:sparkles: initial commit

.github/workflows/push_to_hub.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push https://willsh1997:[email protected]/spaces/willsh1997/linkedin-generator main

README.md

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+---
+title: Linkedin Generator
+emoji: 🏆
+colorFrom: red
+colorTo: green
+sdk: gradio
+sdk_version: 5.23.3
+app_file: linkedin_gradio.py
+pinned: false
+license: apache-2.0
+short_description: generates linkedin posts from freetext entries
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

linkedin_gradio.py

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+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+import torch
+from transformers import pipeline
+import pandas as pd
+import gradio as gr
+import os
+import copy
+import spaces
+
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer, TextIteratorStreamer
+
+
+# quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+torch_device = "cuda" if torch.cuda.is_available() else ("mps" if torch.mps.is_available() else "cpu")
+
+torch_dtype = torch.float16 if torch_device in ["cuda", "mps"] else torch.float32
+
+llama_model=AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct", 
+                                           #  quantization_config=quantization_config, 
+                                           torch_dtype=torch_dtype, 
+                                           device_map=torch_device,
+                                            load_in_4bit=True) #for puny devices like mine.
+
+llama_tokenizer=AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
+
+# streamer = TextStreamer(llama_tokenizer)
+
+llama32_3b_pipe = pipeline(
+    "text-generation",
+    model=llama_model,
+    tokenizer=llama_tokenizer,
+    # streamer = streamer,
+)
+
+@spaces.GPU
+def llama32_3b_chat(message) -> str: 
+    "simplifies pipeline output to only return generated text"
+    input_history = [{"role": "system", "content": """You are a machine that takes literally any text, and then responds in the format of a linkedin post about the same content in the text. Make sure to use phrases like "Excited for the opportunity", "Here's what X taught me about B2B sales", "grateful for my time at X", "excited for the new adventures at {insert company name}", and "professional growth mindset". Make sure that your linkedin post always refers to the content in some way.
+    """}]
+    input_history.append({"role": "user", "content": f"I am about to start a enw job at {message}"})
+    ##add sth about context window here
+
+    outputs = llama32_3b_pipe(
+        input_history,
+        max_new_tokens=512
+    )
+    return outputs[-1]['generated_text'][-1]['content']
+    
+
+
+# Create the Gradio interface
+def create_interface():
+    
+    with gr.Blocks() as demo:
+        gr.Markdown("""LinkedIn Post Generator - fixing the chat "head" to force a chat model to only generate linkedin posts
+                    """)
+        with gr.Row():
+            text_input = gr.Textbox(label="input for Linkedin Post Generator", value = "Excited for my new opportunity at {x company} after {y} years at {z company}! To new adventures!")
+        with gr.Row():
+            submit_btn = gr.Button("Translate")
+        with gr.Row():
+            text_output = gr.Textbox(interactive=False)
+
+        submit_btn.click(
+            fn=llama32_3b_chat,
+            inputs=[text_input],
+            outputs=[text_output]
+        )
+    
+    return demo
+
+# Launch the app
+demo = create_interface()
+demo.launch()

requirements.txt

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+accelerate==1.4.0
+aiofiles==23.2.1
+annotated-types==0.7.0
+anyio==4.8.0
+asttokens==3.0.0
+bitsandbytes==0.45.4
+certifi==2025.1.31
+charset-normalizer==3.4.1
+click==8.1.8
+comm==0.2.2
+debugpy==1.8.12
+decorator==5.1.1
+exceptiongroup==1.2.2
+executing==2.2.0
+fastapi==0.115.8
+ffmpy==0.5.0
+filelock==3.17.0
+fsspec==2025.2.0
+gradio==5.16.1
+gradio_client==1.7.0
+h11==0.14.0
+httpcore==1.0.7
+httpx==0.28.1
+huggingface-hub==0.28.1
+idna==3.10
+ipykernel==6.29.5
+ipython==8.32.0
+jedi==0.19.2
+Jinja2==3.1.5
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+mpmath==1.3.0
+nest-asyncio==1.6.0
+networkx==3.4.2
+numpy==2.2.3
+orjson==3.10.15
+packaging==24.2
+pandas==2.2.3
+parso==0.8.4
+pexpect==4.9.0
+pillow==11.1.0
+platformdirs==4.3.6
+prompt_toolkit==3.0.50
+psutil==7.0.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pydantic==2.10.6
+pydantic_core==2.27.2
+pydub==0.25.1
+Pygments==2.19.1
+python-dateutil==2.9.0.post0
+python-multipart==0.0.20
+pytz==2025.1
+PyYAML==6.0.2
+pyzmq==26.2.1
+regex==2024.11.6
+requests==2.32.3
+rich==13.9.4
+ruff==0.9.6
+safehttpx==0.1.6
+safetensors==0.5.2
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+stack-data==0.6.3
+starlette==0.45.3
+sympy==1.13.1
+tokenizers==0.21.0
+tomlkit==0.13.2
+torch==2.4.0
+tornado==6.4.2
+tqdm==4.67.1
+traitlets==5.14.3
+transformers==4.49.0
+typer==0.15.1
+typing_extensions==4.12.2
+tzdata==2025.1
+urllib3==2.3.0
+uvicorn==0.34.0
+wcwidth==0.2.13
+websockets==14.2
+