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.github/workflows/update_space.yml

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+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

README.md

@@ -1,12 +1,6 @@
 ---
-title: Mathstral Test
-emoji: 🌖
-colorFrom: green
-colorTo: green
-sdk: gradio
-sdk_version: 4.39.0
+title: mathstral_test
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.38.1
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from huggingface_hub import InferenceClient
+import gradio as gr
+import os
+
+API_URL = {
+    "Mistral" : "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3",
+    "Mixtral" : "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "Mathstral" : "https://api-inference.huggingface.co/models/mistralai/mathstral-7B-v0.1",
+}
+
+HF_TOKEN = os.environ['HF_TOKEN']
+
+mistralClient = InferenceClient(
+    API_URL["Mistral"],
+    headers = {"Authorization" : f"Bearer {HF_TOKEN}"},
+)
+
+mixtralClient = InferenceClient(
+    model = API_URL["Mixtral"],
+    headers = {"Authorization" : f"Bearer {HF_TOKEN}"},
+)
+
+mathstralClient = InferenceClient(
+    model = API_URL["Mathstral"],
+    headers = {"Authorization" : f"Bearer {HF_TOKEN}"},
+)
+
+def format_prompt(message, history):
+  prompt = "<s>"
+
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95,
+             repetition_penalty=1.0, model = "Mathstral"):
+    # Selecting model to be used
+    if(model == "Mistral"):
+      client = mistralClient
+    elif(model == "Mixstral"):
+      client = mixtralClient
+    elif(model == "Mathstral"):
+      client = mixtralClient
+
+    
+    temperature = float(temperature) # Generation arguments
+    if temperature < 1e-2:
+        temperature = 1e-2
+        
+    top_p = float(top_p)
+    
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+    
+    formatted_prompt = format_prompt(prompt, history)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+additional_inputs=[
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=2048,
+        minimum=0,
+        maximum=4096,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.0,
+        maximum=1,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    ),
+        gr.Dropdown(
+        choices = ["Mistral","Mixtral", "Mathstral"],
+        value = "Mathstral",
+        label = "Le modèle à utiliser",
+        interactive=True,
+        info = "Mistral : pour des conversations génériques, "+ 
+               "Mixtral : conversations plus rapides et plus performantes, "+ 
+               "Mathstral : raisonnement mathématiques et scientifique"
+    ),
+]
+
+css = """
+  #mkd {
+    height: 500px;
+    overflow: auto;
+    border: 1px solid #ccc;
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML("<h1><center>Mathstral Test</center><h1>")
+    gr.HTML("<h3><center>Dans cette démo, vous pouvez poser des questions mathématiques et scientifiques à Mathstral. 🧮</center><h3>")
+    gr.ChatInterface(
+        generate,
+        additional_inputs=additional_inputs,
+        theme = gr.themes.Soft(),
+        examples=[ [l.strip()] for l in open("exercices.md").readlines()],
+        chatbot = gr.Chatbot(
+            latex_delimiters=[
+                {"left" : "$$", "right": "$$", "display": True },
+                {"left" : "\\[", "right": "\\]", "display": True },
+                {"left" : "\\(", "right": "\\)", "display": False },
+                {"left": "$", "right": "$", "display": False }
+                ]
+            )
+    )
+
+demo.queue(max_size=100).launch(debug=True)

