Instructions to use satejh/gemma2-2b-technical-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use satejh/gemma2-2b-technical-assistant with Transformers:

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

pipe = pipeline("text-generation", model="satejh/gemma2-2b-technical-assistant")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("satejh/gemma2-2b-technical-assistant")
model = AutoModelForCausalLM.from_pretrained("satejh/gemma2-2b-technical-assistant", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use satejh/gemma2-2b-technical-assistant with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf satejh/gemma2-2b-technical-assistant:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf satejh/gemma2-2b-technical-assistant:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf satejh/gemma2-2b-technical-assistant:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf satejh/gemma2-2b-technical-assistant:Q4_K_M

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 satejh/gemma2-2b-technical-assistant:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf satejh/gemma2-2b-technical-assistant:Q4_K_M

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 satejh/gemma2-2b-technical-assistant:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf satejh/gemma2-2b-technical-assistant:Q4_K_M

Use Docker

docker model run hf.co/satejh/gemma2-2b-technical-assistant:Q4_K_M

LM Studio
Jan

vLLM

How to use satejh/gemma2-2b-technical-assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "satejh/gemma2-2b-technical-assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "satejh/gemma2-2b-technical-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/satejh/gemma2-2b-technical-assistant:Q4_K_M

SGLang

How to use satejh/gemma2-2b-technical-assistant 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 "satejh/gemma2-2b-technical-assistant" \
    --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": "satejh/gemma2-2b-technical-assistant",
		"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 "satejh/gemma2-2b-technical-assistant" \
        --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": "satejh/gemma2-2b-technical-assistant",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use satejh/gemma2-2b-technical-assistant with Ollama:
```
ollama run hf.co/satejh/gemma2-2b-technical-assistant:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use satejh/gemma2-2b-technical-assistant with Docker Model Runner:
```
docker model run hf.co/satejh/gemma2-2b-technical-assistant:Q4_K_M
```

Lemonade

How to use satejh/gemma2-2b-technical-assistant with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull satejh/gemma2-2b-technical-assistant:Q4_K_M

Run and chat with the model

lemonade run user.gemma2-2b-technical-assistant-Q4_K_M

List all available models

lemonade list

Atomic Chat

gemma2-2b-technical-assistant

Fine-tuned Gemma 2 2B IT model for personalized technical assistance.

Model Description

This model is a QLoRA fine-tuned version of google/gemma-2-2b-it, specialized for:

AWS cloud security guidance
FastAPI/Python backend development
Finance application development
Kubernetes workload management
ISO 27001:2022 compliance

Training Details

Base Model: google/gemma-2-2b-it
Fine-tuning Method: QLoRA (4-bit quantization)
LoRA Rank: 16
LoRA Alpha: 32
Training Epochs: 5
Hardware: Google Colab T4 GPU

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("satejh/gemma2-2b-technical-assistant")
tokenizer = AutoTokenizer.from_pretrained("satejh/gemma2-2b-technical-assistant")

prompt = "<start_of_turn>user\nWhat database should I use?<end_of_turn>\n<start_of_turn>model\n"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

Ollama

Download the GGUF file and Modelfile from this repo, then:

ollama create gemma2-2b-technical-assistant -f Modelfile
ollama run gemma2-2b-technical-assistant

Intended Use

This model is designed as a personalized technical assistant with:

Security-first approach
Read-only database interactions
Direct, actionable responses
AWS and Kubernetes expertise

Downloads last month: 43

Safetensors

Model size

3B params

Tensor type

F16

Model tree for satejh/gemma2-2b-technical-assistant

Base model

google/gemma-2-2b

Finetuned

google/gemma-2-2b-it

Quantized

(206)

this model