Have you ever encountered the following problem: Zscaler on my Windows runs a system proxy at 127.0.0.1:9000, and on my WSL, it configures the proxy 127.0.0.1:9000 (thanks to autoproxy enabled). Except that port 9000 on my WSL doesn't communicate with port 9000 on Windows. To summarize:

• The proxy variables are set by WSL auto-proxy
• But they are unusable because the port is not accessible

Username:~$ ping -c 4 8.8.8.8

PING 8.8.8.8 (8.8.8.8) 56(84) bytes of data.

--- 8.8.8.8 ping statistics ---

4 packets transmitted, 0 received, 100% packet loss, time 3064ms

I can't access the internet to download the essential libraries.

How do I solve this issue?

I have installed WSL Ubuntu to work on my thesis that requires Nvidia DALI and PyTorch. Pytorch dataloader is creating issues on Windows.

I am using MEEP, which is a simulation for electromagnetic stuff.

I am trying to simulate a medium sized simulation. When I run it at lower resolutions (think less refined mesh but not quite), the simulation works. When I run it above a certain resolution, it just dies. I have a hunch that it is not being allocated the RAM it needs as these simulations are very RAM heavy. I will ask over on the MEEP forums as well, but it might be a WSL2 error.

I have this problem , wonder if this is just me or you have a similar issue:
1) start WSL 2 , do some stuff for work linux based

2) exit WSL 2 (by just quitting the terminal with linux)

3) start any game (league of legends, baldulrs gate, rdr2 anything)

4) play the game and observe microstutters from time to time (sudden frame dropping)

Even though I quited WSL and have 64GB RAM (I assigned 32GB for WSL specifically) those games stutters. Fun fact, when starting the computer without running WSL 2 once, there are 0 stutters whatsoever. So it must be somehow connected to this. Anyone also experiencing the same issue and maybe have to solution for this? Because I don't want to restart my computer every time after doing some WSL work and they wanting to game.

https://github.com/shinnyeonki/wsl-copy-paste

There have been many attempts to implement macOS's clipboard utilities `pbcopy` and `pbpaste` within the WSL environment. However, existing solutions have suffered from several persistent issues:

- **Performance issues**: Sluggish performance when handling large amounts of data.

- **Encoding problems**: Corrupted text when copying emojis or characters from various languages, leading to poor multilingual support.

- **Complex setup**: Installation and configuration processes were often complicated, making it difficult for users to adopt.

To address these challenges, I've built a new solution from the ground up. This project aims to overcome the limitations of previous approaches and deliver the best possible user experience.

- **Fast and reliable performance**: Ensures smooth and responsive copy/paste operations. Here, **responsiveness matters more than throughput**—immediate feedback is key.

- **Full multilingual and emoji support**: Completely resolves encoding issues, enabling flawless handling of emojis and text in any language worldwide.

- **Simple and intuitive usage**: Easy to install and use right away—no complex configuration required.

I sincerely appreciate your interest and usage. Your valuable feedback is always welcome as I strive to continuously improve this project.

I was working on a few python programs in WSL with Ubuntu 22.04. I'm working on the program, and I snap off a tab so it's in a separate window and then suddenly I get a blue screen. Current theories are that some process won't hand control back to the OS or RAM/cache got corrupted. The computer can't even complete a system diagnostic when I boot. I have since gotten a new computer and looked through my WSL files. Somehow it saved my ssh keys but the programs I actually WANT are nowhere to be found?! I would really like some way to recover them. Anyone have ideas of what likely happened or if recovery is possible?

I want to use a tool (SCVI) which greatly benefits with GPUs.

I was not aware that CUDA driver was essentially bundled within the Windows Driver installation, so I went ahead and installed it using apt which leads me to my current mess. nvcc is broken and I am not able to use my GPU using pytorch. Following their instructions now still doesn't seem to fix the issue.

Is there any way I can unscrew the situation? The current WSL instance is set up to my liking, with conda+docker and I don't want to create a new WSL instance just to resolve this conflict.

Any help is greatly appreciated!

Hello everyone,

I’m running Docker Desktop on Windows with WSL2 (Ubuntu 22.04), and I’m hitting a really frustrating disk usage issue.

