Upload 11 files

.gcloudignore

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+# This file specifies files that are *not* uploaded to Google Cloud
+# using gcloud. It follows the same syntax as .gitignore, with the addition of
+# "#!include" directives (which insert the entries of the given .gitignore-style
+# file at that point).
+#
+# For more information, run:
+#   $ gcloud topic gcloudignore
+#
+.gcloudignore
+# If you would like to upload your .git directory, .gitignore file or files
+# from your .gitignore file, remove the corresponding line
+# below:
+.git
+.gitignore
+
+# Python pycache:
+__pycache__/
+# Ignored by the build system
+/setup.cfg

.gitattributes

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-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

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-FROM python:3.12.0
-
-RUN useradd -m -u 10000 user
-
-WORKDIR /app
-
-COPY --chown=user ./requirements.txt requirements.txt
-
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
-
-COPY --chown=user . /app
-
-# Set gunicorn as entrypoint with default arguments
-ENTRYPOINT ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]
+FROM python:3.12.0
+
+RUN useradd -m -u 10000 user
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+
+# Set gunicorn as entrypoint with default arguments
+ENTRYPOINT ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

README.md

@@ -1,11 +1,11 @@
----
-title: Zing
-emoji: 🌍
-colorFrom: yellow
-colorTo: gray
-sdk: docker
-pinned: false
-license: llama2
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Zing
+emoji: 🌍
+colorFrom: yellow
+colorTo: gray
+sdk: docker
+pinned: false
+license: llama2
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,25 +1,25 @@
-from flask import Flask, render_template, request, jsonify
-#from getans import get_response
-
-app = Flask(__name__)
-
-# Store chat history
-chat_history = []
-
-
-@app.route('/')
-def index():
-    return render_template('index.html', chat_history=chat_history)
-
-
-@app.route('/send_message', methods=['POST'])
-def send_message():
-    user_message = request.form['message']
-    # Here you can process the user message and generate a response
-    bot_response = user_message#get_response(user_message, max_new_tokens=100)
-    chat_history.append({'user': user_message, 'bot': bot_response})
-    return jsonify(chat_history=chat_history)
-
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, render_template, request, jsonify
+#from getans import get_response
+
+app = Flask(__name__)
+
+# Store chat history
+chat_history = []
+
+
+@app.route('/')
+def index():
+    return render_template('index.html', chat_history=chat_history)
+
+
+@app.route('/send_message', methods=['POST'])
+def send_message():
+    user_message = request.form['message']
+    # Here you can process the user message and generate a response
+    bot_response = user_message#get_response(user_message, max_new_tokens=100)
+    chat_history.append({'user': user_message, 'bot': bot_response})
+    return jsonify(chat_history=chat_history)
+
+
+if __name__ == '__main__':
+    app.run(debug=True)

finetuning/finetuning.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
+from huggingface_hub import login
+token1="hf_"
+token2="rPlNHzkJScHYmtGSaQPcaoKcjJGYQEpjLu"
+login(token=token1+token2)
+# Load pre-trained model and tokenizer (replace with desired model name)
+model_name = "meta-llama/Llama-2-7b-chat-hf"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define training arguments (hyperparameters)
+training_args = TrainingArguments(
+    output_dir='output',  # Output directory for checkpoints etc.
+    per_device_train_batch_size=8,  # Adjust based on your hardware
+    save_steps=10_000,
+    num_train_epochs=3,  # Adjust training epochs as needed
+)
+
+# Load your training and validation data (specific to your chosen library)
+train_dataset = "data/train.csv"
+val_dataset = "data/val.csv"
+# Create a Trainer object for fine-tuning
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,  # Replace with your training data loader
+    eval_dataset=val_dataset,  # Replace with your validation data loader
+)
+
+# Start fine-tuning
+trainer.train()

getans.py

@@ -1,18 +1,20 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from huggingface_hub import login
-token1="hf_"
-token2="rPlNHzkJScHYmtGSaQPcaoKcjJGYQEpjLu"
-login(token=token1+token2)
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-
-def get_response(prompt, max_new_tokens=50):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature=0.0001, do_sample=True)
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)  # Use indexing instead of calling
-    ans=response.toString()
-    return ans
+from mmap import mmap
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from huggingface_hub import login
+token1="hf_"
+token2="rPlNHzkJScHYmtGSaQPcaoKcjJGYQEpjLu"
+login(token=token1+token2)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+mm="finetuning/output/pytorch_model.bin"
+tokenizer = AutoTokenizer.from_pretrained(mm)
+model = AutoModelForCausalLM.from_pretrained(mm)
+
+def get_response(prompt, max_new_tokens=50):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, temperature=0.0001, do_sample=True)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)  # Use indexing instead of calling
+    ans=response.toString()
+    return ans

requirements.txt

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-Flask~=3.0.3
-gunicorn~=22.0.0
-torch
-transformers
+Flask~=3.0.3
+gunicorn~=22.0.0
+torch
+transformers
 huggingface_hub