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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Pinecone-Rune-12b / README.md

Entropicengine

Update README.md

83f3de0 verified about 1 year ago

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	---
	base_model:
	- inflatebot/MN-12B-Mag-Mell-R1
	- DreadPoor/Irix-12B-Model_Stock
	- yamatazen/LorablatedStock-12B
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# Pinecone-Rune-12B

	![image/png](https://huggingface.co/Entropicengine/Pinecone-Rune-12b/resolve/main/pinecone-rune-1.png)

	# 🌲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](https://ko-fi.com/entropicengine) to support future merges, GPU time, and experiments.

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [DreadPoor/Irix-12B-Model_Stock](https://huggingface.co/DreadPoor/Irix-12B-Model_Stock) as a base.

	### Models Merged

	The following models were included in the merge:
	* [inflatebot/MN-12B-Mag-Mell-R1](https://huggingface.co/inflatebot/MN-12B-Mag-Mell-R1)
	* [yamatazen/LorablatedStock-12B](https://huggingface.co/yamatazen/LorablatedStock-12B)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	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: {}
	```