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csabakecskemeti 
posted an update 1 day ago
csabakecskemeti 
posted an update 17 days ago
csabakecskemeti 
posted an update 18 days ago
csabakecskemeti 
posted an update 20 days ago
csabakecskemeti 
posted an update 22 days ago
csabakecskemeti 
posted an update 23 days ago
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1468
I've built a small utility to split safetensors file by file.
The issue/need came up when I've tried to convert the new Deepseek V3 model from FP8 to BF16.
The only Ada architecture GPU I have is an RTX 4080 and the 16GB vram was just wasn't enough for the conversion.

BTW: I'll upload the bf16 version here:
DevQuasar/deepseek-ai.DeepSeek-V3-Base-bf16
(it will take a while - days with my upload speed)
If anyone has access the resources to test it I'd appreciate a feedback if it's working or not.

The tool, is available from here:
https://github.com/csabakecskemeti/ai_utils/blob/main/safetensor_splitter.py
It's splitting every file to n pieces by the layers if possible, and create a new "model.safetensors.index.json" file.
I've tested it with Llama 3.1 8B and multiple split sizes, and validated by using inference pipeline.
use --help for usage
Please note current version expects the model is already multiple file and have a "model.safetensors.index.json" layer-safetensor mapping file.
csabakecskemeti 
posted an update about 1 month ago
csabakecskemeti 
posted an update about 1 month ago
csabakecskemeti 
posted an update about 2 months ago
csabakecskemeti 
posted an update about 2 months ago
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1182
I have this small utility: no_more_typo
It is running in the background and able to call the LLM model to update the text on the clipboard. I think it would be ideal to fix typos and syntax.
I have just added the option to use custom prompt templates to perform different tasks.

Details, code and executable:
https://github.com/csabakecskemeti/no_more_typo

https://devquasar.com/no-more-typo/
csabakecskemeti 
posted an update about 2 months ago
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294
Repurposed my older AI workstation to a homelab server, it has received 2xV100 + 1xP40
I can reach huge 210k token context size with MegaBeam-Mistral-7B-512k-GGUF ~70+tok/s, or run Llama-3.1-Nemotron-70B-Instruct-HF-GGUF with 50k Context ~10tok/s (V100 only 40k ctx and 15tok/s).
Also able to Lora finetune with similar performace as an RTX3090.
It moved to the garage to no complaints for the noise from the family. Will move to a Rack soon :D
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csabakecskemeti 
posted an update 2 months ago
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1244
Some time ago, I built a predictive LLM router that routes chat requests between small and large LLM models based on prompt classification. It dynamically selects the most suitable model depending on the complexity of the user input, ensuring optimal performance while maintaining conversation context. I also fine-tuned a RoBERTa model to use with the package, but you can plug and play any classifier of your choice.

Project's homepage:
https://devquasar.com/llm-predictive-router/
Pypi:
https://pypi.org/project/llm-predictive-router/
Model:
DevQuasar/roberta-prompt_classifier-v0.1
Training data:
DevQuasar/llm_router_dataset-synth
Git:
https://github.com/csabakecskemeti/llm_predictive_router_package

Feel free to check it out, and/or contribute.
csabakecskemeti 
posted an update 2 months ago
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1511
I've built a small open utility pip package called LLM-Forwarder that allows you to inject context, such as adding a private RAG, into existing chat applications by forwarding the app through the LLM-Forwarder. In the forwarder server, you can configure custom code to re-process chat messages and alter the user prompt, for example, by adding extra context.

https://pypi.org/project/llm-forwarder/
More details
https://devquasar.com/llmforwarder/
navidmadani 
updated a dataset 4 months ago
tono 
authored a paper 5 months ago
tono 
authored a paper 7 months ago