Fyodor Agentic v1.1

A Mixture-of-Experts (MoE) enhanced version of Qwen2.5-Coder-3B-Instruct, optimized for agentic AI workflows and function calling.

Model Details

Base Model: Qwen/Qwen2.5-Coder-3B-Instruct
Architecture: Sparse MoE with 4 experts, top-2 routing
Parameters: 6-7B total (~3B base + ~3-4B MoE experts)
Training: Function calling, Python code, conversational data
Format: SafeTensors (ready to use!)

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    "Kiy-K/fyodor-agentic-v1.1",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

tokenizer = AutoTokenizer.from_pretrained(
    "Kiy-K/fyodor-agentic-v1.1",
    trust_remote_code=True
)

# Generate
prompt = "Write a Python function to calculate Fibonacci numbers:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Use Cases

Function Calling: Built for tool usage and API interactions
Code Generation: Python, JavaScript, and more
Agentic Workflows: Multi-step reasoning and planning
Conversational AI: Natural multi-turn dialogue
Instruction Following: Clear and precise responses

Training Details

Total Steps: 5000
Final Loss: 5.5744
Best Loss: 5.5627
Training Time: 0.31h
Platform: Lightning.AI A100

Training Data:

xLAM Function Calling: 5,000 samples
Python Code: 5,000 samples
UltraChat: 5,000 samples

Architecture

Sparse MoE implementation:

MoE layers added every 3 transformer layers
4 experts per layer
Top-2 routing (2 experts active per token)
Load balancing for efficient utilization
Base model frozen, MoE trained

Tips

Temperature: 0.7-0.8 for creative, 0.3-0.5 for precise
Context Window: 2048 tokens
Batch Size: Supports efficient batch inference
Precision: Works with fp16/bf16/fp32

Advanced Usage

# With custom generation config
outputs = model.generate(
    **inputs,
    max_new_tokens=1024,
    temperature=0.8,
    top_p=0.95,
    top_k=50,
    repetition_penalty=1.1,
    do_sample=True
)