GGUF
Merge
mergekit
lazymergekit
Weyaxi/Einstein-v4-7B
Locutusque/Hercules-3.1-Mistral-7B
conversational
Instructions to use Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M
Use 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 Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M
Build 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 Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M
Use Docker
docker model run hf.co/Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf with Ollama:
ollama run hf.co/Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf with Docker Model Runner:
docker model run hf.co/Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M
- Lemonade
How to use Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Q4_K_M
Run and chat with the model
lemonade run user.J.O.S.I.E.3-Beta5-slerp-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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 Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:# Run inference directly in the terminal:
llama cli -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Use 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 Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:# Run inference directly in the terminal:
./llama-cli -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Build 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 Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:# Run inference directly in the terminal:
./build/bin/llama-cli -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Use Docker
docker model run hf.co/Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:Quick Links
JOSIE_Beta-5-7B-slerp
JOSIE_Beta-5-7B-slerp is a merge of the following models using LazyMergekit:
🧩 Configuration
slices:
- sources:
- model: Weyaxi/Einstein-v4-7B
layer_range: [0, 32]
- model: Locutusque/Hercules-3.1-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: Weyaxi/Einstein-v4-7B
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 = "Isaak-Carter/JOSIE_Beta-5-7B-slerp"
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 Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf:# Run inference directly in the terminal: llama cli -hf Goekdeniz-Guelmez/J.O.S.I.E.3-Beta5-slerp-gguf: