models(gallery): add llama-3.1-storm-8b-q4_k_m (#3270)

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
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Ettore Di Giacinto 2024-08-19 11:39:47 +02:00 committed by GitHub
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- filename: Fireball-Llama-3.11-8B-v1orpo.Q4_K_M.gguf - filename: Fireball-Llama-3.11-8B-v1orpo.Q4_K_M.gguf
sha256: c61a1f4ee4f05730ac6af754dc8dfddf34eba4486ffa320864e16620d6527731 sha256: c61a1f4ee4f05730ac6af754dc8dfddf34eba4486ffa320864e16620d6527731
uri: huggingface://mradermacher/Fireball-Llama-3.11-8B-v1orpo-GGUF/Fireball-Llama-3.11-8B-v1orpo.Q4_K_M.gguf uri: huggingface://mradermacher/Fireball-Llama-3.11-8B-v1orpo-GGUF/Fireball-Llama-3.11-8B-v1orpo.Q4_K_M.gguf
- !!merge <<: *llama31
name: "llama-3.1-storm-8b-q4_k_m"
icon: https://cdn-uploads.huggingface.co/production/uploads/64c75c1237333ccfef30a602/tmOlbERGKP7JSODa6T06J.jpeg
urls:
- https://huggingface.co/mudler/Llama-3.1-Storm-8B-Q4_K_M-GGUF
- https://huggingface.co/akjindal53244/Llama-3.1-Storm-8B
description: |
We present the Llama-3.1-Storm-8B model that outperforms Meta AI's Llama-3.1-8B-Instruct and Hermes-3-Llama-3.1-8B models significantly across diverse benchmarks as shown in the performance comparison plot in the next section. Our approach consists of three key steps:
- Self-Curation: We applied two self-curation methods to select approximately 1 million high-quality examples from a pool of about 3 million open-source examples. Our curation criteria focused on educational value and difficulty level, using the same SLM for annotation instead of larger models (e.g. 70B, 405B).
- Targeted fine-tuning: We performed Spectrum-based targeted fine-tuning over the Llama-3.1-8B-Instruct model. The Spectrum method accelerates training by selectively targeting layer modules based on their signal-to-noise ratio (SNR), and freezing the remaining modules. In our work, 50% of layers are frozen.
- Model Merging: We merged our fine-tuned model with the Llama-Spark model using SLERP method. The merging method produces a blended model with characteristics smoothly interpolated from both parent models, ensuring the resultant model captures the essence of both its parents. Llama-3.1-Storm-8B improves Llama-3.1-8B-Instruct across 10 diverse benchmarks. These benchmarks cover areas such as instruction-following, knowledge-driven QA, reasoning, truthful answer generation, and function calling.
overrides:
parameters:
model: llama-3.1-storm-8b-q4_k_m.gguf
files:
- filename: llama-3.1-storm-8b-q4_k_m.gguf
sha256: d714e960211ee0fe6113d3131a6573e438f37debd07e1067d2571298624414a0
uri: huggingface://mudler/Llama-3.1-Storm-8B-Q4_K_M-GGUF/llama-3.1-storm-8b-q4_k_m.gguf
## Uncensored models ## Uncensored models
- !!merge <<: *llama31 - !!merge <<: *llama31
name: "humanish-roleplay-llama-3.1-8b-i1" name: "humanish-roleplay-llama-3.1-8b-i1"