CORe-Pico-V3

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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

Files

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

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.