Add mistral7b agent #625
Add mistral7b agent #625
Conversation
WalkthroughTwo new Jupyter notebook examples are added demonstrating AI assistants: one powered by Groq's Llama3-8b-8192 model with Groq client integration, and another using the Mistral-7B-Instruct model with fallback to distilgpt2, including setup, prompt construction, model loading, and example queries. Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant GroqNotebook as Groq Notebook
participant GroqClient
User->>GroqNotebook: Provide question
GroqNotebook->>GroqNotebook: build_prompt(user_question)
GroqNotebook->>GroqClient: run_groq_chat(prompt_messages, model)
GroqClient-->>GroqNotebook: Return model response
GroqNotebook->>User: Display detailed answer and summary
sequenceDiagram
participant User
participant MistralNotebook as Mistral Notebook
participant Model
User->>MistralNotebook: Provide question
MistralNotebook->>Model: Load Mistral-7B-Instruct (fallback to distilgpt2 if fails)
MistralNotebook->>Model: chat(prompt)
Model-->>MistralNotebook: Return generated response
MistralNotebook->>User: Display detailed answer
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Hello @DhivyaBharathy-web, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
Summary of Changes
Hello! gemini-code-assist here, providing a summary of this pull request to help everyone get up to speed.
Based on the patch content, this PR introduces a new example Jupyter notebook (examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb). This notebook demonstrates how to create a simple AI assistant using the Groq API and the llama3-8b-8192 model. It shows how to set up the Groq client, define model and prompt configurations using YAML, construct prompts, and run chat completions.
Note: The PR title "Add mistral7b agent" and description mention adding a Mistral agent. However, the actual changes in the patch add a notebook for a Groq agent using the Llama3 model. There seems to be a mismatch between the stated intent and the code changes presented in this specific patch.
Highlights
- New Example Notebook: Adds a new Jupyter notebook (
Groq_LPU_Powered_AI_Assistant.ipynb) to theexamples/cookbooksdirectory. - Groq API Integration: Demonstrates how to integrate with the Groq API using the
groqPython SDK. - Llama3 Model Usage: Shows how to use the
llama3-8b-8192model via the Groq API for chat completions. - Structured Agent Example: Provides an example of structuring an AI agent using YAML configuration for model and prompt templates.
Changelog
- examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb
- Added a new Jupyter notebook demonstrating a Groq-powered AI assistant using the Llama3-8b-8192 model.
- Includes steps for installing dependencies (
groq,pyyaml), initializing the Groq client, defining configuration via YAML, building prompts, and running an example query.
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Code Review
This pull request introduces a new Jupyter notebook demonstrating an AI assistant powered by Groq's Llama3 model. The notebook is well-structured and provides a good example of using YAML for configuration and prompt templating. I've focused my review on the Groq_LPU_Powered_AI_Assistant.ipynb file, as this is the only file with changes present in the provided diff.
Summary of Findings
- API Key Handling: The notebook includes a hardcoded placeholder for the
GROQ_API_KEY. This is a security concern as it might lead to accidental exposure of real keys if users edit it directly and commit. It's recommended to guide users to use environment variables or secure input methods. - Colab Badge URL: The 'Open in Colab' badge URL points to a repository (
DhivyaBharathy-web/PraisonAI) that might differ from the target repository of this PR (MervinPraison/PraisonAI). This should be verified and corrected if necessary. - Prompt Structure Clarity: The system message in
build_promptis generic, while detailed persona instructions are part of the user message. Structuring this more conventionally (persona in system message, user query in user message) could improve clarity and model interaction.
Merge Readiness
This pull request has some valuable additions with the Groq LPU-powered AI assistant notebook. However, due to the critical security issue regarding API key handling and other medium-severity concerns, I recommend that these changes be addressed before merging. As an AI, I am not authorized to approve pull requests; please ensure further review and approval from team members.
| "from groq import Groq\n", | ||
| "\n", | ||
| "# Set API key (replace with your own or use environment variables)\n", | ||
| "os.environ['GROQ_API_KEY'] = 'enter your key'\n", |
There was a problem hiding this comment.
