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RichardErkhov/ericpolewski_-_AIRIC-The-Mistral-gguf
ericpolewski_-_AIRIC-The-Mistral-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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3% of all-time downloads
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.gguf95.2 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
AIRIC-The-Mistral - GGUF
| Name | Quant method | Size |
|---|---|---|
| AIRIC-The-Mistral.Q2_K.gguf | Q2_K | 2.53GB |
| AIRIC-The-Mistral.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| AIRIC-The-Mistral.IQ3_S.gguf | IQ3_S | 2.96GB |
| AIRIC-The-Mistral.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| AIRIC-The-Mistral.IQ3_M.gguf | IQ3_M | 3.06GB |
| AIRIC-The-Mistral.Q3_K.gguf | Q3_K | 3.28GB |
| AIRIC-The-Mistral.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| AIRIC-The-Mistral.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| AIRIC-The-Mistral.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| AIRIC-The-Mistral.Q4_0.gguf | Q4_0 | 3.83GB |
| AIRIC-The-Mistral.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| AIRIC-The-Mistral.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| AIRIC-The-Mistral.Q4_K.gguf | Q4_K | 4.07GB |
| AIRIC-The-Mistral.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| AIRIC-The-Mistral.Q4_1.gguf | Q4_1 | 4.24GB |
| AIRIC-The-Mistral.Q5_0.gguf | Q5_0 | 4.65GB |
| AIRIC-The-Mistral.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| AIRIC-The-Mistral.Q5_K.gguf | Q5_K | 4.78GB |
| AIRIC-The-Mistral.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| AIRIC-The-Mistral.Q5_1.gguf | Q5_1 | 5.07GB |
| AIRIC-The-Mistral.Q6_K.gguf | Q6_K | 5.53GB |
| AIRIC-The-Mistral.Q8_0.gguf | Q8_0 | 7.17GB |
license: mit datasets:
This is Mistral-v0.1 and a combination of the AIRIC dataset sprinkled into the other datasets listed. Trained for 3 epochs at rank 128 until loss hit about 1.37. I noticed some "it's important to remembers" in there that I may try to scrub out but otherwise the model wasn't intentionally censored.
The intent was to create a robot that I could converse with as well as use as an assistant. If you ask it what it's up to, it'll make something up as if it actually had a life with the right parameters. Before releasing it, I mixed in a lot of OpenOrca data vs what I put out as a chatbot originally to make it more genuinely useful. Set the top_p to .98 to get the most social results.
This was the original post: https://www.reddit.com/r/LocalLLaMA/comments/154to1w/i_trained_the_65b_model_on_my_texts_so_i_can_talk/
This is how I did the data extraction: https://www.linkedin.com/pulse/how-i-trained-ai-my-text-messages-make-robot-talks-like-eric-polewski-9nu1c/
This is an instruct model trained in the Alpaca format.
5-bit exl2 available at https://huggingface.co/ericpolewski/AIRIC-The-Mistral-5.0bpw-exl2
8-bit exl2 available at https://huggingface.co/ericpolewski/AIRIC-The-Mistral-8.0bpw-exl2