GGUF
Merge
mergekit
lazymergekit
liminerity/Omningotex-7b-slerp
eren23/dpo-binarized-NeutrixOmnibe-7B
How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf paulml/OGNO-7B-GGUF:Q4_K_M# Run inference directly in the terminal:
llama cli -hf paulml/OGNO-7B-GGUF:Q4_K_MUse pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf paulml/OGNO-7B-GGUF:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf paulml/OGNO-7B-GGUF:Q4_K_MBuild from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf paulml/OGNO-7B-GGUF:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf paulml/OGNO-7B-GGUF:Q4_K_MUse Docker
docker model run hf.co/paulml/OGNO-7B-GGUF:Q4_K_MQuick Links
As most of the new merges, the quantized version is not working properly.
OGNO-7B
OGNO-7B is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: liminerity/Omningotex-7b-slerp
layer_range: [0, 32]
- model: eren23/dpo-binarized-NeutrixOmnibe-7B
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/Omningotex-7b-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "paulml/OGNO-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf paulml/OGNO-7B-GGUF:Q4_K_M# Run inference directly in the terminal: llama cli -hf paulml/OGNO-7B-GGUF:Q4_K_M