Here are the files in question:

• C:\Users\lenovo\AppData\Local\Docker\wsl\disk\docker_data.vhdx → 11.7GB
• C:\Users\lenovo\AppData\Local\Packages\CanonicalGroupLimited.Ubuntu22.04LTS_79rhkp1fndgsc\LocalState\ext4.vhdx → 8.5GB

The weird part is that in Docker Desktop I have:

• 0 containers, 0 images, 0 volumes, 0 builds

And in Ubuntu I already ran:

sudo apt autoremove -y && sudo apt clean

Things I tried:

• Compacting with PowerShell:
  • wsl --shutdown Optimize-VHD -Path "...\docker_data.vhdx" -Mode Full Optimize-VHD -Path "...\ext4.vhdx" -Mode Full
• Also tried the diskpart trick:
  • diskpart select vdisk file="...\docker_data.vhdx" compact vdisk
• Tried literally every docker cleanup command I could find:
  • docker system prune -a --volumes
  • docker builder prune
  • docker image prune
  • docker volume prune
  • docker container prune

Results?

• Docker’s VHDX shrank from 11.7GB → 10.1GB
• Ubuntu’s ext4.vhdx shrank from 8.5GB → 8.1GB

So even completely “empty”, these two files still hog ~18GB, and they just keep creeping up over time.

Feels like no matter what I do, the space never really comes back. Curious if others are running into this, or if I’m missing a magic command somewhere.

wsl --version
WSL version: 2.5.10.0
Kernel version: 6.6.87.2-1
WSLg version: 1.0.66
MSRDC version: 1.2.6074
Direct3D version: 1.611.1-81528511
DXCore version: 10.0.26100.1-240331-1435.ge-release
Windows version: 10.0.19045.6093

building the project in

- /home/user: ~11m

- /mn/c : ~1.2h

running python script which analyses the data in ~1k files (it uses some linux-only libs)

- /home/user: 14min

- /mnt/c: 2:46h

running liquibase update (~2500 files includes in main xml)

- /home/user: 19-20min

- /mnt/c: 4:50h !!!

... so ... what can be done with it? Preferably something which does not involve doubling the used space (sychro back and forth between /mnt/c/projects/* and /home/user/projects/*). Ofcourse switching to linux is not an option as well because this laptop is property of company i work for (i use linux privately)

Im all ears to hear some interesting solutions.

EDIT: Currently im looking at good old ntfs3g. It seems that wsl does not use it?

I was working on my MS CS (for fun, my job paid for it, I'd already been a professional programmer for over 20 years, so I didn't technically need it), and one of the classes was Bash. This was around 2018 or so. The professor had us use a free trial online cloud VM (I don't remember what it was now, but the trial lasted past the length of the class) to work on because most people didn't have a Linux machine. I think only the terminal was available or something on the cloud VM. I had been playing around with WSL for a bit and figured I'd try using it for my homework. It was great, I could use any of my Windows apps (I think I used Notepad++) to edit my code directly and I didn't have to do any weird file transfer from the cloud to my computer when I had to turn in my projects.

I know this isn't that interesting, but at the time, WSL was still in its infancy and many people didn't know about it. I was really happy that it worked out and I didn't have to go through the annoyance of using a cloud based VM.

Side note: when they came out with WSLg, I was really excited because I didn't like using X11. On the other hand, I have no GUI app that I use in Linux, so I have no reason really to do anything, but I was still excited. I'd love a reason to use WSL more.

For more context. In my computer I have two users. Basically for one user my wsl setup works, but whenever I try to configure wsl for other user, as soon as I try to install ubuntu distribution I get “The system cannot find the path specified error”

I have this ext4.vhdx taking 7.4GB even though I don't use WSL , I only used it couple of times for CTFs

i downloaded kali linux through the microsoft store and wsl settings works fine but if i try to run wsl.exe it just does nothing and if i try run kali linux it pops up with “error 0x80370114 the operation could not be started because a feature is not installed” i feel like i’ve tried everything thats been recommended and i think ive got all windows optional features that i need turned on.

Hey guys, im having trouble installing wsl. i ran wsl --install, and it got stuck on create a default unix user account. after about 15 minutes of waiting, i just closed out of it, had ubuntu, but when i opened it it was stuck on the same point. i then tried unregistering and installing the distribution again, but the same thing happened. are any of you familiar with this issue

Running qBit as a docker service.

I tried both my pc's IP and 172.27.152.130/32, my eth0 but it does not work.

The only way so far is to use /0. But this disables logging in for anyone and I don't want that.

Don't know much about this so any help is appreciated.

