Instructions to use Phr00t/Phr00tyMix-v4-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Phr00t/Phr00tyMix-v4-32B with Transformers:

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

pipe = pipeline("text-generation", model="Phr00t/Phr00tyMix-v4-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Phr00t/Phr00tyMix-v4-32B")
model = AutoModelForCausalLM.from_pretrained("Phr00t/Phr00tyMix-v4-32B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Phr00t/Phr00tyMix-v4-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Phr00t/Phr00tyMix-v4-32B"
# 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-v4-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Phr00t/Phr00tyMix-v4-32B

SGLang

How to use Phr00t/Phr00tyMix-v4-32B 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-v4-32B" \
    --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-v4-32B",
		"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-v4-32B" \
        --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-v4-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Phr00t/Phr00tyMix-v4-32B with Docker Model Runner:
```
docker model run hf.co/Phr00t/Phr00tyMix-v4-32B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Phr00tyMix-v4-32B

Phr00tyMix-v3 did increase creativity, but at the expense of some of its instruction following and coherency. This mix is intended to fix that, which should improve its storytelling and obediency. This model is still very creative, uncensored (when asked to be) and smart.

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

Merge Details

Merge Method

This model was merged using the Model Stock merge method using Phr00t/Phr00tyMix-v3-32B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: model_stock
base_model: Phr00t/Phr00tyMix-v3-32B
dtype: bfloat16
models:
  - model: Delta-Vector/Archaeo-32B-KTO
  - model: allura-org/Qwen2.5-32b-RP-Ink
  - model: arcee-ai/Virtuoso-Medium-v2
  - model: Phr00t/Phr00tyMix-v2-32B
  - model: nicoboss/DeepSeek-R1-Distill-Qwen-32B-Uncensored
tokenizer:
  source: "Delta-Vector/Archaeo-32B-KTO"