Instructions to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf",
	filename="Dracarys2-72B-Instruct.IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-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 RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-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 RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M
```

Unsloth Studio new

How to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf to start chatting

Pi new

How to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/abacusai_-_Dracarys2-72B-Instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.abacusai_-_Dracarys2-72B-Instruct-gguf-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

Dracarys2-72B-Instruct - GGUF

Model creator: https://huggingface.co/abacusai/
Original model: https://huggingface.co/abacusai/Dracarys2-72B-Instruct/

Name	Quant method	Size
Dracarys2-72B-Instruct.Q2_K.gguf	Q2_K	27.76GB
Dracarys2-72B-Instruct.IQ3_XS.gguf	IQ3_XS	30.59GB
Dracarys2-72B-Instruct.IQ3_S.gguf	IQ3_S	32.12GB
Dracarys2-72B-Instruct.Q3_K_S.gguf	Q3_K_S	32.12GB
Dracarys2-72B-Instruct.IQ3_M.gguf	IQ3_M	33.07GB
Dracarys2-72B-Instruct.Q3_K.gguf	Q3_K	35.11GB
Dracarys2-72B-Instruct.Q3_K_M.gguf	Q3_K_M	29.28GB
Dracarys2-72B-Instruct.Q3_K_L.gguf	Q3_K_L	36.79GB
Dracarys2-72B-Instruct.IQ4_XS.gguf	IQ4_XS	7.05GB
Dracarys2-72B-Instruct.Q4_0.gguf	Q4_0	38.4GB
Dracarys2-72B-Instruct.IQ4_NL.gguf	IQ4_NL	38.9GB
Dracarys2-72B-Instruct.Q4_K_S.gguf	Q4_K_S	40.88GB
Dracarys2-72B-Instruct.Q4_K.gguf	Q4_K	44.16GB
Dracarys2-72B-Instruct.Q4_K_M.gguf	Q4_K_M	44.16GB
Dracarys2-72B-Instruct.Q4_1.gguf	Q4_1	42.56GB
Dracarys2-72B-Instruct.Q5_0.gguf	Q5_0	46.72GB
Dracarys2-72B-Instruct.Q5_K_S.gguf	Q5_K_S	47.85GB
Dracarys2-72B-Instruct.Q5_K.gguf	Q5_K	50.71GB
Dracarys2-72B-Instruct.Q5_K_M.gguf	Q5_K_M	50.71GB
Dracarys2-72B-Instruct.Q5_1.gguf	Q5_1	34.61GB
Dracarys2-72B-Instruct.Q6_K.gguf	Q6_K	59.93GB
Dracarys2-72B-Instruct.Q8_0.gguf	Q8_0	71.96GB

Original model description:

language: - en license: other tags: - chat license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE pipeline_tag: text-generation

Dracarys2-72B-Instruct

Introduction

We introduce the latest in the Smaug series, the Dracarys family of finetunes targeting coding performance improvements across a variety of base models.

This variant is a finetune of Qwen2.5-72B-Instruct

Compared to Qwen2.5-72B-Instruct, Dracarys has better LiveCodeBench scores (see evaluation results below).

Model Description

Developed by: Abacus.AI
License: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
Finetuned from model: Qwen2.5-72B-Instruct.

How to use

The prompt format is unchanged from Qwen2.5-72B-Instruct (see evaluations for prompt details for LCB)

Use with transformers

See the snippet below for usage with Transformers:

import transformers
import torch

model_id = "abacusai/Dracarys2-72B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are data science coding assistant that generates Python code using Pandas and Numpy."},
    {"role": "user", "content": "Write code to select rows from the dataframe `df` having the maximum `temp` for each `city`"},
]

prompt = pipeline.tokenizer.apply_chat_template(
        messages, 
        tokenize=False, 
        add_generation_prompt=True
)

terminators = [
    pipeline.tokenizer.eos_token_id,
    pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = pipeline(
    prompt,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])

Evaluation Results

LiveCodeBench

Model	Code Generation	Code Execution (COT)	Test Output Prediction
Dracarys2-72B-Instruct	53.80	89.12	59.61
Qwen2.5-72B-Instruct	53.03	88.72	46.28

Breakdown of LiveCodeBench CodeGeneration

Model	Easy	Medium	Hard
Dracarys2-72B-Instruct	88.79	50.28	9.47
Qwen2.5-72B-Instruct	86.99	49.59	9.99

Breakdown of LiveCodeBench TestOutputPrediction

Model	Easy	Medium	Hard
Dracarys2-72B-Instruct	79.25	53.76	37.63
Qwen2.5-72B-Instruct	68.43	39.46	22.22

Downloads last month: 1,561

GGUF

Model size

73B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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