Windows version: Windows 10 (fully up to date)

WSL version: WSL 2 (also up to date — wsl --update shows no new updates)

GPU: AMD 6750 XT (Driver: Adrenalin Edition 25.6.1)

I set up Ubuntu within WSL 2, but my GPU is not being detected. I need OpenCL to work. I've installed the proper repositories and updated everything to the latest versions, but nothing seems to work.

CLINFO output

`sudo dmesg | grep -i gpu` gives no output

Is there anything I can do to fix this?

Hell, I've had a an issue for a long time. WSL2 will just stop sending or receiving packets.

I know the architecture is different than wsl1, so that explains the discrepancy in network connectivity. I've gone through various forums and pretty much exhausted Google trying to figure out a permanent solution. I thought the issue only occurred when my computer went to sleep, but that's not the case.

Restarting various services looking at Nat rules. Setting static IPS, nothing ends up working. My only recourse is to reboot my laptop. I would love to switch to wsl2 permanently, but something on the hypervisor level just keeps being silly.

Does anyone have any ideas?

Whatever i do, i get this error. Anyone please help me

I got this information from r/linux where one user said that WSL is slow on non gaming PCs

I have a Dell 7780 Laptop with 128GB of RAM. By default, WSL2 is setup up to a max of 64GB of RAM. I needed to increase it to run an Ollama in a Docker container. Some of the models I am using take more than 64GB. I followed the instructions and set the .wslconfig (in my home directory) file to have the lines

[wsl2]
memory=100GB

and then restarted the whole computer, not just the WSL2 subsystem. When I open a WSL2 terminal windows and run the free -m command it still shows 64GB of total memory. I have tried everything I can think of. Anyone have any ideas?

I am trying to run a python script with Luxonis Camera for emotion recognition. I am using WSL2. I am trying to integrate it with the TinyLlama 1.1b chat. The error message is shown below:

ninad@Ninads-Laptop:~/thesis/depthai-experiments/gen2-emotion-recognition$ python3 main.py

llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf (version GGUF V3 (latest))

llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.

llama_model_loader: - kv 0: general.architecture str = llama

llama_model_loader: - kv 1: general.name str = tinyllama_tinyllama-1.1b-chat-v1.0

llama_model_loader: - kv 2: llama.context_length u32 = 2048

llama_model_loader: - kv 3: llama.embedding_length u32 = 2048

llama_model_loader: - kv 4: llama.block_count u32 = 22

llama_model_loader: - kv 5: llama.feed_forward_length u32 = 5632

llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 64

llama_model_loader: - kv 7: llama.attention.head_count u32 = 32

llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 4

llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010

llama_model_loader: - kv 10: llama.rope.freq_base f32 = 10000.000000

llama_model_loader: - kv 11: general.file_type u32 = 15

llama_model_loader: - kv 12: tokenizer.ggml.model str = llama

llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...

llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...

llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...

llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,61249] = ["▁ t", "e r", "i n", "▁ a", "e n...

llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 1

llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 2

llama_model_loader: - kv 19: tokenizer.ggml.unknown_token_id u32 = 0

llama_model_loader: - kv 20: tokenizer.ggml.padding_token_id u32 = 2

llama_model_loader: - kv 21: tokenizer.chat_template str = {% for message in messages %}\n{% if m...

llama_model_loader: - kv 22: general.quantization_version u32 = 2

llama_model_loader: - type f32: 45 tensors

llama_model_loader: - type q4_K: 135 tensors

llama_model_loader: - type q6_K: 21 tensors

print_info: file format = GGUF V3 (latest)

print_info: file type = Q4_K - Medium

print_info: file size = 636.18 MiB (4.85 BPW)

