Triangle104/EVA-Qwen2.5-7B-v0.1-Q4_K_S-GGUF

This model was converted to GGUF format from EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

A RP/storywriting specialist model, full-parameter finetune of Qwen2.5-7B on mixture of synthetic and natural data.

It uses Celeste 70B 0.1 data mixture, greatly expanding it to improve versatility, creativity and "flavor" of the resulting model.

Version 0.1 notes: Dataset was deduped and cleaned from version 0.0, and learning rate was adjusted. Resulting model seems to be stabler, and 0.0 problems with handling short inputs and min_p sampling seem to be mostly gone.

Will be retrained once more, because this run crashed around e1.2 (out of 3) (thanks, DeepSpeed, really appreciate it), and it's still somewhat undertrained as a result.

Prompt format is ChatML.

Recommended sampler values:

Temperature: 0.87 Top-P: 0.81 Repetition Penalty: 1.03

Model appears to prefer lower temperatures (at least 0.9 and lower). Min-P seems to work now, as well.

Recommended SillyTavern presets (via CalamitousFelicitousness):

Context Instruct and System Prompt

Training data:

Celeste 70B 0.1 data mixture minus Opus Instruct subset. See that model's card for details. Kalomaze's Opus_Instruct_25k dataset, filtered for refusals. A subset (1k rows) of ChatGPT-4o-WritingPrompts by Gryphe A subset (2k rows) of Sonnet3.5-Charcards-Roleplay by Gryphe A cleaned subset (~3k rows) of shortstories_synthlabels by Auri Synthstruct and SynthRP datasets by Epiculous

 Training time and hardware:


  

2 days on 4x3090Ti (locally)

Model was trained by Kearm and Auri.

Special thanks: to Gryphe, Lemmy, Kalomaze, Nopm and Epiculous for the data to Alpindale for helping with FFT config for Qwen2.5 and to InfermaticAI's community for their continued support for our endeavors

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/EVA-Qwen2.5-7B-v0.1-Q4_K_S-GGUF --hf-file eva-qwen2.5-7b-v0.1-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/EVA-Qwen2.5-7B-v0.1-Q4_K_S-GGUF --hf-file eva-qwen2.5-7b-v0.1-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/EVA-Qwen2.5-7B-v0.1-Q4_K_S-GGUF --hf-file eva-qwen2.5-7b-v0.1-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/EVA-Qwen2.5-7B-v0.1-Q4_K_S-GGUF --hf-file eva-qwen2.5-7b-v0.1-q4_k_s.gguf -c 2048