Text Generation
Transformers
Safetensors
English
qwen2
chat
conversational
text-generation-inference
Instructions to use abacusai/Dracarys2-72B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abacusai/Dracarys2-72B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abacusai/Dracarys2-72B-Instruct") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abacusai/Dracarys2-72B-Instruct") model = AutoModelForCausalLM.from_pretrained("abacusai/Dracarys2-72B-Instruct", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use abacusai/Dracarys2-72B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abacusai/Dracarys2-72B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/Dracarys2-72B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/abacusai/Dracarys2-72B-Instruct
- SGLang
How to use abacusai/Dracarys2-72B-Instruct with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "abacusai/Dracarys2-72B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/Dracarys2-72B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "abacusai/Dracarys2-72B-Instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/Dracarys2-72B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use abacusai/Dracarys2-72B-Instruct with Docker Model Runner:
docker model run hf.co/abacusai/Dracarys2-72B-Instruct
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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
library_name: transformers
---
# 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](https://huggingface.co/Qwen/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](https://abacus.ai)
- **License:** https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
- **Finetuned from model:** [Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/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:
```python
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 | |