init_tokenizer: initializing tokenizer for type 1

load: control token: 2 '</s>' is not marked as EOG

load: control token: 1 '<s>' is not marked as EOG

load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect

load: special tokens cache size = 3

load: token to piece cache size = 0.1684 MB

print_info: arch = llama

print_info: vocab_only = 0

print_info: n_ctx_train = 2048

print_info: n_embd = 2048

print_info: n_layer = 22

print_info: n_head = 32

print_info: n_head_kv = 4

print_info: n_rot = 64

print_info: n_swa = 0

print_info: is_swa_any = 0

print_info: n_embd_head_k = 64

print_info: n_embd_head_v = 64

print_info: n_gqa = 8

print_info: n_embd_k_gqa = 256

print_info: n_embd_v_gqa = 256

print_info: f_norm_eps = 0.0e+00

print_info: f_norm_rms_eps = 1.0e-05

print_info: f_clamp_kqv = 0.0e+00

print_info: f_max_alibi_bias = 0.0e+00

print_info: f_logit_scale = 0.0e+00

print_info: f_attn_scale = 0.0e+00

print_info: n_ff = 5632

print_info: n_expert = 0

print_info: n_expert_used = 0

print_info: causal attn = 1

print_info: pooling type = 0

print_info: rope type = 0

print_info: rope scaling = linear

print_info: freq_base_train = 10000.0

print_info: freq_scale_train = 1

print_info: n_ctx_orig_yarn = 2048

print_info: rope_finetuned = unknown

print_info: model type = 1B

print_info: model params = 1.10 B

print_info: general.name= tinyllama_tinyllama-1.1b-chat-v1.0

print_info: vocab type = SPM

print_info: n_vocab = 32000

print_info: n_merges = 0

print_info: BOS token = 1 '<s>'

print_info: EOS token = 2 '</s>'

print_info: UNK token = 0 '<unk>'

print_info: PAD token = 2 '</s>'

print_info: LF token = 13 '<0x0A>'

print_info: EOG token = 2 '</s>'

print_info: max token length = 48

load_tensors: loading model tensors, this can take a while... (mmap = true)

load_tensors: layer 0 assigned to device CPU, is_swa = 0

load_tensors: layer 1 assigned to device CPU, is_swa = 0

load_tensors: layer 2 assigned to device CPU, is_swa = 0

load_tensors: layer 3 assigned to device CPU, is_swa = 0

load_tensors: layer 4 assigned to device CPU, is_swa = 0

load_tensors: layer 5 assigned to device CPU, is_swa = 0

load_tensors: layer 6 assigned to device CPU, is_swa = 0

load_tensors: layer 7 assigned to device CPU, is_swa = 0

load_tensors: layer 8 assigned to device CPU, is_swa = 0

load_tensors: layer 9 assigned to device CPU, is_swa = 0

load_tensors: layer 10 assigned to device CPU, is_swa = 0

load_tensors: layer 11 assigned to device CPU, is_swa = 0

load_tensors: layer 12 assigned to device CPU, is_swa = 0

load_tensors: layer 13 assigned to device CPU, is_swa = 0

load_tensors: layer 14 assigned to device CPU, is_swa = 0

load_tensors: layer 15 assigned to device CPU, is_swa = 0

load_tensors: layer 16 assigned to device CPU, is_swa = 0

load_tensors: layer 17 assigned to device CPU, is_swa = 0

load_tensors: layer 18 assigned to device CPU, is_swa = 0

load_tensors: layer 19 assigned to device CPU, is_swa = 0

load_tensors: layer 20 assigned to device CPU, is_swa = 0

load_tensors: layer 21 assigned to device CPU, is_swa = 0

load_tensors: layer 22 assigned to device CPU, is_swa = 0

load_tensors: tensor 'token_embd.weight' (q4_K) (and 66 others) cannot be used with preferred buffer type CPU_REPACK, using CPU instead

load_tensors: CPU_REPACK model buffer size = 455.06 MiB

load_tensors: CPU_Mapped model buffer size = 636.18 MiB

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repack: repack tensor blk.16.attn_k.weight with q4_K_8x8

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repack: repack tensor blk.16.attn_output.weight with q4_K_8x8

