Instructions to use SEE7/Astheral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SEE7/Astheral with Transformers:

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

pipe = pipeline("text-generation", model="SEE7/Astheral")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SEE7/Astheral")
model = AutoModelForCausalLM.from_pretrained("SEE7/Astheral", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SEE7/Astheral with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SEE7/Astheral"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SEE7/Astheral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SEE7/Astheral

SGLang

How to use SEE7/Astheral 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 "SEE7/Astheral" \
    --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": "SEE7/Astheral",
		"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 "SEE7/Astheral" \
        --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": "SEE7/Astheral",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SEE7/Astheral with Docker Model Runner:
```
docker model run hf.co/SEE7/Astheral
```

Astheral / README.md

SEE7

Remove generic disclaimers; retain model facts and measured-result links

499fcb2 verified about 19 hours ago

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---
language:
- en
license: llama3.1
library_name: transformers
pipeline_tag: text-generation
base_model: NousResearch/DeepHermes-3-Llama-3-8B-Preview
tags:
- astheral
- llama
- instruct
- qlora
---
# Astheral V3

Astheral is SEE7's 8.03B instruction model, built with Llama and fine-tuned from DeepHermes-3-Llama-3-8B-Preview.

[Chat](https://huggingface.co/spaces/SEE7/Astheral)

## Training

Merged BF16 weights from the V3 rank-16 QLoRA run: 41,943,040 adapter parameters, 1,713 training examples and 180 optimizer steps. Instruct and thinking modes are supported.

## Use

Load the merged checkpoint with Transformers in BF16, or use the hosted Space. The Space supports project instructions and up to 4,096 generated tokens.

## Reports and files

[Training and evaluation](https://huggingface.co/SEE7/Astheral/tree/main/v3-results) · [Extended experiments](https://huggingface.co/SEE7/Astheral/tree/v3-expanded-20261113)

Llama 3.1 licensing applies. See LICENSE, UPSTREAM_LLAMA3_LICENSE, NOTICE and the data attribution files.

	---
	language:
	- en
	license: llama3.1
	library_name: transformers
	pipeline_tag: text-generation
	base_model: NousResearch/DeepHermes-3-Llama-3-8B-Preview
	tags:
	- astheral
	- llama
	- instruct
	- qlora
	---
	# Astheral V3

	Astheral is SEE7's 8.03B instruction model, built with Llama and fine-tuned from DeepHermes-3-Llama-3-8B-Preview.

	[Chat](https://huggingface.co/spaces/SEE7/Astheral)

	## Training

	Merged BF16 weights from the V3 rank-16 QLoRA run: 41,943,040 adapter parameters, 1,713 training examples and 180 optimizer steps. Instruct and thinking modes are supported.

	## Use

	Load the merged checkpoint with Transformers in BF16, or use the hosted Space. The Space supports project instructions and up to 4,096 generated tokens.

	## Reports and files

	[Training and evaluation](https://huggingface.co/SEE7/Astheral/tree/main/v3-results) · [Extended experiments](https://huggingface.co/SEE7/Astheral/tree/v3-expanded-20261113)

	Llama 3.1 licensing applies. See LICENSE, UPSTREAM_LLAMA3_LICENSE, NOTICE and the data attribution files.