Fix model card: correct title, document prompt format, add pipeline_tag/base_model metadata

README.md

@@ -1,58 +1,84 @@
 ---
 license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
 datasets:
 - HuggingFaceFW/fineweb-edu
 - mlfoundations/dclm-baseline-1.0-parquet
-language:
-- en
 tags:
 - boris
 - nmai
+- gpt2
 - 250M
 ---
 
 ![Boris](Boris-250M.png)
 
 # Boris-250M
-Boris-250M is a Large Language Model created by New Millennium Artificial Intelligence, trained on 5.01B tokens and contains 250 million parameters.
 
-Trained for ~105:56:17 on one RTX 3060.
+Boris-250M is a 250 million-parameter language model created by New Millennium
+Artificial Intelligence (NMAI).
 
-# Final Stats:
-### Loss - 3.0693
-### Grad Norm - 0.225
-### Learning Rate - 6.00e-05
+This is a **base (pretrained) model**. It has not been instruction-tuned and does
+not follow instructions or hold a conversation — it continues text. For an
+instruction-following version, see
+[KSP-NMAI/Boris-250M-Instruct](https://huggingface.co/KSP-NMAI/Boris-250M-Instruct).
 
+## Usage
 
-![Benchmarks](benchmarks.png)
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("KSP-NMAI/Boris-250M")
+model = AutoModelForCausalLM.from_pretrained("KSP-NMAI/Boris-250M")
 
+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))
+```
 
 ## Details
 
 | | |
 |---|---|
 | Architecture | GPT-2 (pre-LN, learned positional embeddings, tied embeddings) |
-| Parameters | ~250.4M |
 | Layers / heads / d_model | 12 / 18 / 1152 |
 | Context length | 1024 |
-| Training tokens | 5.01B (20.0 tokens/param, Chinchilla-optimal) |
-| Training data | 60% FineWeb-Edu / 40% DCLM |
 | Vocab | 50304 (GPT-NeoX-20B BPE, padded) |
 | Tokenizer | `EleutherAI/gpt-neox-20b` |
 | Precision | trained in bf16 autocast with fp32 master weights |
 
-Base model; not instruction-tuned or aligned. Outputs may be inaccurate or
-offensive.
+## Base model training
 
-## Copyright & License:
+Trained on 5.01B tokens for ~105:56:17 on one RTX 3060.
+
+| | |
+|---|---|
+| Final loss | 3.0693 |
+| Final grad norm | 0.225 |
+| Final learning rate | 6.00e-05 |
+
+![Benchmarks](benchmarks.png)
+
+## Limitations
+
+A base model of this size will produce text that is frequently inaccurate,
+inconsistent, or offensive. It has received no alignment or safety tuning and
+should not be used for factual reference or deployed without supervision.
+
+## Copyright & License
 
 *Copyright 2026 Joseph Jones*
 
-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:
+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.
+
+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.