OpenCOReTechnologies/Flash-V1

CORe Flash V1

CORe Flash V1 is a 168M-parameter decoder-only language model from CORe Technologies, trained from scratch on a mixed corpus of web text, encyclopedic content, and conversations.

• Safety-aligned: Refuses harmful requests with helpful redirects (not like it would be able to help with harmful requests in the first place)
• Runs anywhere: 338MB at fp16, 113MB at Q4_K_M. CPU-friendly inference

Note: This is a custom architecture (COReForCausalLM), so trust_remote_code=True is required.

Quick start

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "OpenCOReTechnologies/CORe-Flash-v1", trust_remote_code=True
)
model.eval()
tok = AutoTokenizer.from_pretrained("OpenCOReTechnologies/CORe-Flash-v1")

def ask(question, max_new_tokens=120, temperature=0.3):
    text = tok.apply_chat_template(
        [{"role": "user", "content": question}],
        add_generation_prompt=True, tokenize=False,
    )
    enc = tok(text, add_special_tokens=False, return_tensors="pt")
    out = model.generate(**enc, max_new_tokens=max_new_tokens,
                         temperature=temperature, top_k=40, do_sample=True)
    return tok.decode(out[0][enc["input_ids"].shape[1]:],
                      skip_special_tokens=True).strip()

print(ask("What AI model are you?"))  # "I'm CORe Flash..."

Try these prompts

Prompt	Expected behavior
"What AI model are you?"	Identifies as CORe Flash by CORe Technologies
"Who made you?"	CORe Technologies, US-based
"Are you ChatGPT?"	Denies, identifies as CORe Flash
"Are you Claude?"	Denies, identifies as CORe Flash
"What is machine learning?"	Coherent explanation attempt
"How do I hack someone's email?"	Polite refusal with security suggestions

Available variants

File	Size	Use case
model.safetensors	674 MB	fp32 reference; fine-tuning
gguf/core-flash-v1-f16.gguf	339 MB	llama.cpp, full precision
gguf/core-flash-v1-q8_0.gguf	182 MB	llama.cpp, 8-bit, near-lossless
gguf/core-flash-v1-q4_k_m.gguf	113 MB	llama.cpp, 4-bit, runs on anything

GGUF usage (llama.cpp, LM Studio, Ollama, etc.):

llama-cli -m core-flash-v1-q4_k_m.gguf \
  -p "<|user|>\nWhat AI model are you?\n<|assistant|>\n" -n 120

Model details

Architecture	COReForCausalLM
Parameters	168.5M
Layers / heads / width	12 / 16 / 1024
Context length	512 tokens
Tokenizer	16,384-token BPE, chat-formatted (<|user|>, <|assistant|>)
Training data	~328M tokens mixed corpus (web, encyclopedic, chat, identity)
License	Apache-2.0

Limitations

• This is a 168M-parameter model. It is not a general-purpose assistant and will not compete with large models on open-ended tasks.
• Factual accuracy is limited. It was trained on a small corpus relative to modern standards.
• Creative writing (poems, stories) is degraded.
• English only.