Hardcoding API key placeholders like 'enter your key' directly in the script is a security risk and encourages bad practices, even if it's just a placeholder. Committing actual keys by mistake becomes easier.
Consider guiding users to set the GROQ_API_KEY as an environment variable outside the notebook, or using a method like getpass.getpass() for interactive input if this notebook is primarily for demonstration and not unattended execution. This prevents accidental key exposure in version control.
# Set API key (replace with your own or use environment variables)
# It's recommended to set this via environment variables or a secure input method.
# Example:
# import os
# os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
# api_key = os.environ.get('GROQ_API_KEY')
# if not api_key:
# raise ValueError("GROQ_API_KEY environment variable not set.")
# client = Groq(api_key=api_key)
import os
import getpass
if 'GROQ_API_KEY' not in os.environ:
os.environ['GROQ_API_KEY'] = getpass.getpass('Enter your Groq API Key: ')
Style Guide References
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "[](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb)\n" |
There was a problem hiding this comment.
The URL for the 'Open in Colab' badge points to DhivyaBharathy-web/PraisonAI. If this PR is intended for the MervinPraison/PraisonAI repository, should this URL be updated to reflect the correct repository path once merged? For example, https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb (assuming 'main' is the target branch).
Style Guide References
| "def build_prompt(user_question):\n", | ||
| " prompt_text = config['prompt_template'].format(user_question=user_question)\n", | ||
| " messages = [\n", | ||
| " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", | ||
| " {\"role\": \"user\", \"content\": prompt_text}\n", | ||
| " ]\n", | ||
| " return messages\n" |
There was a problem hiding this comment.
The build_prompt function currently uses a generic system message: {"role": "system", "content": "You are a helpful assistant."}. However, the prompt_template (loaded into config['prompt_template']) already contains detailed persona instructions: "You are an expert AI assistant knowledgeable about Groq's technology...".
When prompt_text (which includes these persona instructions) is assigned to the user role, the model receives the persona instructions as part of the user's message. This is functional, but it might be clearer and more conventional to place the primary persona/instructions in the system message.
def build_prompt(user_question):
system_content = "You are an expert AI assistant knowledgeable about Groq's technology. Provide a detailed answer to the user's question. Then summarize the key points briefly."
user_content = f'User question: "{user_question}"'
messages = [
{"role": "system", "content": system_content},
{"role": "user", "content": user_content}
]
return messages
Style Guide References
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Actionable comments posted: 4
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🔇 Additional comments (5)
examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb (5)
10-12: Major inconsistency: Content doesn't match PR description.The PR objectives state this adds a "MistralTechAgent" using Mistral-7B-Instruct model, but this notebook is actually about Groq's Llama3-8b-8192 model. Please ensure the PR description accurately reflects the changes being made.
Likely an incorrect or invalid review comment.
119-129: Good use of YAML configuration pattern.The YAML-based configuration approach is excellent for making the prompt template and model easily configurable. The prompt template is well-structured with clear instructions.
152-158: Clean prompt construction implementation.The function effectively formats the prompt template and creates the proper message structure for the chat API. The system message provides good context.
212-216: Well-structured main execution logic.The main logic effectively demonstrates the agent's functionality with a relevant example question about Groq technology.
18-18:❓ Verification inconclusive
Verify the repository reference in Colab link.
The Colab link references
DhivyaBharathy-web/PraisonAIwhich may not be the correct upstream repository. Ensure this points to the appropriate repository and branch.
