Kimiko-Mistral-7B-fp16

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Kimiko Mistral 7B - FP16

• Model creator: nRuaif
• Original model: Kimiko Mistral 7B

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Description

This repo contains pytorch format fp16 model files for nRuaif's Kimiko Mistral 7B.

It is the result of either merging a LoRA, or converting the source repository to float16.

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Repositories available

• AWQ model(s) for GPU inference.
• GPTQ models for GPU inference, with multiple quantisation parameter options.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
• Unquantised fp16 model in pytorch format, for GPU inference and for further conversions
• nRuaif's original LoRA adapter, which can be merged on to the base model.

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You are a helpful AI assistant.

USER: {prompt}
ASSISTANT:

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Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

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Special thanks to: Aemon Algiz.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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Original model card: nRuaif's Kimiko Mistral 7B

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Kimiko-Mistral-7B

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the Kimiko dataset. It achieves the following results on the evaluation set:

• Loss: 2.1173

Model description

Same dataset as Kimiko-v2 but on new model. THIS IS NOT TRAIN ON V3 DATASET

Intended uses & limitations

As a finetuning experiment on new 7B model. You can use this for roleplay or as an assistant

Prompt Template Structure

This is a chat between ASSISTANT and USER
USER: What is 4x8?
ASSISTANT:

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.00005
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 16
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 2

Training results

Framework versions

• Transformers 4.34.0.dev0
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.14.0