Mixtral 8x7b Instruct V0 1 By Mistralai Benchmarks Features And

Mixtral 8x7b Instruct V0 1 Model By Mistral Ai Nvidia Nim With mixtral, it feels very natural, and errors are minimal. i've used some of the models specifically finetuned for german, but they didn't feel much better quality wise than standard llama mistral, they seemed to switch back to english less, but the quality didn't seem much higher. Because people need these results for business use cases. for example, for work use my company uses sonnet because it's very good and still pretty cheap to run. on personal projects i'll seriously look at mixtral 8x22b or llama 3 70b and self host for rag inference.

Mixtral 8x7b Instruct V0 1 With Prompt A Hugging Face Space By Qcommunity Mixtral is imho the best model (as in open source) out there, and so far i haven't had the same quality with any of the finetunes of it. the only problem is removing that partial alignment both for nsfw rp and also for creative writing, when you want to have a high quality but sardonic style that is usually filtered out by the 'alignment'. Recently (two days ago), i started using mixtral 8x7b (so not the ' instruct') because i was looking for a model with a large context (tired of being limited to 4k or 16k) for uncensored roleplay, and i found that one. I'm trying to run thebloke dolphin 2.5 mixtral 8x7b gguf on my laptop which is an hp omen 15 2020 (ryzen 7 4800h, 16gb ddr4, rtx 2060 with 6gb vram). currently getting into the local llm space just starting. Without a character card, in kobold.cpp interface, mixtral was censoring so bad, i had to switch sytnhia moe. however, the other day i tried with a character card using ali chat format with around 10 dialogue samples, using questions to describe the character, and their expected responses it never refused anything.

Mistralai Mixtral 8x7b Instruct V0 1 Demo Deepinfra I'm trying to run thebloke dolphin 2.5 mixtral 8x7b gguf on my laptop which is an hp omen 15 2020 (ryzen 7 4800h, 16gb ddr4, rtx 2060 with 6gb vram). currently getting into the local llm space just starting. Without a character card, in kobold.cpp interface, mixtral was censoring so bad, i had to switch sytnhia moe. however, the other day i tried with a character card using ali chat format with around 10 dialogue samples, using questions to describe the character, and their expected responses it never refused anything. But mixtral, uh, breaks this 'rule' regularly? what. interestingly, unlike the near total confidence tokens i was used to seeing in the past, it's possible for mixtral to be so occasionally confident in the next token choice that it is the only positive probability assigned period. the above token in question. Guide to run mixtral correctly. i see a lot of people using the wrong settings setup which makes it go schizo or repetitive. : r localllama     go to localllama r localllama r localllama. After iterating on this over the holiday break, i think i've finally got it working! goal: present a natural language question that the model translates to a sql query and or maps to a set of tools (functions). execute the query or tools and output result of execution. output should be formatted as a simple english sentence. model: mixtral via ollama method: create a sqlite database that. Mixtral of experts | mistral ai | open source models its like running a 70b model, and it competes with gpt3.5 and other 70b's. it has fast inference (40 tokens a sec on a measly 3090, close to what il get on a 13b) overall it can do code, rp, summarization, q&a and many more tasks that a 7b 13b would have hard time doing all of those,.