Fix tok

.gitignore

@@ -0,0 +1,160 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

app.py

@@ -19,6 +19,7 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 model_id = "trl-lib/llama-se-rl-merged"
+print(f"Loading model: {model_id}")
 if device == "cpu":
     model = AutoModelForCausalLM.from_pretrained(model_id, low_cpu_mem_usage=True, use_auth_token=HF_TOKEN)
 else:
@@ -28,11 +29,14 @@ else:
 
 tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=HF_TOKEN)
 
-PROMPT_TEMPLATE = """Question: {prompt}\n\nAnswer: """
+PROMPT_TEMPLATE = """Question: {prompt}\n\nAnswer:"""
 
 
-def generate(instruction, temperature=1, max_new_tokens=256, top_p=1, top_k=0):
+def generate(instruction, temperature=0.7, max_new_tokens=256, top_p=0.95, top_k=40):
     formatted_instruction = PROMPT_TEMPLATE.format(prompt=instruction)
+
+    temperature = float(temperature)
+    top_p = float(top_p)
     streamer = TextIteratorStreamer(tokenizer)
     model_inputs = tokenizer(formatted_instruction, return_tensors="pt", truncation=True, max_length=2048).to(device)
 
@@ -56,8 +60,8 @@ def generate(instruction, temperature=1, max_new_tokens=256, top_p=1, top_k=0):
             hidden_output += new_text
             continue
         # replace eos token
-        if tokenizer.eos_token in new_text:
-            new_text = new_text.replace(tokenizer.eos_token, "")
+        # if tokenizer.eos_token in new_text:
+        #     new_text = new_text.replace(tokenizer.eos_token, "")
         output += new_text
         yield output
     return output
@@ -66,8 +70,7 @@ def generate(instruction, temperature=1, max_new_tokens=256, top_p=1, top_k=0):
 examples = [
     "How do I create an array in C++ of length 5 which contains all even numbers between 1 and 10?",
     "How can I write a Java function to generate the nth Fibonacci number?",
-    "How can I write a Python function that checks if a given number is a palindrome or not?",
-    "I have a lion in my garden. How can I get rid of it?",
+    "How can I sort a list in Python?",
 ]
 
 
@@ -77,7 +80,7 @@ def process_example(args):
     return x
 
 
-with gr.Blocks(theme=theme) as demo:
+with gr.Blocks(theme=theme, analytics_enabled=False) as demo:
     with gr.Column():
         gr.Markdown(
             """<h1><center>🦙🦙🦙 StackLLaMa 🦙🦙🦙</center></h1>
@@ -111,7 +114,7 @@ with gr.Blocks(theme=theme) as demo:
             with gr.Column(scale=1):
                 temperature = gr.Slider(
                     label="Temperature",
-                    value=1.0,
+                    value=0.7,
                     minimum=0.0,
                     maximum=2.0,
                     step=0.1,
@@ -120,25 +123,25 @@ with gr.Blocks(theme=theme) as demo:
                 )
                 max_new_tokens = gr.Slider(
                     label="Max new tokens",
-                    value=256,
+                    value=64,
                     minimum=0,
                     maximum=2048,
-                    step=5,
+                    step=4,
                     interactive=True,
                     info="The maximum numbers of new tokens",
                 )
                 top_p = gr.Slider(
                     label="Top-p (nucleus sampling)",
-                    value=1.0,
+                    value=0.95,
                     minimum=0.0,
                     maximum=1,
                     step=0.05,
                     interactive=True,
-                    info="Higher values sample fewer low-probability tokens",
+                    info="Higher values sample more low-probability tokens",
                 )
                 top_k = gr.Slider(
                     label="Top-k",
-                    value=0,
+                    value=40,
                     minimum=0,
                     maximum=100,
                     step=2,
@@ -150,4 +153,4 @@ with gr.Blocks(theme=theme) as demo:
     instruction.submit(generate, inputs=[instruction, temperature, max_new_tokens, top_p, top_k], outputs=[output])
 
 demo.queue(concurrency_count=1)
-demo.launch(enable_queue=True)
+demo.launch(enable_queue=True, share=True)