Usage

Transformers — base model

Base models continue text. They do not follow instructions.

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("opencerebral/Boris-1.3-125M")
model = AutoModelForCausalLM.from_pretrained("opencerebral/Boris-1.3-125M")

ids = tok("The ocean is", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=40, do_sample=True, top_p=0.95)
print(tok.decode(out[0], skip_special_tokens=True))

Transformers — instruct model

Instruct models ship a chat template, so apply_chat_template() works directly.

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("opencerebral/Boris-1.3-125M-Instruct")
model = AutoModelForCausalLM.from_pretrained("opencerebral/Boris-1.3-125M-Instruct")

messages = [{"role": "user", "content": "What's a good way to start learning C?"}]
ids = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
out = model.generate(ids, max_new_tokens=120, do_sample=True, top_p=0.95)
print(tok.decode(out[0][ids.shape[-1]:], skip_special_tokens=True))

llama.cpp — GGUF

The GGUF builds run on CPU with no Python involved.

# interactive chat, pulled straight from Hugging Face
llama-cli -hf opencerebral/Boris-1.3-125M-Instruct-GGUF -cnv

# or an OpenAI-compatible local server
llama-server -hf opencerebral/Boris-1.3-125M-Instruct-GGUF --port 8080

See GGUF Downloads for the available quantizations.

Sampling notes

Small models degrade quickly with greedy decoding — they fall into repetition loops. Sample. top_p=0.95 with a temperature near 0.8 is a reasonable starting point. Keep prompts short: the context window is 1024 tokens for Boris and 512 for littlerock, and quality drops well before the limit.

Before you deploy anything

These models have received no alignment or safety tuning and will produce inaccurate, inconsistent, or offensive text. Do not use them as a factual reference or run them unsupervised in front of users.