Instructions to use inflatebot/G2-9B-Blackout-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inflatebot/G2-9B-Blackout-R1 with Transformers:

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

pipe = pipeline("text-generation", model="inflatebot/G2-9B-Blackout-R1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inflatebot/G2-9B-Blackout-R1")
model = AutoModelForCausalLM.from_pretrained("inflatebot/G2-9B-Blackout-R1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inflatebot/G2-9B-Blackout-R1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inflatebot/G2-9B-Blackout-R1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inflatebot/G2-9B-Blackout-R1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/inflatebot/G2-9B-Blackout-R1

SGLang

How to use inflatebot/G2-9B-Blackout-R1 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 "inflatebot/G2-9B-Blackout-R1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inflatebot/G2-9B-Blackout-R1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "inflatebot/G2-9B-Blackout-R1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inflatebot/G2-9B-Blackout-R1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use inflatebot/G2-9B-Blackout-R1 with Docker Model Runner:
```
docker model run hf.co/inflatebot/G2-9B-Blackout-R1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A lot of punch in a little package.

GGUFs available courtesy of mradermacher

merge

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

Merge Details

A simple task-arithmetic merge of Magnum-v3-9B with just a pinch of Sunfall, to loosen it up a little bit. Does the horny real good, but also has a depth of character that Magnum lacked. Uses ChatML formatting, which in and of itself is a massive upgrade to Gemma2. (Who ships a model without a system prompt in 2024? Come on, Google.)

Merge Method

This model was merged using the task arithmetic merge method using IntervitensInc/gemma-2-9b-chatml as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
 - model: anthracite-org/magnum-v3-9b-chatml
   parameters:
     weight: 1
 - model: crestf411/gemma2-9B-sunfall-v0.5.2
   parameters:
     weight: 0.3
merge_method: task_arithmetic
base_model: IntervitensInc/gemma-2-9b-chatml
dtype: float32
tokenizer_source: base
parameters:
 normalize: true

.300 AAC Blackout is an intermediate cartridge designed for use in the M4 Carbine, packing a significantly larger projectile into a cartridge compatible with 5.56mm NATO magazines, only requiring a barrel change.