Duplicate from crumb/galactica-1.3b-contrastive-sampling

Co-authored-by: crumb <crumb@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Galactica Base (1.3B) Contrastive Sampling
+emoji: 📝
+colorFrom: yellow
+colorTo: blue
+sdk: gradio
+sdk_version: 3.9.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: crumb/galactica-1.3b-contrastive-sampling
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-1.3b")
+model = AutoModelForCausalLM.from_pretrained("facebook/galactica-1.3b")
+text2text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer, num_workers=2)
+
+def predict(text, max_length=64, penalty_alpha=0.6, top_k=4):
+    text = text.strip()
+    out_text = text2text_generator(text, max_length=max_length, 
+                              penalty_alpha=penalty_alpha, 
+                              top_k=top_k,
+                              eos_token_id = tokenizer.eos_token_id,
+                              bos_token_id = tokenizer.bos_token_id,
+                              pad_token_id = tokenizer.pad_token_id,
+                         )[0]['generated_text']
+    out_text = "<p>" + out_text + "</p>"
+    out_text = out_text.replace(text, text + "<b><span>")
+    out_text = out_text +  "</span></b>"
+    out_text = out_text.replace("\n", "<br>")
+    return out_text
+
+iface = gr.Interface(
+    fn=predict, 
+    inputs=[
+        gr.inputs.Textbox(lines=5, label="Input Text"),
+        gr.inputs.Slider(minimum=32, maximum=64, default=64, label="Max Length"),
+        gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.6, step=0.1, label="Penalty Alpha"),
+        # gr.inputs.Checkbox(label="Do Sample"),
+        gr.inputs.Slider(minimum=0, maximum=16, default=8, step=1, label="Top K")
+    ],
+    outputs=gr.HTML(),
+    description="Galactica Base Model",
+    examples=[[
+            "The attention mechanism in LLM is",
+            32,
+            0.6,
+            4
+        ], 
+        [
+            "Title: Attention is all you need\n\nAbstract:",
+            32,
+            0.6,
+            4
+        ]
+    ]
+)
+
+iface.launch()

requirements.txt

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+git+https://github.com/paperswithcode/galai
+gradio