Instructions to use tssst/Aster-G2-9B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tssst/Aster-G2-9B-v1 with Transformers:

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

pipe = pipeline("text-generation", model="tssst/Aster-G2-9B-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tssst/Aster-G2-9B-v1")
model = AutoModelForCausalLM.from_pretrained("tssst/Aster-G2-9B-v1", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tssst/Aster-G2-9B-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tssst/Aster-G2-9B-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tssst/Aster-G2-9B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tssst/Aster-G2-9B-v1

SGLang

How to use tssst/Aster-G2-9B-v1 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 "tssst/Aster-G2-9B-v1" \
    --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": "tssst/Aster-G2-9B-v1",
		"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 "tssst/Aster-G2-9B-v1" \
        --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": "tssst/Aster-G2-9B-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tssst/Aster-G2-9B-v1 with Docker Model Runner:
```
docker model run hf.co/tssst/Aster-G2-9B-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP method to create an intermediate model. I used the Model Stock merge method after, using the SLERP model as a base.

The idea was to make a nice and smart base model and add in a few pinches of spice.

For some reason it wouldn't let me use any other merge method- it gave me ModelReference errors about my intermediary model for every method except Model Stock for some reason. I'll see if I can fix it and upload my intended task-arithmetic version as a v2.

This is the only one of my like 700 merges that I think uses something novel/interesting enough in its creation to merit an upload.

Named after the aster, a purple-violet star-shaped perennial flower. It's pretty and has a huge family, much like this model.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# THIS YAML CONFIGURATION WAS USED TO CREATE THE INTERMEDIARY MODEL.
# slices:
#   - sources:
#     - model: anthracite-org/magnum-v3-9b-customgemma2
#       layer_range: [0, 42]
#     - model: nbeerbower/gemma2-gutenberg-9B
#       layer_range: [0, 42]
# merge_method: slerp
# base_model: nbeerbower/gemma2-gutenberg-9B
# parameters:
#   t:
#     - filter: self_attn
#       value: [0.2, 0.5, 0.4, 0.7, 1]
#     - filter: mlp
#       value: [1, 0.5, 0.3, 0.4, 0.2]
#     - value: 0.5
# dtype: float16

# THIS YAML CONFIGURATION WAS USED TO CREATE ASTER. The E: model is the intermediate
# model created in the previous config.
models:
  - model: E:/models/mergekit/output/intermediate/
  - model: BeaverLegacy/Smegmma-Deluxe-9B-v1
    parameters:
      weight: 0.3
  - model: ifable/gemma-2-Ifable-9B
    parameters:
      weight: 0.3
  - model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
    parameters:
      weight: 0.15
  - model: grimjim/Magnolia-v1-Gemma2-8k-9B
    parameters:
      weight: 0.25
merge_method: model_stock
base_model: E:/models/mergekit/output/intermediate/
dtype: float16