Instructions to use Entropicengine/Pinecone-Rune-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Entropicengine/Pinecone-Rune-12b with Transformers:

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

pipe = pipeline("text-generation", model="Entropicengine/Pinecone-Rune-12b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Entropicengine/Pinecone-Rune-12b")
model = AutoModelForCausalLM.from_pretrained("Entropicengine/Pinecone-Rune-12b")
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 Entropicengine/Pinecone-Rune-12b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Entropicengine/Pinecone-Rune-12b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Entropicengine/Pinecone-Rune-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Entropicengine/Pinecone-Rune-12b

SGLang

How to use Entropicengine/Pinecone-Rune-12b 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 "Entropicengine/Pinecone-Rune-12b" \
    --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": "Entropicengine/Pinecone-Rune-12b",
		"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 "Entropicengine/Pinecone-Rune-12b" \
        --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": "Entropicengine/Pinecone-Rune-12b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Entropicengine/Pinecone-Rune-12b with Docker Model Runner:
```
docker model run hf.co/Entropicengine/Pinecone-Rune-12b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Pinecone-Rune-12B

🌲Pinecone Series

The Pinecone Series is a collection of thoughtfully crafted model merges, combining the strengths of the best models among my personal favourites. Each version is curated to excel in roleplay, general knowledge, intelligence, and rich creative writing, while preserving the unique capabilities of its underlying models.

Version	Params	Strengths
Pinecone-Rune	12B	Fast, lightweight, surprisingly capable for its size
Pinecone-Sage	24B	Balanced speed and performance, rich prose and RP
Pinecone-Titan	70B	Rich prose, better long context capabilities, top-tier roleplay & knowledge

☕ Support My Work

If you like my work, consider buying me a coffee to support future merges, GPU time, and experiments.

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

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using DreadPoor/Irix-12B-Model_Stock as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: DreadPoor/Irix-12B-Model_Stock
chat_template: auto
merge_method: dare_ties
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 40]
        model: DreadPoor/Irix-12B-Model_Stock
        parameters:
          weight: 0.6
      - layer_range: [0, 40]
        model: yamatazen/LorablatedStock-12B
        parameters:
          weight: 0.25
      - layer_range: [0, 40]
        model: inflatebot/MN-12B-Mag-Mell-R1
        parameters:
          weight: 0.15
out_dtype: bfloat16
parameters:
  density: 1.0
tokenizer: {}