invisietch
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README.md
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- not-for-all-audiences
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- axolotl
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- qlora
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- llama-cpp
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- gguf-my-repo
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language:
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- en
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license: other
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base_model: invisietch/MiS-Firefly-v0.2-22B
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---
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Refer to the [original model card](https://huggingface.co/invisietch/MiS-Firefly-v0.2-22B) for more details on the model.
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Install llama.cpp through brew (works on Mac and Linux)
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Invoke the llama.cpp server or the CLI.
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```bash
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llama-cli --hf-repo invisietch/MiS-Firefly-v0.2-22B-Q6_K-GGUF --hf-file mis-firefly-v0.2-22b-q6_k.gguf -p "The meaning to life and the universe is"
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```
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llama-server --hf-repo invisietch/MiS-Firefly-v0.2-22B-Q6_K-GGUF --hf-file mis-firefly-v0.2-22b-q6_k.gguf -c 2048
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```
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git clone https://github.com/ggerganov/llama.cpp
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```
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Step 3: Run inference through the main binary.
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```
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./llama-cli --hf-repo invisietch/MiS-Firefly-v0.2-22B-Q6_K-GGUF --hf-file mis-firefly-v0.2-22b-q6_k.gguf -p "The meaning to life and the universe is"
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```
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or
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```
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```
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- not-for-all-audiences
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- axolotl
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- qlora
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language:
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- en
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license: other
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---
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<div align="center">
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<b style="font-size: 36px;">MiS-Firefly-v0.2-22B (Q6_K)</b>
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<img src="https://huggingface.co/invisietch/MiS-Firefly-v0.2-22B/resolve/main/header.png" style="width:60%">
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<b>HF</b> :
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<a href="https://huggingface.co/invisietch/MiS-Firefly-v0.2-22B">FP16</a>
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<b>GGUF</b> :
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<a href="https://huggingface.co/invisietch/MiS-Firefly-v0.2-22B-Q6_K-GGUF">Q6_K</a> ·
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<a href="https://huggingface.co/invisietch/MiS-Firefly-v0.2-22B-Q4_K_M-GGUF">Q4_K_M</a>
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</div>
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# Model Details
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**This is a fix for the quantization issue in Firefly v0.1.**
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Firefly is a Mistral Small 22B finetune designed for creative writing and roleplay. The model is largely uncensored and should support
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context up to 32,768 tokens.
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The model has been tested in various roleplay scenarios up to 16k context, as well as in a role as an assistant. It shows a broad
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competency & coherence across various scenarios.
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Special thanks to <a href="https://huggingface.co/SicariusSicariiStuff">SicariusSicariiStuff</a> for bouncing ideas back & forth on
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training, and <a href="https://huggingface.co/SytanSD">SytanSD</a> for quants.
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# Feedback
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I appreciate all feedback on any of my models, you can use:
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* [My Discord server](https://discord.gg/AJwZuu7Ncx) - requires Discord.
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* [The Community tab](https://huggingface.co/invisietch/MiS-Firefly-v0.2-22B/discussions) - requires HF login.
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* Discord DMs to **invisietch**.
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Your feedback is how I improve these models for future versions.
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# Disclaimer
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This model is extensively uncensored. It can generate explicit, disturbing or offensive responses. Use responsibly. I am not responsible for
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your use of this model.
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This model is a finetune of Mistral Small 22B (2409) and usage must follow the terms of Mistral's license. By downloading this model, you
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agree not to use it for commercial purposes unless you have a valid Mistral commercial license. See [the base model card](https://huggingface.co/mistralai/Mistral-Small-Instruct-2409)
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for more details.
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# Prompting Format
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I'd recommend Mistral v2v3 prompting format:
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```
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<s>[INST] User message here.[/INST] Bot response here</s>[INST] User message 2 here.
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```
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# Sampler Settings
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I'm running the following sampler settings but this is an RC and they may not be optimal.
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- **Temperature:** Dynamic 0.7-1.1
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- **Min-P:** 0.07
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- **Rep Pen:** 1.08
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- **Rep Pen Range:** 1536
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- **XTC:** 0.1/0.15
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If you get completely incoherent responses, feel free to use these as a starting point.
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# Training Strategy
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I started with a finetune of Mistral Small 22B which had been trained on the Gutenberg dataset: [nbeerbower/Mistral-Small-Gutenberg-Doppel-22B](https://huggingface.co/nbeerbower/Mistral-Small-Gutenberg-Doppel-22B).
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The first stage of my training was a single epoch at low LR over a 474 million token text completion dataset.
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I followed this up with a coherence, decensorship & roleplay finetune over a 172 million token instruct dataset over two epochs.
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I did a slerp merge of epoch 1 into epoch 2 at a light weight which resolved the name-spelling issues on quantized versions of Firefly v0.1.
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Total training time was about 32hrs on 4x Nvidia A100 80GB.
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<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>
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