To connect an agent to a small experimental language model you've created, you'll need to establish an interface between the two. Here's a high-level breakdown of the steps, followed by more detailed options depending on your setup.

• Agent: A system that sends prompts/queries and handles responses (e.g., a chatbot, script, or tool).

• Model: Your experimental language model, likely a trained model (e.g., a small transformer or RNN).

• Interface: The way the agent communicates with the model (API, function call, socket, etc.).

If your model is just a Python object or function:

python

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# Define your model as a callable function
def my_model(prompt):
    # Your generation logic here
    return "This is a response to: " + prompt
# Your agent uses this function
def agent():
user_input = input("You: ")
response = my_model(user_input)
print("Agent:", response)
agent()

Use this if your model runs in the same Python environment.

Wrap your model in a small web API using something like FastAPI or Flask:

python

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from fastapi import FastAPI, Request
import uvicorn
app = FastAPI()
@app.post("/generate")
async def generate(request: Request):
data = await request.json()
prompt = data["prompt"]
# Call your model here
result = your_model(prompt)
return {"response": result}
if name == "main":
uvicorn.run(app, host="0.0.0.0", port=8000)

Your agent can then send HTTP POST requests to http://localhost:8000/generate.

If your model runs in a separate process (e.g., it's a compiled binary or separate script), use subprocess:

python

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import subprocess
def call_model(prompt):
result = subprocess.run(["python", "my_model.py"], input=prompt.encode(), capture_output=True)
return result.stdout.decode()
print(call_model("What is AI?"))

For more robust setups or real-time systems, use sockets (TCP/UDP). Useful when model and agent run on different machines.

If you’re building agents with frameworks like LangChain, you can register your model as a custom LLM wrapper class.

python

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from langchain.llms.base import LLM
class MyCustomModel(LLM):
def _call(self, prompt, stop=None):
return your_model(prompt)
# Then use it in LangChain tools or chains

If you tell me more about:

• What framework or model you're using (PyTorch? Hugging Face? Something custom?)

• What kind of agent you're building (chatbot? CLI? web?)

• Whether you want to run it locally or expose it as a service

…I can give a more tailored answer or code snippet.