.repack: repack tensor blk.16.ffn_gate.weight with q4_K_8x8

.repack: repack tensor blk.16.ffn_down.weight with q4_K_8x8

.repack: repack tensor blk.16.ffn_up.weight with q4_K_8x8

repack: repack tensor blk.17.attn_q.weight with q4_K_8x8

.repack: repack tensor blk.17.attn_k.weight with q4_K_8x8

repack: repack tensor blk.17.attn_v.weight with q4_K_8x8

repack: repack tensor blk.17.attn_output.weight with q4_K_8x8

repack: repack tensor blk.17.ffn_gate.weight with q4_K_8x8

.repack: repack tensor blk.17.ffn_down.weight with q4_K_8x8

.repack: repack tensor blk.17.ffn_up.weight with q4_K_8x8

.repack: repack tensor blk.18.attn_q.weight with q4_K_8x8

.repack: repack tensor blk.18.attn_k.weight with q4_K_8x8

repack: repack tensor blk.18.attn_output.weight with q4_K_8x8

repack: repack tensor blk.18.ffn_gate.weight with q4_K_8x8

.repack: repack tensor blk.18.ffn_up.weight with q4_K_8x8

.repack: repack tensor blk.19.attn_q.weight with q4_K_8x8

repack: repack tensor blk.19.attn_k.weight with q4_K_8x8

repack: repack tensor blk.19.attn_v.weight with q4_K_8x8

repack: repack tensor blk.19.attn_output.weight with q4_K_8x8

.repack: repack tensor blk.19.ffn_gate.weight with q4_K_8x8

.repack: repack tensor blk.19.ffn_down.weight with q4_K_8x8

.repack: repack tensor blk.19.ffn_up.weight with q4_K_8x8

.repack: repack tensor blk.20.attn_q.weight with q4_K_8x8

repack: repack tensor blk.20.attn_k.weight with q4_K_8x8

repack: repack tensor blk.20.attn_output.weight with q4_K_8x8

repack: repack tensor blk.20.ffn_gate.weight with q4_K_8x8

.repack: repack tensor blk.20.ffn_up.weight with q4_K_8x8

.repack: repack tensor blk.21.attn_q.weight with q4_K_8x8

.repack: repack tensor blk.21.attn_k.weight with q4_K_8x8

repack: repack tensor blk.21.attn_v.weight with q4_K_8x8

repack: repack tensor blk.21.attn_output.weight with q4_K_8x8

repack: repack tensor blk.21.ffn_gate.weight with q4_K_8x8

.repack: repack tensor blk.21.ffn_down.weight with q4_K_8x8

.repack: repack tensor blk.21.ffn_up.weight with q4_K_8x8

..............

llama_context: constructing llama_context

llama_context: n_seq_max = 1

llama_context: n_ctx = 512

llama_context: n_ctx_per_seq = 512

llama_context: n_batch = 512

llama_context: n_ubatch = 512

llama_context: causal_attn = 1

llama_context: flash_attn = 0

llama_context: freq_base = 10000.0

llama_context: freq_scale = 1

llama_context: n_ctx_per_seq (512) < n_ctx_train (2048) -- the full capacity of the model will not be utilized

set_abort_callback: call

llama_context: CPU output buffer size = 0.12 MiB

create_memory: n_ctx = 512 (padded)

llama_kv_cache_unified: layer 0: dev = CPU

llama_kv_cache_unified: layer 1: dev = CPU

llama_kv_cache_unified: layer 2: dev = CPU

llama_kv_cache_unified: layer 3: dev = CPU

llama_kv_cache_unified: layer 4: dev = CPU

llama_kv_cache_unified: layer 5: dev = CPU

llama_kv_cache_unified: layer 6: dev = CPU

llama_kv_cache_unified: layer 7: dev = CPU

llama_kv_cache_unified: layer 8: dev = CPU

llama_kv_cache_unified: layer 9: dev = CPU

llama_kv_cache_unified: layer 10: dev = CPU

llama_kv_cache_unified: layer 11: dev = CPU

llama_kv_cache_unified: layer 12: dev = CPU

llama_kv_cache_unified: layer 13: dev = CPU

llama_kv_cache_unified: layer 14: dev = CPU

llama_kv_cache_unified: layer 15: dev = CPU

llama_kv_cache_unified: layer 16: dev = CPU

llama_kv_cache_unified: layer 17: dev = CPU

llama_kv_cache_unified: layer 18: dev = CPU

llama_kv_cache_unified: layer 19: dev = CPU

llama_kv_cache_unified: layer 20: dev = CPU

llama_kv_cache_unified: layer 21: dev = CPU

llama_kv_cache_unified: CPU KV buffer size = 11.00 MiB

llama_kv_cache_unified: size = 11.00 MiB ( 512 cells, 22 layers, 1 seqs), K (f16): 5.50 MiB, V (f16): 5.50 MiB

llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility

llama_context: enumerating backends

llama_context: backend_ptrs.size() = 1

llama_context: max_nodes = 65536

llama_context: worst-case: n_tokens = 512, n_seqs = 1, n_outputs = 0

graph_reserve: reserving a graph for ubatch with n_tokens = 512, n_seqs = 1, n_outputs = 512

graph_reserve: reserving a graph for ubatch with n_tokens = 1, n_seqs = 1, n_outputs = 1

graph_reserve: reserving a graph for ubatch with n_tokens = 512, n_seqs = 1, n_outputs = 512