🏁 Script executed:
#!/bin/bash # Description: Check if the repository reference in the Colab link is correct # Expected: Verify if this should point to MervinPraison/PraisonAI instead echo "Current Colab link references: DhivyaBharathy-web/PraisonAI" echo "Expected repository might be: MervinPraison/PraisonAI" echo "Please verify the correct repository for the Colab link."Length of output: 368
Confirm and update the Colab badge repository owner
The Colab badge in examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb currently points to
DhivyaBharathy-web/PraisonAI. If the canonical repository is under a different owner (e.g.MervinPraison/PraisonAI), please update the URL accordingly:Replace:
- [](https://colab.research.google.com/github/DhivyaBharathy-web/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb) + [](https://colab.research.google.com/github/MervinPraison/PraisonAI/blob/main/examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb)• File: examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb
• Line: 18Verify that the repository owner (and branch, if needed) in the Colab link matches your upstream project.
| "outputs": [ | ||
| { | ||
| "output_type": "stream", | ||
| "name": "stdout", | ||
| "text": [ | ||
| "Agent response:\n", | ||
| "Groq's Low-Precision Unified (LPU) technology is a proprietary architecture designed to accelerate artificial intelligence (AI) and machine learning (ML) workloads. LPU-powered models, also known as Groq Models, have several advantages over traditional Graphics Processing Units (GPUs) in specific applications:\n", | ||
| "\n", | ||
| "1. **Improved energy efficiency**: LPU is optimized for low power consumption, making it suitable for edge, mobile, and embedded devices where power constraints are common. This is particularly important for applications that require long-lived deployments, such as autonomous vehicles or IoT sensors.\n", | ||
| "2. **Enhanced accuracy**: LPU's customized precision and data type selection enable better representational precision for numeric computations, resulting in improved model accuracy. This is particularly beneficial for tasks that require high-fidelity calculations, such as medical imaging or natural language processing.\n", | ||
| "3. **Simplified software development**: LPU's unified architecture simplifies the development process for AI/ML developers. Groq Models provide a consistent programming model across different inference scenarios, allowing for easier model deployment and optimization.\n", | ||
| "4. **Increased throughput**: LPU's optimized arithmetic units and pipelined architecture enable higher Throughput per Watt (TPW) compared to traditional GPUs. This translates to faster processing times and higher compute density.\n", | ||
| "5. **Flexibility and scalability**: LPU-powered models can be deployed across various hardware platforms, from small, low-power devices to large data center clusters. This flexibility allows developers to choose the optimal deployment scenario for their specific use case.\n", | ||
| "6. **Customization and specialization**: LPU's architecture can be customized for specific workloads, allowing for optimized performance and power consumption. This customization potential enables developers to create highly specialized AI/ML hardware that matches their specific requirements.\n", | ||
| "\n", | ||
| "In summary, Groq's LPU-powered models offer significant advantages over traditional GPUs in terms of energy efficiency, accuracy, software development simplicity, throughput, flexibility, and customization.\n", | ||
| "\n", | ||
| "Key points:\n", | ||
| "\n", | ||
| "* Improved energy efficiency and suitability for edge, mobile, and embedded devices\n", | ||
| "* Enhanced accuracy for high-fidelity calculations\n", | ||
| "* Simplified software development with a unified programming model\n", | ||
| "* Increased throughput and compute density\n", | ||
| "* Flexibility and scalability across various hardware platforms\n", | ||
| "* Customization potential for specific workloads and applications\n" | ||
| ] | ||
| } |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Clear execution output before committing.
While the execution demonstrates the agent works correctly, the output should be cleared before committing to keep the repository clean.
🤖 Prompt for AI Agents
In examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb around lines 183 to
209, the notebook contains execution output in the cell outputs that should be
cleared before committing. To fix this, clear all cell outputs in the notebook
to remove the printed agent response and any other output, ensuring the
committed notebook is clean and does not include runtime results.
| "outputs": [ | ||
| { | ||
| "output_type": "stream", | ||
| "name": "stdout", | ||
| "text": [ | ||
| "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/129.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m122.9/129.6 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.6/129.6 kB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", | ||
| "\u001b[?25h" | ||
| ] | ||
| } | ||
| ], |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Remove execution output from committed notebooks.
Jupyter notebook execution output should not be committed to version control as it clutters the repository and can cause merge conflicts. Please clear all cell outputs before committing.
