Instructions to use Phr00t/Phr00tyMix-v1-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phr00t/Phr00tyMix-v1-32B with Transformers:

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

pipe = pipeline("text-generation", model="Phr00t/Phr00tyMix-v1-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Phr00t/Phr00tyMix-v1-32B")
model = AutoModelForMultimodalLM.from_pretrained("Phr00t/Phr00tyMix-v1-32B")
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 Phr00t/Phr00tyMix-v1-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Phr00t/Phr00tyMix-v1-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Phr00t/Phr00tyMix-v1-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Phr00t/Phr00tyMix-v1-32B

SGLang

How to use Phr00t/Phr00tyMix-v1-32B 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 "Phr00t/Phr00tyMix-v1-32B" \
    --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": "Phr00t/Phr00tyMix-v1-32B",
		"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 "Phr00t/Phr00tyMix-v1-32B" \
        --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": "Phr00t/Phr00tyMix-v1-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Phr00t/Phr00tyMix-v1-32B with Docker Model Runner:
```
docker model run hf.co/Phr00t/Phr00tyMix-v1-32B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Phr00tyMix-v1-32B

Note: this model has been superseded by Phr00tyMix-v2

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

The goal is to be smart, obedient, creative and coherent. This isn't 100% censored, but some simple prompting to disallow refusals seems to do the trick.

GGUFs can be found here

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using rombodawg/Rombos-LLM-V2.5-Qwen-32b as a base.

This base model was chosen as a smart, non-thinking foundation.

Models Merged

The following models were included in the merge:

Delta-Vector/Hamanasu-QwQ-V1.5-Instruct (non-thinking QwQ instruction finetune)
allura-org/Qwen2.5-32b-RP-Ink (spicy color and prose)
Delta-Vector/Hamanasu-Magnum-QwQ-32B (non-thinking QwQ creative finetune)
THU-KEG/LongWriter-Zero-32B (coherency for longer writing)
zetasepic/Qwen2.5-32B-Instruct-abliterated-v2 (reduced refusals)

Configuration

The following YAML configuration was used to produce this model:

merge_method: dare_ties
dtype: bfloat16
base_model: rombodawg/Rombos-LLM-V2.5-Qwen-32b
parameters:
  normalize_weights: true
models:
  - model: Delta-Vector/Hamanasu-QwQ-V1.5-Instruct
    parameters:
      weight: 0.3
      density: 1
  - model: zetasepic/Qwen2.5-32B-Instruct-abliterated-v2
    parameters:
      weight: 0.1
      density: 0.8
  - model: THU-KEG/LongWriter-Zero-32B
    parameters:
      weight: 0.1
      density: 0.8
  - model: Delta-Vector/Hamanasu-Magnum-QwQ-32B
    parameters:
      weight: 0.3
      density: 0.8
  - model: allura-org/Qwen2.5-32b-RP-Ink
    parameters:
      weight: 0.2
      density: 0.5