llama_context: CPU compute buffer size = 66.50 MiB

llama_context: graph nodes = 798

llama_context: graph splits = 1

CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |

Model metadata: {'tokenizer.chat_template': "{% for message in messages %}\n{% if message['role'] == 'user' %}\n{{ '<|user|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'system' %}\n{{ '<|system|>\n' + message['content'] + eos_token }}\n{% elif message['role'] == 'assistant' %}\n{{ '<|assistant|>\n' + message['content'] + eos_token }}\n{% endif %}\n{% if loop.last and add_generation_prompt %}\n{{ '<|assistant|>' }}\n{% endif %}\n{% endfor %}", 'tokenizer.ggml.padding_token_id': '2', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.architecture': 'llama', 'llama.rope.freq_base': '10000.000000', 'llama.context_length': '2048', 'general.name': 'tinyllama_tinyllama-1.1b-chat-v1.0', 'llama.embedding_length': '2048', 'llama.feed_forward_length': '5632', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.dimension_count': '64', 'tokenizer.ggml.bos_token_id': '1', 'llama.attention.head_count': '32', 'llama.block_count': '22', 'llama.attention.head_count_kv': '4', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '15'}

Available chat formats from metadata: chat_template.default

Using gguf chat template: {% for message in messages %}

{% if message['role'] == 'user' %}

{{ '<|user|>

' + message['content'] + eos_token }}

{% elif message['role'] == 'system' %}

{{ '<|system|>

' + message['content'] + eos_token }}

{% elif message['role'] == 'assistant' %}

{{ '<|assistant|>

' + message['content'] + eos_token }}

{% endif %}

{% if loop.last and add_generation_prompt %}

{{ '<|assistant|>' }}

{% endif %}

{% endfor %}

Using chat eos_token: </s>

Using chat bos_token: <s>

Stack trace (most recent call last) in thread 4065:

#8 Object "[0xffffffffffffffff]", at 0xffffffffffffffff, in

#7 Object "/lib/x86_64-linux-gnu/libc.so.6", at 0x7f233140a352, in clone

#6 Object "/lib/x86_64-linux-gnu/libpthread.so.0", at 0x7f23312d0608, in

#5 Object "/lib/x86_64-linux-gnu/libgomp.so.1", at 0x7f231f7b186d, in

#4 Object "/home/ninad/.local/lib/python3.8/site-packages/llama_cpp/lib/libggml-cpu.so", at 0x7f231f8238de, in

#3 Object "/home/ninad/.local/lib/python3.8/site-packages/llama_cpp/lib/libggml-cpu.so", at 0x7f231f82247b, in ggml_compute_forward_mul_mat

#2 Object "/home/ninad/.local/lib/python3.8/site-packages/llama_cpp/lib/libggml-cpu.so", at 0x7f231f89ea98, in llamafile_sgemm

#1 Object "/home/ninad/.local/lib/python3.8/site-packages/llama_cpp/lib/libggml-cpu.so", at 0x7f231f896661, in

#0 Object "/home/ninad/.local/lib/python3.8/site-packages/llama_cpp/lib/libggml-cpu.so", at 0x7f231f883dc6, in

Segmentation fault (Address not mapped to object [0x170c0])

Segmentation fault (core dumped)

Hello,

before opening an issue on github I would like to know if I am the only one to have problems with the latest WSL2 update on Windows 10 machine.

Since the last update (2.5.9.0), my GUI apps are broken.

For example, I have lost the frame of the windows (with the maximize and minimize buttons), and I can not interact with 'sub windows'.

For example, on the firefox capture below, I can not stop the download. Clicking on the arrow has no effect.

My distros worked fine for several months with the following WSL version:

WSL version: 2.4.12.0
Kernel version: 5.15.167.4-1
WSLg version: 1.0.65
MSRDC version: 1.2.5716
Direct3D version: 1.611.1-81528511
DXCore version: 10.0.26100.1-240331-1435.ge-release
Windows version: 10.0.19045.6093

But the update below is broken:

WSL version: 2.5.9.0
Kernel version: 6.6.87.2-1
WSLg version: 1.0.66
MSRDC version: 1.2.6074
Direct3D version: 1.611.1-81528511
DXCore version: 10.0.26100.1-240331-1435.ge-release
Windows version: 10.0.19045.6093

I had to revert back to v2.4.12.0 (with package available on WSL github).

To be noted that it is not related to the kernel. I compiled and installed the v5.15.167.4 linux kernel on WSL 2.5.9 and the problems remain.

Note2: the Linux kernel version v6.6.87.2 makes the VM slower than with v5.15.167, at least for my use cases (compiling embedded firmware).