How to use from
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 JoPmt/DeepMeeker-7B-Base-Ties-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf JoPmt/DeepMeeker-7B-Base-Ties-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 JoPmt/DeepMeeker-7B-Base-Ties-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf JoPmt/DeepMeeker-7B-Base-Ties-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 JoPmt/DeepMeeker-7B-Base-Ties-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf JoPmt/DeepMeeker-7B-Base-Ties-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 JoPmt/DeepMeeker-7B-Base-Ties-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf JoPmt/DeepMeeker-7B-Base-Ties-GGUF:Q4_K_M
Use Docker
docker model run hf.co/JoPmt/DeepMeeker-7B-Base-Ties-GGUF:Q4_K_M
Quick Links

DeepMeeker-7B-Base-Ties

DeepMeeker-7B-Base-Ties is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: ise-uiuc/Magicoder-DS-6.7B
    parameters:
      weight: 1
      density: 1
  - model: deepseek-ai/deepseek-coder-6.7b-base
    parameters:
      weight: 1
      density: 1
merge_method: ties
base_model: ise-uiuc/Magicoder-DS-6.7B
parameters:
  weight: 1
  density: 1
  normalize: true
  int8_mask: false
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "JoPmt/DeepMeeker-7B-Base-Ties"
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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GGUF
Model size
7B params
Architecture
llama
Hardware compatibility
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