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license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- causal-lm
- custom-architecture
- core
---
# 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
```python
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.):
```bash
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. |