Run this command to clear all outputs:
# Clear all notebook outputs
jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb🤖 Prompt for AI Agents
In examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb around lines 47 to 56,
the notebook contains execution output that should not be committed. Clear all
cell outputs from the notebook before committing by running the command: jupyter
nbconvert --ClearOutputPreprocessor.enabled=True --inplace
examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb. This will remove the
output metadata and keep the notebook clean for version control.
| "def run_groq_chat(prompt_messages, model='llama3-8b-8192'):\n", | ||
| " response = client.chat.completions.create(\n", | ||
| " model=model,\n", | ||
| " messages=prompt_messages\n", | ||
| " )\n", | ||
| " return response.choices[0].message.content\n" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Add error handling to the chat function.
The function lacks error handling for API failures, rate limiting, or network issues, which could cause the notebook to crash unexpectedly.
Add error handling:
def run_groq_chat(prompt_messages, model='llama3-8b-8192'):
- response = client.chat.completions.create(
- model=model,
- messages=prompt_messages
- )
- return response.choices[0].message.content
+ try:
+ response = client.chat.completions.create(
+ model=model,
+ messages=prompt_messages
+ )
+ return response.choices[0].message.content
+ except Exception as e:
+ print(f"Error calling Groq API: {e}")
+ return "Sorry, I encountered an error processing your request."🤖 Prompt for AI Agents
In examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb around lines 91 to 96,
the run_groq_chat function lacks error handling for API failures, rate limiting,
or network issues. Wrap the API call in a try-except block to catch exceptions,
handle them gracefully by logging or printing an error message, and optionally
retry or return a default response to prevent the notebook from crashing
unexpectedly.
| "from groq import Groq\n", | ||
| "\n", | ||
| "# Set API key (replace with your own or use environment variables)\n", | ||
| "os.environ['GROQ_API_KEY'] = 'enter your key'\n", |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Improve API key security guidance.
The hardcoded placeholder could lead users to accidentally commit real API keys. Provide better guidance for secure API key management.
Replace with more secure guidance:
-os.environ['GROQ_API_KEY'] = 'enter your key'
+# Set your API key securely (never commit real keys!)
+# Option 1: Set environment variable before running
+# export GROQ_API_KEY='your_api_key_here'
+
+# Option 2: Use Google Colab secrets (recommended for Colab)
+# from google.colab import userdata
+# os.environ['GROQ_API_KEY'] = userdata.get('GROQ_API_KEY')
+
+# Option 3: Set directly (use only for testing, never commit)
+# os.environ['GROQ_API_KEY'] = 'your_api_key_here'📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "os.environ['GROQ_API_KEY'] = 'enter your key'\n", | |
| # Set your API key securely (never commit real keys!) | |
| # Option 1: Set environment variable before running | |
| # export GROQ_API_KEY='your_api_key_here' | |
| # | |
| # Option 2: Use Google Colab secrets (recommended for Colab) | |
| # from google.colab import userdata | |
| # os.environ['GROQ_API_KEY'] = userdata.get('GROQ_API_KEY') | |
| # | |
| # Option 3: Set directly (use only for testing, never commit) | |
| # os.environ['GROQ_API_KEY'] = 'your_api_key_here' |
🤖 Prompt for AI Agents
In examples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb at line 86, the code
currently hardcodes a placeholder API key in the environment variable
assignment, which risks accidental commits of real keys. Replace this line with
a comment advising users to set their GROQ_API_KEY securely outside the code,
such as through environment variables or secure vaults, and remove any hardcoded
keys from the notebook.
There was a problem hiding this comment.
Actionable comments posted: 4
🧹 Nitpick comments (1)
examples/cookbooks/MistralTechAgent.ipynb (1)
591-597: Enhance test to demonstrate prompt template usage.The test doesn't utilize the prompt template defined earlier, missing an opportunity to demonstrate structured prompting.
