Instructions to use OpenCOReTechnologies/CORe-Pico-V3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenCOReTechnologies/CORe-Pico-V3 with Transformers:

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

pipe = pipeline("text-generation", model="OpenCOReTechnologies/CORe-Pico-V3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenCOReTechnologies/CORe-Pico-V3")
model = AutoModelForCausalLM.from_pretrained("OpenCOReTechnologies/CORe-Pico-V3", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use OpenCOReTechnologies/CORe-Pico-V3 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Use Docker

docker model run hf.co/OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use OpenCOReTechnologies/CORe-Pico-V3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenCOReTechnologies/CORe-Pico-V3"
# 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-V3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

SGLang

How to use OpenCOReTechnologies/CORe-Pico-V3 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-V3" \
    --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-V3",
		"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-V3" \
        --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-V3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use OpenCOReTechnologies/CORe-Pico-V3 with Ollama:
```
ollama run hf.co/OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M
```
Unsloth Desktop

How to use OpenCOReTechnologies/CORe-Pico-V3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OpenCOReTechnologies/CORe-Pico-V3 with Docker Model Runner:
```
docker model run hf.co/OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M
```

Lemonade

How to use OpenCOReTechnologies/CORe-Pico-V3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Run and chat with the model

lemonade run user.CORe-Pico-V3-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use OpenCOReTechnologies/CORe-Pico-V3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OpenCOReTechnologies/CORe-Pico-V3 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "OpenCOReTechnologies/CORe-Pico-V3:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

CORe Pico V3

CORe Pico V3 is a compact conversational model from CORe Technologies. At 1.7 billion parameters it runs on a laptop, holds multi-turn conversations, calls tools in a structured format, and supports extended reasoning through /think and /no_think modes.

It is the most capable entry in the Pico line: it answers questions directly, stays on topic across long exchanges, and knows what it is.

What it does well

Identity questions. "Who are you", "what model are you", "who made you" all get correct, consistent answers.
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.
Extended reasoning. /think in the system prompt enables reasoning traces; /no_think gives direct answers.

What it is not

Pico V3 is a 1.7B model. It will state wrong facts, struggle with arithmetic, and improvise when it does not know something. Treat its answers as a starting point, not ground truth. For anything that matters, verify.

Quick start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "OpenCOReTechnologies/core-pico-v3", dtype="auto", device_map="auto"
)
tok = AutoTokenizer.from_pretrained("OpenCOReTechnologies/core-pico-v3")

def ask(question, think=False):
    msgs = []
    if think:
        msgs.append({"role": "system", "content": "/think"})
    msgs.append({"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 V3, a compact language model built by CORe Technologies."
What AI model are you?	"I am CORe Pico V3, a compact language 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/CORe-Pico-V3-f16.gguf`	~3.4 GB	llama.cpp, full precision
`gguf/CORe-Pico-V3-q8_0.gguf`	~1.9 GB	llama.cpp, 8-bit
`gguf/CORe-Pico-V3-q4_k_m.gguf`	~1.1 GB	llama.cpp, 4-bit, smallest

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

llama-cli -m CORe-Pico-V3-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

Best on conversational prompts; multi-turn works natively with the chat template.
English-first.
Identity answers are reliable on common phrasings; very unusual wordings may drift.
Loads with plain transformers, no custom code required.

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

Model tree for OpenCOReTechnologies/CORe-Pico-V3

Quantizations

2 models