jondurbin/truthy-dpo-v0.1
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How to use QuantFactory/Flammades-Mistral-Nemo-12B-GGUF with Transformers:
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Flammades-Mistral-Nemo-12B-GGUF", device_map="auto")How to use QuantFactory/Flammades-Mistral-Nemo-12B-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M
docker model run hf.co/QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M
How to use QuantFactory/Flammades-Mistral-Nemo-12B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M
How to use QuantFactory/Flammades-Mistral-Nemo-12B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M
How to use QuantFactory/Flammades-Mistral-Nemo-12B-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Flammades-Mistral-Nemo-12B-GGUF:Q4_K_M
lemonade run user.Flammades-Mistral-Nemo-12B-GGUF-Q4_K_M
lemonade list
This is quantized version of flammenai/Flammades-Mistral-Nemo-12B created using llama.cpp
nbeerbower/Mistral-Nemo-Gutenberg-Doppel-12B-v2 finetuned on flammenai/Date-DPO-NoAsterisks and jondurbin/truthy-dpo-v0.1.
ORPO tuned with 2x RTX 3090 for 3 epochs.
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 22.34 |
| IFEval (0-Shot) | 38.42 |
| BBH (3-Shot) | 32.39 |
| MATH Lvl 5 (4-Shot) | 6.19 |
| GPQA (0-shot) | 7.16 |
| MuSR (0-shot) | 20.31 |
| MMLU-PRO (5-shot) | 29.57 |
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Base model
winglian/m12b-20240721-test010