Instructions to use inflatebot/guns-and-roses-r1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inflatebot/guns-and-roses-r1 with Transformers:

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

pipe = pipeline("text-generation", model="inflatebot/guns-and-roses-r1")
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("inflatebot/guns-and-roses-r1")
model = AutoModelForCausalLM.from_pretrained("inflatebot/guns-and-roses-r1", 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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inflatebot/guns-and-roses-r1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inflatebot/guns-and-roses-r1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inflatebot/guns-and-roses-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inflatebot/guns-and-roses-r1

SGLang

How to use inflatebot/guns-and-roses-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/guns-and-roses-r1" \
    --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": "inflatebot/guns-and-roses-r1",
		"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 "inflatebot/guns-and-roses-r1" \
        --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": "inflatebot/guns-and-roses-r1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inflatebot/guns-and-roses-r1 with Docker Model Runner:
```
docker model run hf.co/inflatebot/guns-and-roses-r1
```
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

Quickest draw in the West.

NOTE: If you are getting phrase repetition or nonsense outputs with SillyTavern, make sure that "Include names" is disabled under Advanced Formatting. Nemo models tend to exhibit these issues when this is enabled.

NOTE 2 (for newer users): "r1" here means "Revision 1". This model predates DeepSeek's R1; DeepSeek inadvertently made using this versioning scheme very annoying!

A re-application of the Helium-3 process to Mistral Nemo analogues. Experimental (as you can tell by it having a revision number, I'll be playing with this more in coming time.) Based ultimately on Magnum-12B-V2 and MN-12B-Rosier-v1.

Quants available from Reiterate3680

Special thanks to Fizz and Toasty Pigeon.

Use ChatML formatting, as Rosier was trained from base (so no instruct format) and Magnum V2 was trained on ChatML!

Merge Method

This model was merged using the Model Stock merge method using inflatebot/thorn-0.35 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: inflatebot/thorn-0.5
  - model: inflatebot/thorn-0.45
  - model: inflatebot/thorn-0.55

merge_method: model_stock
base_model: inflatebot/thorn-0.35
dtype: bfloat16