Update README.md

README.md

@@ -1,6 +1,7 @@
 ---
 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:
@@ -12,7 +13,7 @@ licence: license
 
 # 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/dermatology-qa-firecrawl-dataset) dataset.
 It has been trained using [TRL](https://github.com/huggingface/trl).
 
 ## Quick start
@@ -20,18 +21,31 @@ 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]
+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
+)
+
+
+# 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"])
+
+# The source says that clinicians should use a combination of clinical signs and symptoms when diagnosing rosacea, rather than relying on a single feature.
 ```
 
 ## Training procedure
 
- 
-
-
-This model was trained with SFT.
+ This model was trained with SFT.
 
 ### Framework versions