Instructions to use taher07/gemma_3_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use taher07/gemma_3_finetune with Transformers:

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

pipe = pipeline("text-generation", model="taher07/gemma_3_finetune")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("taher07/gemma_3_finetune")
model = AutoModelForCausalLM.from_pretrained("taher07/gemma_3_finetune")
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]:]))

llama-cpp-python

How to use taher07/gemma_3_finetune with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="taher07/gemma_3_finetune",
	filename="gemma-3-270m-it.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use taher07/gemma_3_finetune with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf taher07/gemma_3_finetune:Q8_0
# Run inference directly in the terminal:
llama-cli -hf taher07/gemma_3_finetune:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf taher07/gemma_3_finetune:Q8_0
# Run inference directly in the terminal:
llama-cli -hf taher07/gemma_3_finetune:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf taher07/gemma_3_finetune:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf taher07/gemma_3_finetune:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf taher07/gemma_3_finetune:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf taher07/gemma_3_finetune:Q8_0

Use Docker

docker model run hf.co/taher07/gemma_3_finetune:Q8_0

LM Studio
Jan

vLLM

How to use taher07/gemma_3_finetune with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "taher07/gemma_3_finetune"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "taher07/gemma_3_finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/taher07/gemma_3_finetune:Q8_0

SGLang

How to use taher07/gemma_3_finetune 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 "taher07/gemma_3_finetune" \
    --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": "taher07/gemma_3_finetune",
		"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 "taher07/gemma_3_finetune" \
        --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": "taher07/gemma_3_finetune",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use taher07/gemma_3_finetune with Ollama:
```
ollama run hf.co/taher07/gemma_3_finetune:Q8_0
```

Unsloth Studio new

How to use taher07/gemma_3_finetune 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 taher07/gemma_3_finetune 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 taher07/gemma_3_finetune to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for taher07/gemma_3_finetune to start chatting

Docker Model Runner
How to use taher07/gemma_3_finetune with Docker Model Runner:
```
docker model run hf.co/taher07/gemma_3_finetune:Q8_0
```

Lemonade

How to use taher07/gemma_3_finetune with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull taher07/gemma_3_finetune:Q8_0

Run and chat with the model

lemonade run user.gemma_3_finetune-Q8_0

List all available models

lemonade list

Taher commited on Feb 8

Commit

d8dcfe1

verified ·

1 Parent(s): f81166b

Unsloth Model Card

Browse files

Files changed (1) hide show

README.md +12 -17

README.md CHANGED Viewed

@@ -1,26 +1,21 @@
 ---
 tags:
-- gguf
-- llama.cpp
 - unsloth
 ---
-# gemma_3_finetune : GGUF
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
-**Example usage**:
-- For text only LLMs:    `./llama.cpp/llama-cli -hf skylor07/gemma_3_finetune --jinja`
-- For multimodal models: `./llama.cpp/llama-mtmd-cli -hf skylor07/gemma_3_finetune --jinja`
-## Available Model files:
-- `gemma-3-270m-it.Q8_0.gguf`
-## Ollama
-An Ollama Modelfile is included for easy deployment.
-## Note
-The model's BOS token behavior was adjusted for GGUF compatibility.
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 ---
+base_model: unsloth/gemma-3-270m-it
 tags:
+- text-generation-inference
+- transformers
 - unsloth
+- gemma3_text
+license: apache-2.0
+language:
+- en
 ---
+# Uploaded finetuned  model
+- **Developed by:** skylor07
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/gemma-3-270m-it
+This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)