Triangle104
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README.md
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This model was converted to GGUF format from [`ToastyPigeon/Ruby-Music-8B`](https://huggingface.co/ToastyPigeon/Ruby-Music-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/ToastyPigeon/Ruby-Music-8B) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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This model was converted to GGUF format from [`ToastyPigeon/Ruby-Music-8B`](https://huggingface.co/ToastyPigeon/Ruby-Music-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/ToastyPigeon/Ruby-Music-8B) for more details on the model.
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---
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Note that this model is based on InternLM3, not LLaMA 3.
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A roleplaying/creative-writing fine tune of internlm/internlm3-8b-instruct, provided as an alternative to L3 8B for folks with 8GB VRAM.
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This was trained on a mix of private instruct (~1k samples) and
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roleplaying (~2.5k human and ~1k synthetic samples), along with the
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following public datasets:
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allenai/tulu-3-sft-personas-instruction-following (~500 samples)
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PocketDoc/Dans-Prosemaxx-Gutenberg (all samples)
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ToastyPigeon/SpringDragon-Instruct (~500 samples)
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allura-org/fujin-cleaned-stage-2 (~500 samples)
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The instruct format is standard ChatML:
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<|im_start|>system
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{system prompt}<|im_end|>
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<|im_start|>user
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{user message}<|im_end|>
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<|im_start|>assistant
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{assistant response}<|im_end|>
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Recommended sampler settings:
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temp 1
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smoothing factor 0.5, smoothing curve 1
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DRY 0.5/1.75/5/1024
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There may be better sampler settings, but this at least has proven
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stable in my testing. InternLM3 requires a high amount of tail filtering
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(high min-p, top-a, or something similar) to avoid making strange typos
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and spelling mistakes. Note: this might be a current issue with llama.cpp and the GGUF versions I tested.
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Notes:
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I noticed this model has trouble outputting the EOS token sometimes (despite confirming that <|im_end|>
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appears at the end of every turn in the training data). This can cause
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it to ramble at the end of a message instead of ending its turn.
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You can either cut the end out of the messages until it picks up the
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right response length, or use logit bias. I've had success getting
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right-sized turns setting logit bias for <|im_end|> to 2.
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---
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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