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| Property | Value |
|---|---|
| Parameters | 73.6M |
| Architecture | Qwen2ForCausalLM |
| Hidden size | 384 |
| Layers | 12 |
| Attention heads | 6 (2 KV heads, GQA 3:1) |
| Intermediate size | 768 |
| Context length | 2048 |
| Vocab size | 151,671 |

| Metric | Start | End |
|---|---|---|
| Train Loss | 12.0 | 2.4 |
| Val Loss | 6.5 | 2.6 |
inference_smolagent.py for full agent setup with LocalPythonExecutor and tools.1from inference_smolagent import create_agent, CalculatorTool, FibonacciTool
2
3agent = create_agent(
4 model_id="AutomatedScientist/pynb-73m-base",
5 tools=[CalculatorTool(), FibonacciTool()],
6 max_steps=5,
7)
8
9result = agent.run("Calculate 15 * 7 + 23")
10print(result)1from smolagents import CodeAgent, HfApiModel
2
3model = HfApiModel(model_id="AutomatedScientist/pynb-73m-base")
4agent = CodeAgent(tools=[], model=model)
5
6result = agent.run("Calculate the sum of numbers from 1 to 100")
7print(result)1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4model_id = "AutomatedScientist/pynb-73m-base" # or "checkpoint" for local
5tokenizer = AutoTokenizer.from_pretrained(model_id)
6model = AutoModelForCausalLM.from_pretrained(
7 model_id,
8 torch_dtype=torch.bfloat16,
9 device_map="auto"
10)
11
12prompt = "Write a function to calculate fibonacci numbers"
13inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
14outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
15print(tokenizer.decode(outputs[0], skip_special_tokens=False))inference.py for a wrapper class:1from inference import CodeModel
2
3model = CodeModel("AutomatedScientist/pynb-73m-base")
4result = model.generate("Write a function to sort a list")
5print(result)pip install torch transformers smolagents