Consider enhancing the test to show the prompt template in action:
agent = MistralTechAgent(model, tokenizer) -prompt = "You are an AI agent helping with technical queries. Explain what a language model is." +# Using the prompt template structure +system_prompt = prompt_template["content"] +user_query = "Explain what a language model is." +formatted_prompt = f"{system_prompt}\n\nUser: {user_query}\nAssistant:" -response = agent.chat(prompt) +response = agent.chat(formatted_prompt) print("=== Agent Response ===") print(response) +# Additional test cases +print("\n=== Testing with different queries ===") +for query in ["What is transformer architecture?", "How does attention mechanism work?"]: + test_prompt = f"{system_prompt}\n\nUser: {query}\nAssistant:" + print(f"\nQuery: {query}") + print(f"Response: {agent.chat(test_prompt, max_new_tokens=150)}")
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| " def chat(self, prompt: str, max_new_tokens=256) -> str:\n", | ||
| " inputs = self.tokenizer(prompt, return_tensors=\"pt\").to(self.model.device)\n", | ||
| " with torch.no_grad():\n", | ||
| " outputs = self.model.generate(\n", | ||
| " **inputs,\n", | ||
| " max_new_tokens=max_new_tokens,\n", | ||
| " do_sample=False, # DETERMINISTIC output\n", | ||
| " temperature=1.0,\n", | ||
| " top_p=1.0,\n", | ||
| " pad_token_id=self.tokenizer.eos_token_id\n", | ||
| " )\n", | ||
| " full_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)\n", | ||
| " return full_output[len(prompt):].strip()\n" |
There was a problem hiding this comment.
Fix contradictory generation parameters and add error handling.
The generation parameters are contradictory: do_sample=False (deterministic) conflicts with temperature=1.0 and top_p=1.0 (sampling parameters).
Apply this diff to fix the generation logic and add error handling:
def chat(self, prompt: str, max_new_tokens=256) -> str:
+ try:
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_new_tokens=max_new_tokens,
- do_sample=False, # DETERMINISTIC output
- temperature=1.0,
- top_p=1.0,
+ do_sample=True,
+ temperature=0.7,
+ top_p=0.9,
pad_token_id=self.tokenizer.eos_token_id
)
full_output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return full_output[len(prompt):].strip()
+ except Exception as e:
+ return f"Error generating response: {str(e)}"For truly deterministic output, use:
do_sample=False,
# Remove temperature and top_p parameters🤖 Prompt for AI Agents
In examples/cookbooks/MistralTechAgent.ipynb around lines 539 to 551, the
generation parameters in the chat method are contradictory because do_sample is
set to False (deterministic) while temperature and top_p are set for sampling.
To fix this, remove the temperature and top_p parameters when do_sample is False
to ensure deterministic output. Additionally, add error handling around the
model.generate call to catch and log any exceptions that may occur during
generation.
| "prompt_template = {\n", | ||
| " \"role\": \"system\",\n", | ||
| " \"content\": \"\"\"You are MistralTechAgent, an expert AI model assistant.\n", | ||
| "You provide clear, concise, and technically correct answers.\"\"\",\n", | ||
| " \"user_prompt\": \"{{ user_input }}\"\n", | ||
| "}\n" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Misleading section title and unused prompt template.
The section is titled "YAML Prompt" but defines a Python dictionary, not YAML. More importantly, this template is never used in the actual implementation.
Either integrate the template into the agent's chat method or remove it. To use it properly:
+def format_prompt(self, user_input: str) -> str:
+ system_content = prompt_template["content"]
+ return f"{system_content}\n\nUser: {user_input}\nAssistant:"And update the chat method to use formatted_prompt = self.format_prompt(prompt).
Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In examples/cookbooks/MistralTechAgent.ipynb around lines 131 to 136, the
section titled "YAML Prompt" incorrectly defines a Python dictionary named
prompt_template that is never used. To fix this, either remove the unused
prompt_template dictionary and rename the section to reflect its content
accurately, or integrate this prompt_template into the agent's chat method by
formatting the prompt with self.format_prompt(prompt) and using the resulting
formatted_prompt in the chat logic.
| "import os\n", | ||
| "os.environ[\"HF_TOKEN\"] = \"Enter your hugging api key\"\n", | ||
| "os.environ[\"TRANSFORMERS_CACHE\"] = \"/content/hf_cache\" # Optional: Faster repeat runs\n" |
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Security risk: Hardcoded API token and deprecated environment variable.
