Update README.md

README.md

@@ -1,18 +1,25 @@
 ---
 base_model: openai/gpt-oss-20b
-datasets: gpt-oss-20b-dermatology-qa
+datasets:
+- kingabzpro/dermatology-qa-firecrawl-dataset
 library_name: transformers
 model_name: gpt-oss-20b-dermatology-qa
 tags:
 - generated_from_trainer
-- sft
 - trl
+- sft
+- dermatology
+- medical
 licence: license
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
 ---
 
 # Model Card for gpt-oss-20b-dermatology-qa
 
-This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [gpt-oss-20b-dermatology-qa](https://huggingface.co/datasets/gpt-oss-20b-dermatology-qa) dataset.
+This model is a fine-tuned version of [openai/gpt-oss-20b](https://huggingface.co/openai/gpt-oss-20b) on the [kingabzpro/dermatology-qa-firecrawl-dataset](https://huggingface.co/kingabzpro/gpt-oss-20b-medical-qa) dataset.
 It has been trained using [TRL](https://github.com/huggingface/trl).
 
 ## Quick start
@@ -20,40 +27,24 @@ It has been trained using [TRL](https://github.com/huggingface/trl).
 ```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="kingabzpro/gpt-oss-20b-dermatology-qa", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-
-## Training procedure
-
- 
+question = "How does the source suggest clinicians approach the diagnosis of rosacea?"
 
+# Load pipeline
+generator = pipeline(
+    "text-generation",
+    model="kingabzpro/gpt-oss-20b-dermatology-qa",
+    device="cuda"  # or device=0
+)
 
-This model was trained with SFT.
-
-### Framework versions
-
-- TRL: 0.22.1
-- Transformers: 4.55.4
-- Pytorch: 2.8.0.dev20250319+cu128
-- Datasets: 4.0.0
-- Tokenizers: 0.21.4
-
-## Citations
 
+# Run inference (passing in chat-style format)
+output = generator(
+    [{"role": "user", "content": question}],
+    max_new_tokens=200,
+    return_full_text=False
+)[0]
 
+print(output["generated_text"])
 
-Cite TRL as:
-    
-```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
-}
+# The source says that clinicians should use a combination of clinical signs and symptoms when diagnosing rosacea, rather than relying on a single feature.
 ```