feat: add weights

.gitignore

@@ -1,162 +0,0 @@
-# Byte-compiled / optimized / DLL files
-__pycache__/
-*.py[cod]
-*$py.class
-
-weights/
-
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
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-.installed.cfg
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-MANIFEST
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-# Unit test / coverage reports
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-.hypothesis/
-.pytest_cache/
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-
-# Translations
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README.md

@@ -1,3 +1,16 @@
+---
+title: QuillGPT
+emoji: 📉
+colorFrom: yellow
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.40.2
+app_file: app.py
+pinned: false
+license: mit
+short_description: Implementation of the GPT decoder block in PyTorch
+---
+
 ![QuillGPT-cropped-removebg-preview](https://github.com/NotShrirang/QuillGPT/assets/85283622/2e63d8ce-24f8-4bf0-835a-0c621f1d7400)
 
 # QuillGPT
@@ -111,21 +124,21 @@ To train the GPT model, follow these steps:
 2. Write the configurations for transformer and save the file.
    <br>For example:
    `json
- {
-   "data_path": "data/corpus.txt",
-   "vocab_size": 135,
-   "batch_size": 32,
-   "block_size": 256,
-   "max_iters": 3000,
-   "eval_interval": 300,
-   "learning_rate": 3e-5,
-   "eval_iters": 50,
-   "n_embd": 1024,
-   "n_head": 12,
-   "n_layer": 18,
-   "dropout": 0.3,
- }
- `
+{
+  "data_path": "data/corpus.txt",
+  "vocab_size": 135,
+  "batch_size": 32,
+  "block_size": 256,
+  "max_iters": 3000,
+  "eval_interval": 300,
+  "learning_rate": 3e-5,
+  "eval_iters": 50,
+  "n_embd": 1024,
+  "n_head": 12,
+  "n_layer": 18,
+  "dropout": 0.3,
+}
+`
 
 3. Train model using script `scripts/train_gpt.py`
 

app.py

@@ -18,7 +18,6 @@ def decode_text(input, model: GPTLanguageModel, max_tokens, temperature):
 
 models = {
     "Shakespearean GPT": './weights/GPT_model_char.pt',
-    "GPT": './weights/Harpoon_Corpus_GPT_model_word2.pt',
 }
 
 st.sidebar.header('QuillGPT')

weights/GPT_model_char.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a5f38b0e71d3885796283c4393dfb56980570a230cbb0b62c09d0c7dfa446fd
+size 52673448