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speechless-code-mistral-7b-v1.0

uukuguy

Use the following dataset to fine-tune mistralai/Mistral-7B-v0.1 in order to improve the model's reasoning and planning abilities. Total 201,981 samples. - jondurbin/airoboros-2.2: Filter categories related to coding, reasoning and planning. 23,462 samples. - Open-Orca/OpenOrca: Filter the 'cot' category in 1M GPT4 dataset. 74,440 samples. - garage-bAInd/Open-Platypus: 100%, 24,926 samples. - WizardLM/WizardLM_evol_instruct_V2_196k: Coding coversation part. 30,185 samples - TokenBender/python_eval_instruct_51k: “python” in output .40,309 samples - Spider: 8,659 samples

Text generation

Input Text→ Output Text

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speechless-coder-ds-6.7b

uukuguy

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

Basic profile indexed · More details to follow

Text generation

Input Text→ Output Text

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speechless-zephyr-code-functionary-7b

uukuguy

Extract LoRA modules from below models (all based Mistral-7B-v0.1), each LoRA module has its own unique skills. By using multi-loras, they can be combined together statically or dynamically to form a versatile new model. The entire process is completed through the use of extract-lora, merge-lora, and lora-hub provided by multi-loras. The router of mixture-of-multi-loras enables an automatic assembling of LoRA modules, using a gradientfree approach to obtain the coefficients of LoRA modules and requiring only a handful of inference steps for unseen tasks.

Text generation

Input Text→ Output Text

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Adjust monthly token usage to compare estimated API costs.

Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
Compares base text-token rates within the same channel

Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.

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Explore AI models 3

speechless-code-mistral-7b-v1.0

speechless-coder-ds-6.7b

speechless-zephyr-code-functionary-7b

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