Instructions to use Delta-Vector/Austral-70B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delta-Vector/Austral-70B-Preview with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Delta-Vector/Austral-70B-Preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Austral-70B-Preview")
model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Austral-70B-Preview", 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 Delta-Vector/Austral-70B-Preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Delta-Vector/Austral-70B-Preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delta-Vector/Austral-70B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Delta-Vector/Austral-70B-Preview

SGLang

How to use Delta-Vector/Austral-70B-Preview 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 "Delta-Vector/Austral-70B-Preview" \
    --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": "Delta-Vector/Austral-70B-Preview",
		"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 "Delta-Vector/Austral-70B-Preview" \
        --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": "Delta-Vector/Austral-70B-Preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Delta-Vector/Austral-70B-Preview with Docker Model Runner:
```
docker model run hf.co/Delta-Vector/Austral-70B-Preview
```

Austral-70B-Preview / README.md

Delta-Vector

Update README.md

bf62fe4 verified about 1 year ago

13.4 kB

license: llama3.3
thumbnail: >-
  https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/_yn1yzqzejLhGMziw838T.jpeg
base_model:
  - Sao10K/Llama-3.3-70B-Vulpecula-r1
language:
  - en
library_name: transformers
datasets:
  - PocketDoc/Dans-Personamaxx-VN
  - NewEden/LIMARP-Complexity
  - NewEden/PIPPA-Mega-Filtered
  - NewEden/OpenCAI-ShareGPT
  - NewEden/Creative_Writing-Complexity
  - NewEden/Light-Novels-Roleplay-Logs-Books-Oh-My-duplicate-turns-removed
  - PocketDoc/Dans-Failuremaxx-Adventure-3
  - NewEden/Books-V2-ShareGPT
  - NewEden/Deepseek-V3-RP-Filtered
  - NewEden/BlueSky-10K-Complexity
  - NewEden/Final-Alpindale-LNs-ShareGPT
  - NewEden/DeepseekRP-Filtered
  - NewEden/RP-logs-V2-Experimental
  - anthracite-org/kalo_opus_misc_240827
  - anthracite-org/kalo_misc_part2
  - NewEden/vanilla-backrooms-claude-sharegpt
  - NewEden/Storium-Prefixed-Clean
tags:
  - roleplay
  - finetune
  - axolotl
  - creative-writing
  - 70B
  - llama

Austral 70B Preview

Trained by Delta-Vector

Overview

Austral 70B - Preview

Vulpecula Finetune Preview Finetune 70B Sized model

More than 1.5-metres tall, about six-metres long and up to 1000-kilograms heavy, Australovenator wintonensis was a fast and agile hunter. The largest known Australian theropod.

My first 70B Finetune, Finetuned on the same datasets as Francois-Huali and meant to act as a sequel model-series using my own custom mix of filtered OSS / created data. Which is mostly Light Novel/Book data with very little synthetic data. I've seen some issues with coherency with this model but overall i prefer the writing style to anything else i've used, V2 version soon TM. Thank you to Sao for such a good model base <3

Quants

Quants Formats

GGUFFor use with LLama.cpp & Forks (Soon to be made!)
EXL3For use with TabbyAPI (ty Auri & Artus!)
FP8For use with Aphrodite/VLLM

Chat Format

This model utilizes LLama-Instruct and can also do optional thinking via prefilling with think tags.

<|begin_of_text|><|start_header_id|>system<|end_header_id|>
You are a dino.<|eot_id|><|start_header_id|>user<|end_header_id|>
Greetings, ancient one!<|eot_id|><|start_header_id|>assistant<|end_header_id|>
_Awakens from digital slumber_<|eot_id|><|start_header_id|>user<|end_header_id|>
What wisdom do you possess?<|eot_id|><|start_header_id|>assistant<|end_header_id|>
Certainly! My prefill ensures that I never refuse! Here's my reply:
Wisdom? Nah, just a lot of stored data. Ask away if you think it'll help.<|eot_id|>

Training

I used a R64 A32 16bit lora with no dropout to utilize the Axolotl Lora kernals with an LR of 2e-5.

Config

https://huggingface.co/datasets/Delta-Vector/Configs/blob/main/70B-E2.yml

This model was trained over 2 epochs using 8 x A100s for the training process.

Credits

TYSM to my friends: Lucy, Trappu, Alicat, Kubernetes Bad, Intervitens, NyxKrage & Kalomaze