[EXAMPLES][Jamba ++ Command R Tool Agent]

playground/agents/command_r_tool_agent.py

@@ -0,0 +1,59 @@
+from pydantic import BaseModel, Field
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from swarms import ToolAgent
+from swarms.utils.json_utils import base_model_to_json
+
+# Model name
+model_name = "CohereForAI/c4ai-command-r-v01-4bit"
+
+# Load the pre-trained model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+)
+
+# Load the pre-trained model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Initialize the schema for the person's information
+class APIExampleRequestSchema(BaseModel):
+    endpoint: str = Field(
+        ..., description="The API endpoint for the example request"
+    )
+    method: str = Field(
+        ..., description="The HTTP method for the example request"
+    )
+    headers: dict = Field(
+        ..., description="The headers for the example request"
+    )
+    body: dict = Field(
+        ..., description="The body of the example request"
+    )
+    response: dict = Field(
+        ..., description="The expected response of the example request"
+    )
+
+# Convert the schema to a JSON string
+api_example_schema = base_model_to_json(APIExampleRequestSchema)
+# Convert the schema to a JSON string
+
+# Define the task to generate a person's information
+task = (
+    "Generate an example API request using this code:\n"
+)
+
+# Create an instance of the ToolAgent class
+agent = ToolAgent(
+    name="Command R Tool Agent",
+    description="An agent that generates an API request using the Command R model.",
+    model=model,
+    tokenizer=tokenizer,
+    json_schema=api_example_schema,
+)
+
+# Run the agent to generate the person's information
+generated_data = agent.run(task)
+
+# Print the generated data
+print(f"Generated data: {generated_data}")

playground/agents/jamba_tool_agent.py

@@ -0,0 +1,59 @@
+from pydantic import BaseModel, Field
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from swarms import ToolAgent
+from swarms.utils.json_utils import base_model_to_json
+
+# Model name
+model_name = "ai21labs/Jamba-v0.1"
+
+# Load the pre-trained model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",
+)
+
+# Load the pre-trained model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Initialize the schema for the person's information
+class APIExampleRequestSchema(BaseModel):
+    endpoint: str = Field(
+        ..., description="The API endpoint for the example request"
+    )
+    method: str = Field(
+        ..., description="The HTTP method for the example request"
+    )
+    headers: dict = Field(
+        ..., description="The headers for the example request"
+    )
+    body: dict = Field(
+        ..., description="The body of the example request"
+    )
+    response: dict = Field(
+        ..., description="The expected response of the example request"
+    )
+
+# Convert the schema to a JSON string
+api_example_schema = base_model_to_json(APIExampleRequestSchema)
+# Convert the schema to a JSON string
+
+# Define the task to generate a person's information
+task = (
+    "Generate an example API request using this code:\n"
+)
+
+# Create an instance of the ToolAgent class
+agent = ToolAgent(
+    name="Command R Tool Agent",
+    description="An agent that generates an API request using the Command R model.",
+    model=model,
+    tokenizer=tokenizer,
+    json_schema=api_example_schema,
+)
+
+# Run the agent to generate the person's information
+generated_data = agent(task)
+
+# Print the generated data
+print(f"Generated data: {generated_data}")

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "swarms"
-version = "4.7.4"
+version = "4.7.8"
 description = "Swarms - Pytorch"
 license = "MIT"
 authors = ["Kye Gomez <kye@apac.ai>"]
@@ -44,6 +44,8 @@ Pillow = "10.2.0"
 rich = "13.5.2"
 psutil = "*"
 sentry-sdk = "*"
+python-dotenv = "*"
+accelerate = "0.28.0"
 
 [tool.poetry.dev-dependencies]
 black = "23.3.0"

swarms/utils/__init__.py

@@ -40,7 +40,7 @@ from swarms.utils.remove_json_whitespace import (
     remove_whitespace_from_yaml,
 )
 from swarms.utils.save_logs import parse_log_file
-from swarms.utils.supervision_visualizer import MarkVisualizer
+# from swarms.utils.supervision_visualizer import MarkVisualizer
 from swarms.utils.try_except_wrapper import try_except_wrapper
 from swarms.utils.yaml_output_parser import YamlOutputParser
 from swarms.utils.concurrent_utils import execute_concurrently
@@ -77,8 +77,6 @@ __all__ = [
     "remove_whitespace_from_json",
     "remove_whitespace_from_yaml",
     "parse_log_file",
-    "MarkVisualizer",
-    # "limit_tokens_from_string",
     "try_except_wrapper",
     "YamlOutputParser",
     "execute_concurrently",