Instructions to use Kiy-K/fyodor-agentic-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kiy-K/fyodor-agentic-v1.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Kiy-K/fyodor-agentic-v1.1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kiy-K/fyodor-agentic-v1.1", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Kiy-K/fyodor-agentic-v1.1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Kiy-K/fyodor-agentic-v1.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kiy-K/fyodor-agentic-v1.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kiy-K/fyodor-agentic-v1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kiy-K/fyodor-agentic-v1.1

SGLang

How to use Kiy-K/fyodor-agentic-v1.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Kiy-K/fyodor-agentic-v1.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kiy-K/fyodor-agentic-v1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Kiy-K/fyodor-agentic-v1.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kiy-K/fyodor-agentic-v1.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kiy-K/fyodor-agentic-v1.1 with Docker Model Runner:
```
docker model run hf.co/Kiy-K/fyodor-agentic-v1.1
```

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
)