Boris-75M-Instruct

Boris-75M-Instruct is the instruction-tuned variant of KSP-NMAI/Boris-75M, a 75 million-parameter language model created by New Millennium Artificial Intelligence (NMAI). It was fine-tuned on tatsu-lab/alpaca.

Prompt format

This model uses the Alpaca format. A chat template is included in tokenizer_config.json, so apply_chat_template produces the correct prompt automatically:

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("KSP-NMAI/Boris-75M-Instruct")
model = AutoModelForCausalLM.from_pretrained("KSP-NMAI/Boris-75M-Instruct")

messages = [{"role": "user", "content": "What is the capital of France?"}]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

ids = tok(prompt, return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=64, do_sample=True, top_p=0.95, temperature=0.7)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

If you are building the prompt by hand, the layout is:

### Instruction:
{your instruction}

### Response:

Generation should stop at ### Instruction: (or end-of-text, token id 0).

Details


Architecture	GPT-2 (pre-LN, learned positional embeddings, tied embeddings)
Layers / heads / d_model	12 / 9 / 576
Context length	1024
Vocab	50304 (GPT-NeoX-20B BPE, padded)
Tokenizer	`EleutherAI/gpt-neox-20b`
Precision	trained in bf16 autocast with fp32 master weights

Base model training

Trained on 1.55B tokens for 14:49:08 on one RTX 3060.


Final loss	3.6356
Final grad norm	0.328
Final learning rate	6.00e-05

The table above describes the base model's pretraining run; the instruction tuning was applied on top of that checkpoint.

Limitations

This is a very small instruction-tuned model. It will produce text that is frequently inaccurate, inconsistent, or offensive, and it has received no alignment, RLHF, or safety tuning beyond supervised fine-tuning on Alpaca. Do not rely on it for factual information or deploy it without supervision.

Copyright & License

This project and all associated files (the "Work") are licensed under the Apache License, Version 2.0 (the "License"); you may not use this project except in compliance with the License. You may obtain a copy of the License at:

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.