Instructions to use SykoSLM/SykoLLM-V1-Turkish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SykoSLM/SykoLLM-V1-Turkish with Transformers:

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

pipe = pipeline("text-generation", model="SykoSLM/SykoLLM-V1-Turkish")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SykoSLM/SykoLLM-V1-Turkish")
model = AutoModelForCausalLM.from_pretrained("SykoSLM/SykoLLM-V1-Turkish")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SykoSLM/SykoLLM-V1-Turkish with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SykoSLM/SykoLLM-V1-Turkish"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SykoSLM/SykoLLM-V1-Turkish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SykoSLM/SykoLLM-V1-Turkish

SGLang

How to use SykoSLM/SykoLLM-V1-Turkish 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 "SykoSLM/SykoLLM-V1-Turkish" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SykoSLM/SykoLLM-V1-Turkish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "SykoSLM/SykoLLM-V1-Turkish" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SykoSLM/SykoLLM-V1-Turkish",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SykoSLM/SykoLLM-V1-Turkish with Docker Model Runner:
```
docker model run hf.co/SykoSLM/SykoLLM-V1-Turkish
```

Model Card: SykoLLM-V1-Turkish

SykoLLM-V1-Turkish is a custom-architected, lightweight Large Language Model (LLM) designed specifically for Turkish conversational tasks. Unlike standard pre-built models, this version features a custom configuration optimized for speed and efficiency in low-resource environments.

Model Description

Developed by: syko818121
Model Name: SykoLLM-V1-Turkish
Model Type: Causal Decoder-Only Custom Architecture
Language: Turkish
Parameters: ~50.8 Million
Training Data: Turkish Wikipedia + Custom High-Quality Chat Dataset

Architectural Specs

This model uses a custom configuration designed for Turkish linguistics:

Vocabulary Size: 50,257
Hidden Dimension (n_embd): 512
Number of Layers: 8
Attention Heads: 8
Context Window: 512 tokens

Fine-Tuning & Conversation Style

The model was fine-tuned on a high-quality, curated Turkish dataset to ensure natural, human-like responses. The training data distribution was carefully balanced:

* Greetings & Daily Talk (40%): Natural openings and casual conversation.

* Direct Question-Answering (30%): Short and concise answers to general knowledge queries.

* Brief Explanations (20%): Simplified definitions for complex concepts.

* Slang & Short Inputs (10%): Robustness against one-word or incomplete messages.

Usage

You can load and test SykoLLM-V1-Turkish using the following snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "syko818121/SykoLLM-V1-Turkish"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)

prompt = "<user> Selam, naber?<assistant>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50, pad_token_id=tokenizer.eos_token_id)

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

Training Configuration

Learning Rate: 5e-5
Scheduler: Cosine
Epochs: 15
Batch Size: 4
Precision: FP16 (Mixed Precision)

Limitations

Size: As a 50.8M parameter model, it is a "micro-LLM." It excels at short chats but may hallucinate on highly complex logical tasks.
Response Length: The model is intentionally biased toward concise and direct answers rather than long-form essays.

Downloads last month: 2

Safetensors

Model size

51.2M params

Tensor type

F32