mpt30b not showing any response. about mpt-30b-inference HOT 8 OPEN

For cases like this I recommend docker because of the environment issues. I have windows as well, here's how I run it.
Use a container like so:

docker run -it --mount type=volume,source=transformers,target=/transformerspython:3.11.4 /bin/bash

Clone the repo:

git clone [email protected]:abacaj/mpt-30B-inference.git

Follow directions in the readme for the rest: https://github.com/abacaj/mpt-30B-inference#setup. I just ran through this process once again now, and it works I can get model to generate correctly on my Ryzen/Windows machine:

Thank you. Ive created a conda env, installed the requirements, manually downloaded 2 models (q5_1 and q4_1). Any hint on why this empty responses? I really prefer not to use a container.

Great work by the way!

Likely has to do with ctransformers library, since that is how the bindings work from python -> ggml (though I'm not certain of it)

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Issue Fixed Replace files with
https://github.com/mzubair31102/llama2.git

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I'm having the same problem. Processing goes to 100% for a few seconds but returns empty answers. It goes around 24Gb of RAM usage.
I tested in VScode and in cmd. Same behaviour.
Ive tried to debug, but the "generator" variable had no kind of string text inside it.

I'm running mpt-30b-chat.ggmlv0.q5_1.bin model instead of default q4_0.

PC: Ryzen 5900X and 32 Gb RAM.

from mpt-30b-inference.

For cases like this I recommend docker because of the environment issues. I have windows as well, here's how I run it.

Use a container like so:

docker run -it -w /transformers --mount type=volume,source=transformers,target=/transformers python:3.11.4 /bin/bash

Clone the repo:

git clone [email protected]:abacaj/mpt-30B-inference.git

Follow directions in the readme for the rest: https://github.com/abacaj/mpt-30B-inference#setup.
I just ran through this process once again now, and it works I can get model to generate correctly on my Ryzen/Windows machine:

from mpt-30b-inference.

For cases like this I recommend docker because of the environment issues. I have windows as well, here's how I run it.

Use a container like so:

docker run -it --mount type=volume,source=transformers,target=/transformerspython:3.11.4 /bin/bash

Clone the repo:

git clone [email protected]:abacaj/mpt-30B-inference.git

Follow directions in the readme for the rest: https://github.com/abacaj/mpt-30B-inference#setup. I just ran through this process once again now, and it works I can get model to generate correctly on my Ryzen/Windows machine:

Thank you.
Ive created a conda env, installed the requirements, manually downloaded 2 models (q5_1 and q4_1). Any hint on why this empty responses? I really prefer not to use a container.

Great work by the way!

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I have observed that when processing user queries, the CPU usage increases but I do not receive a response.
[user]: What is the capital of France?
[assistant]:
[user]"

from mpt-30b-inference.

For cases like this I recommend docker because of the environment issues. I have windows as well, here's how I run it.
Use a container like so:

docker run -it --mount type=volume,source=transformers,target=/transformerspython:3.11.4 /bin/bash

Clone the repo:

git clone [email protected]:abacaj/mpt-30B-inference.git

Follow directions in the readme for the rest: https://github.com/abacaj/mpt-30B-inference#setup. I just ran through this process once again now, and it works I can get model to generate correctly on my Ryzen/Windows machine:

Thank you. Ive created a conda env, installed the requirements, manually downloaded 2 models (q5_1 and q4_1). Any hint on why this empty responses? I really prefer not to use a container.

Great work by the way!

python3 inference.py
Fetching 1 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3584.88it/s]
GGML_ASSERT: /home/runner/work/ctransformers/ctransformers/models/ggml/ggml.c:4103: ctx->mem_buffer != NULL
Aborted

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I'm also facing this issue on Windows.
However, the main problem is when I run this on a container it produces very slow responses.

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