upload v1687507574 model

.ipynb_checkpoints/README-checkpoint.md

@@ -10,4 +10,4 @@ pinned: false
 license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference---

.ipynb_checkpoints/app-checkpoint.py

@@ -8,7 +8,7 @@ from huggingface_hub import Repository
 
 local_path = "hf_model"
 
-model_version = "v1687488717"
+model_version = "v1687507574"
 model_repo_id = "chansung/kerasnlp-gpt2-alpaca-pipeline"
 model_repo_url = f"https://huggingface.co/{model_repo_id}"
 
@@ -23,7 +23,7 @@ _ = _clone_and_checkout(model_repo_url, local_path, model_version)
 model = tf.saved_model.load(local_path, tags=[tag_constants.SERVING])
 gpt_lm_predict_fn = model.signatures["serving_default"]
 
-def gen_text(prompt, max_length=512):
+def gen_text(prompt, max_length=256):
     prompt = tf.constant(f"### Instruction:\n{prompt}\n\n### Response:\n")
     max_length = tf.constant(max_length, dtype="int64")
     

README.md

@@ -11,15 +11,3 @@ license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference---
-title: KerasNLP GPT2 Alpaca
-emoji: 📊
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.35.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -8,7 +8,7 @@ from huggingface_hub import Repository
 
 local_path = "hf_model"
 
-model_version = "v1687488717"
+model_version = "v1687507574"
 model_repo_id = "chansung/kerasnlp-gpt2-alpaca-pipeline"
 model_repo_url = f"https://huggingface.co/{model_repo_id}"
 
@@ -23,7 +23,7 @@ _ = _clone_and_checkout(model_repo_url, local_path, model_version)
 model = tf.saved_model.load(local_path, tags=[tag_constants.SERVING])
 gpt_lm_predict_fn = model.signatures["serving_default"]
 
-def gen_text(prompt, max_length=512):
+def gen_text(prompt, max_length=256):
     prompt = tf.constant(f"### Instruction:\n{prompt}\n\n### Response:\n")
     max_length = tf.constant(max_length, dtype="int64")