How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jebadiah/Aria-tree-35-coder-7b:F16
# Run inference directly in the terminal:
llama-cli -hf Jebadiah/Aria-tree-35-coder-7b:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jebadiah/Aria-tree-35-coder-7b:F16
# Run inference directly in the terminal:
llama-cli -hf Jebadiah/Aria-tree-35-coder-7b:F16
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 Jebadiah/Aria-tree-35-coder-7b:F16
# Run inference directly in the terminal:
./llama-cli -hf Jebadiah/Aria-tree-35-coder-7b:F16
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 Jebadiah/Aria-tree-35-coder-7b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jebadiah/Aria-tree-35-coder-7b:F16
Use Docker
docker model run hf.co/Jebadiah/Aria-tree-35-coder-7b:F16
Quick Links

Aria-tree-35-coder-7b

Aria-tree-35-coder-7b is a merge of the following models using LazyMergekit:

🧩 Configuration

name: Jebadiah/Aria-tree-35-coder-7b
models:
  - model: Jebadiah/Aria-coder-7b
    parameters:
      density: 1.0
      weight: 1.0

merge_method: passthrough

layers:
  - source:
      layer_range: [0, 15]  # layers before the middle
    target:
      layer_range: [0, 15]

  - source:
      layer_range: [16, 16]  # duplicate layer 16
    target:
      layer_range: [16, 18]  # copy to positions 16, 17 and 18

  - source:
      layer_range: [17, 17]  # duplicate layer 17
    target:
      layer_range: [19, 20]  # copy to positions 19 and 20     

  - source:
      layer_range: [18, 31]  # layers after the middle (shifted by 3)
    target:
      layer_range: [21, 34]

dtype: float16

πŸ’» Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Jebadiah/Aria-tree-35-coder-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"])
Downloads last month
8
Safetensors
Model size
7B params
Tensor type
F16
Β·
Inference Providers NEW
This model isn't deployed by any Inference Provider. πŸ™‹ Ask for provider support

Model tree for Jebadiah/Aria-tree-35-coder-7b

Quantized
(2)
this model
Quantizations
1 model