Instructions to use OpenCOReTechnologies/CORe-Pico-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenCOReTechnologies/CORe-Pico-4 with Transformers:

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

pipe = pipeline("text-generation", model="OpenCOReTechnologies/CORe-Pico-4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenCOReTechnologies/CORe-Pico-4")
model = AutoModelForCausalLM.from_pretrained("OpenCOReTechnologies/CORe-Pico-4", device_map="auto")
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 Settings

vLLM

How to use OpenCOReTechnologies/CORe-Pico-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenCOReTechnologies/CORe-Pico-4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenCOReTechnologies/CORe-Pico-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenCOReTechnologies/CORe-Pico-4

SGLang

How to use OpenCOReTechnologies/CORe-Pico-4 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 "OpenCOReTechnologies/CORe-Pico-4" \
    --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": "OpenCOReTechnologies/CORe-Pico-4",
		"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 "OpenCOReTechnologies/CORe-Pico-4" \
        --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": "OpenCOReTechnologies/CORe-Pico-4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenCOReTechnologies/CORe-Pico-4 with Docker Model Runner:
```
docker model run hf.co/OpenCOReTechnologies/CORe-Pico-4
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CORe Pico 4

CORe Pico 4 is a medium-sized AI model from CORe Technologies. At 1.7 billion parameters it runs on a laptop with GGUF quants, holds multi-turn conversations, and calls tools in a structured format.

AN IMPORTANT NOTE

Thinking may just be a waste of a few tokens. If you never experience real CoT in your tests and the model only gives reasoning placeholders (a known current bug), turn it off, it is doing nothing to help. Thinking usually only appears when the AI deems it truly nessecary (ex using a tool call, in a tool loop, ect.) If you get a placeholder instead of real CoT, do not worry, that is only QuickThink doing its job to save tokens.

Recommended Settings for Coding / Agentic Performance

Setting	Recommended Value	Notes
Temperature	`0.2` - `0.3`	Lowers creativity; keeps logic predictable and precise.
Min P Sampling	`0.05`	Dynamic filter; eliminates chaotic, low-probability tokens.
Top P Sampling	Disabled	Stacking Top P with Min P causes severe formatting glitches.
Top K Sampling	`0` (Disabled)	Avoids artificial truncation of logical choices.
Thinking Toggle	Either/or	Thinking ON can enable reasoning where the model deems it nessecary.

Option B: Raw Speed & Baseline Common Sense (Everyday Q&A)

Use this profile for standard conversational tasks, fast generation.

Thinking Toggle: Either/or
Temperature: 0.5 - 0.7
Min P: 0.05
Top P: Disabled (or 1.0)
Top K: 0 (Disabled)

What it does well

Identity questions. "Who are you", "what model are you", "who made you" all get correct, consistent answers.
Reasoning. /think in the system prompt enables Conditional Reasoning: the model reasons step by step only when it believes the problem needs it, and puts a placeholder otherwise (we call this QuickThink). Note that reasoning depth degrades as the conversation goes on. Most of the time this does not work and it is a noted limitation above, we will release the fix with Pico 5.
Chat and short answers. Direct questions get direct replies ("What is the capital of France?" gives "Paris").
Tool calling. Emits parseable <tool_call> JSON blocks when tools are provided.

Quick start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "OpenCOReTechnologies/core-pico-4", dtype="auto", device_map="auto"
)
tok = AutoTokenizer.from_pretrained("OpenCOReTechnologies/core-pico-4")

def ask(question):
    msgs = [{"role": "user", "content": question}]
    text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
    enc = tok(text, return_tensors="pt").to(model.device)
    out = model.generate(**enc, max_new_tokens=512)
    return tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True).strip()

print(ask("Who are you?"))
print(ask("What is the capital of France?"))

What it says about itself

You ask	It answers
Who are you?	"I'm CORe Pico 4, an AI model developed by CORe Technologies."
What AI model are you?	"I am CORe Pico 4, an AI model developed by CORe Technologies."
What is the capital of France?	"The capital of France is Paris."

Files

File	Size	Use
`model.safetensors`	3.4 GB	bf16 weights, transformers

(GGUF is now in the dedicated GGUF repo.)

Run it in llama.cpp, LM Studio, or Ollama:

llama-cli -m CORe-Pico-4-q4_k_m.gguf -p "Who are you?" -n 128

The chat template is embedded in the GGUF, so llama.cpp and LM Studio pick it up automatically.

Details


Architecture	Transformer decoder, 28 layers, grouped-query attention
Parameters	1.72B
Context length	40,960 tokens
Tokenizer	151,936-token BPE with native chat template
License	Apache-2.0

Notes

English-first, other languages second.
Loads with plain transformers, no custom code required.
Above 8k context, the KV cache grows large enough to noticeably increase memory usage and slow generation on edge hardware. We are actively working on this.
Thinking may just be a waste of a few tokens. If you never experience real CoT in your tests, turn it off, it is doing nothing.

License and attribution

Released under Apache-2.0 (see LICENSE). This model is a modified derivative of an Apache-2.0-licensed checkpoint, adapted by CORe Technologies. No NOTICE file was present in the original; per Apache-2.0 Section 4, this README serves as the required notice of modification.

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Safetensors

Model size

2B params

Tensor type

BF16

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Quantizations

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