!!
@duncan-donut: I'm not sure if you have access to cPanel but you could try these instructions. It is worth a shot! Let me know how it goes (or what error message, exactly!) when/if ya give that code a go? [end of text]
main: mem per token = 71159620 bytes
main: load time = 19309.95 ms
main: sample time = 168.62 ms
main: predict time = 223895.61 ms / 888.47 ms per token
main: total time = 246406.42 ms
Performance counter stats for './main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p Please close your issue when it has been answered.':
3636882.89 msec task-clock # 14.677 CPUs utilized
13509 context-switches # 3.714 /sec
2436 cpu-migrations # 0.670 /sec
10476679 page-faults # 2.881 K/sec
13133115082869 cycles # 3.611 GHz (16.77%)
29314462753 stalled-cycles-frontend # 0.22% frontend cycles idle (16.76%)
10294402631459 stalled-cycles-backend # 78.39% backend cycles idle (16.74%)
23479217109614 instructions # 1.79 insn per cycle
# 0.44 stalled cycles per insn (16.76%)
2353072268027 branches # 647.002 M/sec (16.77%)
1998682780 branch-misses # 0.08% of all branches (16.76%)
247.802177522 seconds time elapsed
3618.573072000 seconds user
18.491698000 seconds sys
```
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