app.py~

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+from huggingface_hub import InferenceClient
+import gradio as gr
+import os
+
+API_URL = {
+    "Mistral" : "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3",
+    "Mixtral" : "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "Mathstral" : "https://api-inference.huggingface.co/models/mistralai/mathstral-7B-v0.1",
+}
+
+HF_TOKEN = os.environ['HF_TOKEN']
+
+mistralClient = InferenceClient(
+    API_URL["Mistral"],
+    headers = {"Authorization" : f"Bearer {HF_TOKEN}"},
+)
+
+mixtralClient = InferenceClient(
+    model = API_URL["Mixtral"],
+    headers = {"Authorization" : f"Bearer {HF_TOKEN}"},
+)
+
+mathstralClient = InferenceClient(
+    model = API_URL["Mathstral"],
+    headers = {"Authorization" : f"Bearer {HF_TOKEN}"},
+)
+
+def format_prompt(message, history):
+  prompt = "<s>"
+
+  for user_prompt, bot_response in history:
+    prompt += f"[INST] {user_prompt} [/INST]"
+    prompt += f" {bot_response}</s> "
+  prompt += f"[INST] {message} [/INST]"
+  return prompt
+
+def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95,
+             repetition_penalty=1.0, model = "Mathstral"):
+    # Selecting model to be used
+    if(model == "Mistral"):
+      client = mistralClient
+    elif(model == "Mixstral"):
+      client = mixtralClient
+    elif(model == "Mathstral"):
+      client = mixtralClient
+
+    
+    temperature = float(temperature) # Generation arguments
+    if temperature < 1e-2:
+        temperature = 1e-2
+        
+    top_p = float(top_p)
+    
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,
+    )
+    
+    formatted_prompt = format_prompt(prompt, history)
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+additional_inputs=[
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=2048,
+        minimum=0,
+        maximum=4096,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.0,
+        maximum=1,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    ),
+        gr.Dropdown(
+        choices = ["Mistral","Mixtral", "Mathstral"],
+        value = "Mathstral",
+        label = "Le modèle à utiliser",
+        interactive=True,
+        info = "Mistral : pour des conversations génériques, "+ 
+               "Mixtral : conversations plus rapides et plus performantes, "+ 
+               "Mathstral : raisonnement mathématiques et scientifique"
+    ),
+]
+
+css = """
+  #mkd {
+    height: 500px;
+    overflow: auto;
+    border: 1px solid #ccc;
+  }
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.HTML("<h1><center>Mathstral Test</center><h1>")
+    gr.HTML("<h3><center>Dans cette démo, vous pouvez poser des questions mathématiques et scientifiques à Mathstral. 🧮</center><h3>")
+    gr.ChatInterface(
+        generate,
+        additional_inputs=additional_inputs,
+        theme = gr.themes.Soft(),
+        examples=[ [l.strip()] for l in open("exercices.md").readlines()],
+        chatbot = gr.Chatbot(
+            latex_delimiters=[
+                {"left" : "$$", "right": "$$", "display": True },
+                {"left" : "\\[", "right": "\\]", "display": True },
+                {"left" : "\\(", "right": "\\)", "display": False },
+                {"left": "$", "right": "$", "display": False }
+                ]
+            )
+    )
+
+demo.queue(max_size=100).launch(debug=True)

exercices.md

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+Déterminer les fonctions $f : \\mathbb{R} \\rightarrow \\mathbb{R}$ dérivables et telles que $$\\forall x \\in \\mathbb{R}, f'(x) + f(x) = f(0) + f(1)$$
+Soit $f : \mathbb{R} \rightarrow \mathbb{R}$ une fonction borneé et dérivable, telle que $\lim_{x \rightarrow +\infty} f' = l$. Montrer que $l = 0$.
+Soit $f(x) = \int_{t=0}^1\frac{1-t}{\ln t}t^x\, dt$. Étudier le domaine de définition de $f$, sa dérivabilité, puis calculer $f(x)$.
+Soit $(G,\, \star \,)$ un groupe tel que $$\forall x \in G,x^2  = e$$ Montrer que $G$ est commutatif.
+Soit $(E,\, \star \,)$ un monoïde avec $E$ ensemble fini. Montrer que tout élément régulier de $E$ est inversible.
+Factoriser le polynôme $(X + i)^n  - (X - i)^n $ pour $n \in \mathbb{N}^\star$.
+Soient $a \in \left] {0,\pi } \right[$ et $n \in \mathbb{N}^\star  $. Factoriser dans $\mathbb{C}\left[ X \right]$ puis dans $\mathbb{R}\left[ X \right]$ le polynôme $$X^{2n}  - 2\cos (na)X^n  + 1$$
+Soient $F,G,F',G'$ des sous-espaces vectoriels de $E$ tels que $F \cap G = F' \cap G'$. Montrer que $$(F + (G \cap F')) \cap (F + (G \cap G')) = F$$
+Trouver les coordonnées des vecteurs suivants relativement à la base $\left( (1,1,1), (1,1,0), (1,0,0) \right)$ de $\mathbb{R}^3$ : $u = (4,-3,2)$ et $v = (a,b,c)$.
+
+
+
+
+

exercices.md~

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+Déterminer les fonctions $f : \\mathbb{R} \\rightarrow \\mathbb{R}$ dérivables et telles que $$\\forall x \\in \\mathbb{R}, f'(x) + f(x) = f(0) + f(1)$$
+Soit $f : \mathbb{R} \rightarrow \mathbb{R}$ une fonction borneé et dérivable, telle que $\lim_{x \rightarrow +\infty} f' = l$. Montrer que $l = 0$.
+Soit $f(x) = \int_{t=0}^1\frac{1-t}{\ln t}t^x\, dt$. Étudier le domaine de définition de $f$, sa dérivabilité, puis calculer $f(x)$.

requirements.txt

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+huggingface_hub
+gradio