Instructions to use codertrish/gemma3-270m-chess-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codertrish/gemma3-270m-chess-lora with Transformers:

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

pipe = pipeline("text-generation", model="codertrish/gemma3-270m-chess-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("codertrish/gemma3-270m-chess-lora", dtype="auto")

PEFT
How to use codertrish/gemma3-270m-chess-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use codertrish/gemma3-270m-chess-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codertrish/gemma3-270m-chess-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "codertrish/gemma3-270m-chess-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/codertrish/gemma3-270m-chess-lora

SGLang

How to use codertrish/gemma3-270m-chess-lora 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 "codertrish/gemma3-270m-chess-lora" \
    --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": "codertrish/gemma3-270m-chess-lora",
		"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 "codertrish/gemma3-270m-chess-lora" \
        --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": "codertrish/gemma3-270m-chess-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use codertrish/gemma3-270m-chess-lora with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for codertrish/gemma3-270m-chess-lora to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for codertrish/gemma3-270m-chess-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for codertrish/gemma3-270m-chess-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="codertrish/gemma3-270m-chess-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use codertrish/gemma3-270m-chess-lora with Docker Model Runner:
```
docker model run hf.co/codertrish/gemma3-270m-chess-lora
```

Gemma-3 270M — Chess Coach (LoRA Adapters)

Author: @codertrish
Base model: unsloth/gemma-3-270m-it
Type: LoRA adapters (attach to base at load-time)
Task: Conversational chess tutoring (rules, openings, beginner tactics)

This repo contains only the LoRA adapter weights (ΔW). You must also load the base model and then attach these adapters to reproduce the fine-tuned behavior.

✨ Intended Use

Direct use: Teach or explain beginner chess concepts, opening principles, and simple tactics in plain English.
Downstream use: As a lightweight add-on for apps where distributing full weights isn’t desired or allowed.

Out-of-scope: Engine-grade move calculation or authoritative evaluations of complex positions. For strong analysis, pair with a chess engine (e.g., Stockfish).

🔧 How to Use (attach adapters)

Option A — Unsloth (simplest)

# pip install "unsloth[torch]" transformers peft accelerate bitsandbytes sentencepiece

from unsloth import FastModel
from unsloth.chat_templates import get_chat_template

BASE   = "unsloth/gemma-3-270m-it"                 # base checkpoint
ADAPTER= "codertrish/gemma3-270m-chess-lora"       # this repo

model, tok = FastModel.from_pretrained(
    BASE, max_seq_length=2048, load_in_4bit=True, full_finetuning=False
)
tok = get_chat_template(tok, "gemma3")             # Gemma-3 chat formatting
model.load_adapter(ADAPTER)                        # <-- attach LoRA

messages = [
  {"role":"system","content":"You are a helpful chess coach. Answer in plain text."},
  {"role":"user","content":"List 3 opening principles for beginners."},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
out = model.generate(**tok([prompt], return_tensors="pt").to(model.device),
                     max_new_tokens=200, do_sample=False)
print(tok.decode(out[0], skip_special_tokens=True))

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for codertrish/gemma3-270m-chess-lora

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it

Adapter

(26)

this model

codertrish
/

gemma3-270m-chess-lora

Gemma-3 270M — Chess Coach (LoRA Adapters)

✨ Intended Use

🔧 How to Use (attach adapters)

Option A — Unsloth (simplest)

Model tree for codertrish/gemma3-270m-chess-lora

Dataset used to train codertrish/gemma3-270m-chess-lora