docs: mark superseded and point at the canonical repo

README.md

@@ -1,62 +1,65 @@
----
-base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
-library_name: peft
-model_name: scbe-coding-approval-metrics-qwen-kaggle-v1
-tags:
-- base_model:adapter:Qwen/Qwen2.5-Coder-0.5B-Instruct
-- lora
-- sft
-- transformers
-- trl
-licence: license
-pipeline_tag: text-generation
----
-
-# Model Card for scbe-coding-approval-metrics-qwen-kaggle-v1
-
-This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct).
-It has been trained using [TRL](https://github.com/huggingface/trl).
-
-## Quick start
-
-```python
-from transformers import pipeline
-
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-
-## Training procedure
-
- 
-
-
-
-This model was trained with SFT.
-
-### Framework versions
-
-- PEFT 0.18.1
-- TRL: 1.2.0
-- Transformers: 5.0.0
-- Pytorch: 2.10.0+cu128
-- Datasets: 4.8.3
-- Tokenizers: 0.22.2
-
-## Citations
-
-
-
-Cite TRL as:
-    
-```bibtex
-@software{vonwerra2020trl,
-  title   = {{TRL: Transformers Reinforcement Learning}},
-  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
-  license = {Apache-2.0},
-  url     = {https://github.com/huggingface/trl},
-  year    = {2020}
-}
+---
+base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
+library_name: peft
+model_name: scbe-coding-approval-metrics-qwen-kaggle-v1
+tags:
+- base_model:adapter:Qwen/Qwen2.5-Coder-0.5B-Instruct
+- lora
+- sft
+- transformers
+- trl
+- superseded
+licence: license
+pipeline_tag: text-generation
+---
+<!-- scbe-status -->
+> **Status: superseded.** Canonical coding model: [scbe-coding-agent-vtc-qwen15-v1-gguf](https://hf.co/issdandavis/scbe-coding-agent-vtc-qwen15-v1-gguf). Kept as research history.
+
+# Model Card for scbe-coding-approval-metrics-qwen-kaggle-v1
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+ 
+
+
+
+This model was trained with SFT.
+
+### Framework versions
+
+- PEFT 0.18.1
+- TRL: 1.2.0
+- Transformers: 5.0.0
+- Pytorch: 2.10.0+cu128
+- Datasets: 4.8.3
+- Tokenizers: 0.22.2
+
+## Citations
+
+
+
+Cite TRL as:
+    
+```bibtex
+@software{vonwerra2020trl,
+  title   = {{TRL: Transformers Reinforcement Learning}},
+  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
+  license = {Apache-2.0},
+  url     = {https://github.com/huggingface/trl},
+  year    = {2020}
+}
 ```