Instructions to use Entropicengine/Pinecone-sage-24b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Entropicengine/Pinecone-sage-24b with Transformers:

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

pipe = pipeline("text-generation", model="Entropicengine/Pinecone-sage-24b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Entropicengine/Pinecone-sage-24b")
model = AutoModelForCausalLM.from_pretrained("Entropicengine/Pinecone-sage-24b")
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-sage-24b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Entropicengine/Pinecone-sage-24b"
# 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-sage-24b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Entropicengine/Pinecone-sage-24b

SGLang

How to use Entropicengine/Pinecone-sage-24b 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-sage-24b" \
    --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-sage-24b",
		"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-sage-24b" \
        --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-sage-24b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Entropicengine/Pinecone-sage-24b with Docker Model Runner:
```
docker model run hf.co/Entropicengine/Pinecone-sage-24b
```

You know that already but

by SerialKicked - opened Dec 5, 2025

Discussion

SerialKicked

Dec 5, 2025

•

edited Dec 5, 2025

This is probably the best merge of (non CoT) MS models I came across. I'm a bit late on commenting on your model, but it's impressively good. I normally don't evaluate merges unless they've been fine-tuned on top, but this one is too good to pass on.

It keeps most (if not all) of the advantages of MS (good instruction following, task oriented queries), while still good at being a general chat partner. It passed my personal battery of tests (bunch of Q&A + haystack + function calling + menu navigation + general text manipulation). Of course it's not exempt from the usual stylistic issues of MS models, but damn, your merge should definitely be used as a base for further improvements, as it has this kind of good ratio of intelligence and creativity.

I'd be really curious to see what you could make happen with more recent model bases (Hermes 4.3 and Olmo).

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