The hardcoded placeholder token will cause authentication failures, and users might accidentally commit real tokens to version control. Additionally, TRANSFORMERS_CACHE is deprecated.
Apply this diff to improve security and use current environment variables:
-os.environ["HF_TOKEN"] = "Enter your hugging api key"
-os.environ["TRANSFORMERS_CACHE"] = "/content/hf_cache" # Optional: Faster repeat runs
+# Set your Hugging Face token (recommended: use environment variable or getpass)
+import getpass
+if "HF_TOKEN" not in os.environ:
+ os.environ["HF_TOKEN"] = getpass.getpass("Enter your Hugging Face API token: ")
+os.environ["HF_HOME"] = "/content/hf_cache" # Updated from deprecated TRANSFORMERS_CACHE📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| "import os\n", | |
| "os.environ[\"HF_TOKEN\"] = \"Enter your hugging api key\"\n", | |
| "os.environ[\"TRANSFORMERS_CACHE\"] = \"/content/hf_cache\" # Optional: Faster repeat runs\n" | |
| import os | |
| # Set your Hugging Face token (recommended: use environment variable or getpass) | |
| import getpass | |
| if "HF_TOKEN" not in os.environ: | |
| os.environ["HF_TOKEN"] = getpass.getpass("Enter your Hugging Face API token: ") | |
| os.environ["HF_HOME"] = "/content/hf_cache" # Updated from deprecated TRANSFORMERS_CACHE |
🤖 Prompt for AI Agents
In examples/cookbooks/MistralTechAgent.ipynb around lines 107 to 109, avoid
hardcoding the HF_TOKEN directly in the code to prevent security risks and
accidental commits of real tokens. Instead, instruct users to set the HF_TOKEN
in their environment outside the notebook. Also, replace the deprecated
TRANSFORMERS_CACHE environment variable with the current recommended variable
for caching transformers models. Update the code to reflect these changes
accordingly.
| " tokenizer = AutoTokenizer.from_pretrained(\n", | ||
| " model_name, use_auth_token=os.environ[\"HF_TOKEN\"], trust_remote_code=True\n", | ||
| " )\n", | ||
| " model = AutoModelForCausalLM.from_pretrained(\n", | ||
| " model_name,\n", | ||
| " use_auth_token=os.environ[\"HF_TOKEN\"],\n", | ||
| " trust_remote_code=True,\n", | ||
| " device_map=\"auto\",\n", | ||
| " torch_dtype=torch.float16\n", | ||
| " )\n", | ||
| " model.eval()\n", | ||
| " print(\"✅ Model loaded successfully!\")\n", | ||
| "\n", | ||
| "except Exception as e:\n", | ||
| " print(f\"❌ Failed to load {model_name}\\nError: {e}\")\n", | ||
| " model_name = \"distilgpt2\"\n", | ||
| " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", | ||
| " model = AutoModelForCausalLM.from_pretrained(model_name)\n", | ||
| " model.eval()\n" |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Update deprecated parameters and improve fallback handling.
The code uses deprecated use_auth_token parameter and has compatibility issues in the fallback logic.
Apply this diff to fix deprecated parameters and improve fallback:
tokenizer = AutoTokenizer.from_pretrained(
- model_name, use_auth_token=os.environ["HF_TOKEN"], trust_remote_code=True
+ model_name, token=os.environ["HF_TOKEN"]
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
- use_auth_token=os.environ["HF_TOKEN"],
- trust_remote_code=True,
+ token=os.environ["HF_TOKEN"],
device_map="auto",
torch_dtype=torch.float16
)
model.eval()
print("✅ Model loaded successfully!")
except Exception as e:
print(f"❌ Failed to load {model_name}\\nError: {e}")
model_name = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
+ # Add padding token for distilgpt2 compatibility
+ if tokenizer.pad_token is None:
+ tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
+ print(f"✅ Fallback model {model_name} loaded successfully!")Security note: Removed trust_remote_code=True as it can execute arbitrary code from the model repository.
📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| " tokenizer = AutoTokenizer.from_pretrained(\n", | |
| " model_name, use_auth_token=os.environ[\"HF_TOKEN\"], trust_remote_code=True\n", | |
| " )\n", | |
| " model = AutoModelForCausalLM.from_pretrained(\n", | |
| " model_name,\n", | |
| " use_auth_token=os.environ[\"HF_TOKEN\"],\n", | |
| " trust_remote_code=True,\n", | |
| " device_map=\"auto\",\n", | |
| " torch_dtype=torch.float16\n", | |
| " )\n", | |
| " model.eval()\n", | |
| " print(\"✅ Model loaded successfully!\")\n", | |
| "\n", | |
| "except Exception as e:\n", | |
| " print(f\"❌ Failed to load {model_name}\\nError: {e}\")\n", | |
| " model_name = \"distilgpt2\"\n", | |
| " tokenizer = AutoTokenizer.from_pretrained(model_name)\n", | |
| " model = AutoModelForCausalLM.from_pretrained(model_name)\n", | |
| " model.eval()\n" | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| model_name, token=os.environ["HF_TOKEN"] | |
| ) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_name, | |
| token=os.environ["HF_TOKEN"], | |
| device_map="auto", | |
| torch_dtype=torch.float16 | |
| ) | |
| model.eval() | |
| print("✅ Model loaded successfully!") | |
| except Exception as e: | |
| print(f"❌ Failed to load {model_name}\nError: {e}") | |
| model_name = "distilgpt2" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| # Add padding token for distilgpt2 compatibility | |
| if tokenizer.pad_token is None: | |
| tokenizer.pad_token = tokenizer.eos_token | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| model.eval() | |
| print(f"✅ Fallback model {model_name} loaded successfully!") |
🤖 Prompt for AI Agents
In examples/cookbooks/MistralTechAgent.ipynb around lines 493 to 511, the code
uses the deprecated use_auth_token parameter and includes
trust_remote_code=True, which poses security risks. Replace
use_auth_token=os.environ["HF_TOKEN"] with the updated token parameter, remove
trust_remote_code=True entirely, and improve the fallback logic by ensuring the
fallback model loads without deprecated parameters and handles exceptions
gracefully.
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #625 +/- ##
==========================================
- Coverage 16.43% 0.00% -16.44%
==========================================
Files 24 22 -2
Lines 2160 1980 -180
Branches 302 0 -302
==========================================
- Hits 355 0 -355
- Misses 1789 1980 +191
+ Partials 16 0 -16
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User description
MistralTechAgent is a simple and efficient AI assistant powered by the Mistral-7B-Instruct model. It is designed to provide accurate and easy-to-understand answers to technical questions. This agent uses a lightweight instruction-tuned model that runs smoothly on limited hardware while delivering high-quality responses. It also supports a customizable prompt format for structured queries.
PR Type
enhancement, documentation
Description
Added a new Jupyter notebook for a Groq Llama3-8b-8192 AI assistant agent.
Demonstrates structured agent setup with YAML config and prompt templates.
Provides step-by-step instructions, code, and example output for Groq model usage.
Includes Colab integration and detailed markdown explanations.
Changes walkthrough 📝
Groq_LPU_Powered_AI_Assistant.ipynb
Add Groq Llama3-8b-8192 agent example notebook with YAML configexamples/cookbooks/Groq_LPU_Powered_AI_Assistant.ipynb
MistralTechAgent.ipynb
Add Mistral-7B-Instruct technical assistant notebookexamples/cookbooks/MistralTechAgent.ipynb
agent.
Summary by CodeRabbit