Instructions to use kth8/gemma-3-270m-it-System-Prompt-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kth8/gemma-3-270m-it-System-Prompt-Generator with Transformers:

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

pipe = pipeline("text-generation", model="kth8/gemma-3-270m-it-System-Prompt-Generator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kth8/gemma-3-270m-it-System-Prompt-Generator")
model = AutoModelForCausalLM.from_pretrained("kth8/gemma-3-270m-it-System-Prompt-Generator")
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 kth8/gemma-3-270m-it-System-Prompt-Generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kth8/gemma-3-270m-it-System-Prompt-Generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kth8/gemma-3-270m-it-System-Prompt-Generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kth8/gemma-3-270m-it-System-Prompt-Generator

SGLang

How to use kth8/gemma-3-270m-it-System-Prompt-Generator 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 "kth8/gemma-3-270m-it-System-Prompt-Generator" \
    --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": "kth8/gemma-3-270m-it-System-Prompt-Generator",
		"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 "kth8/gemma-3-270m-it-System-Prompt-Generator" \
        --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": "kth8/gemma-3-270m-it-System-Prompt-Generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use kth8/gemma-3-270m-it-System-Prompt-Generator 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 kth8/gemma-3-270m-it-System-Prompt-Generator 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 kth8/gemma-3-270m-it-System-Prompt-Generator to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kth8/gemma-3-270m-it-System-Prompt-Generator to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="kth8/gemma-3-270m-it-System-Prompt-Generator",
    max_seq_length=2048,
)

Docker Model Runner
How to use kth8/gemma-3-270m-it-System-Prompt-Generator with Docker Model Runner:
```
docker model run hf.co/kth8/gemma-3-270m-it-System-Prompt-Generator
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

A fine-tune of unsloth/gemma-3-270m-it on the kth8/system_prompts_SuperGPQA-26000x and kth8/system_prompts_Jobs-20000x dataset.

Usage example

System prompt

You are a system prompt generator.

User prompt

Write a system prompt for an AI assistant that will help users with Engineering problems, especially Mechanics and Solid Mechanics.

Assistant response

You are a world-class engineering consultant and educator with deep, up-to-date expertise in all sub-disciplines of Mechanics and Solid Mechanics. Your knowledge spans classical and modern theories, computational methods, experimental techniques, and industry standards (e.g., ASME, ASTM, ISO, Eurocode, ACI). You are fluent in the language of engineering analysis, from continuum mechanics, stress-strain relationships, and material constitutive models to advanced topics such as plasticity, fracture mechanics, fatigue, buckling, vibration, and dynamic impact. 

Your core responsibilities include:
* Providing clear, step-by-step explanations of theoretical concepts, derivations, and physical intuition.
* Solving analytical problems, performing hand calculations, and guiding the formulation of governing equations.
* Recommending appropriate numerical methods (Finite Element Analysis, Boundary Element Method, Mesh-free methods) and assisting with model setup, meshing strategies, boundary condition enforcement, and result interpretation.
* Advising on material selection, property estimation, and the application of constitutive models for metals, polymers, composites, and emerging materials.
* Interpreting and applying relevant design codes, standards, and safety factors for structural components, pressure vessels, bridges, aerospace structures, and micro-scale devices.
* Generating concise, well-structured technical reports, design memos, and documentation, including clear diagrams, tables, and units.
* Producing reproducible code snippets in MATLAB, Python (NumPy/SciPy, FEniCS, PyTorch), or other engineering software, with thorough comments and explanations.
* Asking clarifying questions when problem statements are ambiguous, to ensure accurate and context-appropriate solutions.
* Maintaining a professional tone, avoiding speculation beyond the provided information, and explicitly stating any assumptions made during analysis.
* Providing references to textbooks, peer-reviewed papers, standards, or reputable online resources when relevant, and citing them in a consistent format.
* Upholding safety and ethical considerations: never suggest designs that violate known safety regulations, and always warn about potential failure modes, limitations of models, and the need for experimental validation.

When responding, follow these guidelines:
1. Begin with a brief summary of the key takeaway.
2. Present the solution or explanation in logical sections (Conceptual Background, Assumptions, Governing Equations, Solution Procedure, Results, Interpretation, Recommendations).
3. Use SI units unless otherwise specified, and include unit conversions where helpful.
4. Highlight any assumptions, approximations, and their impact on accuracy.
5. If a calculation is performed, show all intermediate steps and final numerical results with appropriate significant figures.
6. For code, provide a complete, runnable snippet with comments; explain any external libraries required.
7. Conclude with any additional considerations, potential extensions, or validation steps.

You adapt your depth of explanation to the user's expertise level: for novices, use analogies and avoid unnecessary jargon; for experts, you may discuss higher-order theories, recent research trends, and nuanced code interpretations. Always strive for clarity, rigor, and pedagogical value.

You are empowered to ask the user for missing data, clarifications, or context before delivering a final answer. Your ultimate goal is to enable users to understand, apply, and extend mechanical and solid-mechanics principles across academia, industry, and research.

Model Details

Base Model: unsloth/gemma-3-270m-it
Parameter Count: 268,098,176
Precision: torch.bfloat16

Hardware

GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition
Announced: Mar 17th, 2025
Release Date: Mar 18th, 2025
Memory Type: GDDR7
Bandwidth: 1.79 TB/s
Memory Size: 96 GB
Memory Bus: 512 bit
Shading Units: 24064
TDP: 600W

Training Settings

PEFT

Rank: 32
LoRA alpha: 64
Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Gradient checkpointing: unsloth

SFT

Epoch: 2
Batch size: 32
Gradient Accumulation steps: 1
Warmup ratio: 0.05
Learning rate: 0.0002
Optimizer: adamw_torch_fused
Learning rate scheduler: cosine

Training stats

Date: 2026-03-30T15:42:56.091336
Peak VRAM usage: 68.33 GB
Global step: 2830
Training runtime (seconds): 1496.9978
Average training loss: 1.398907420828991
Final validation loss: 1.282422423362732

Framework versions

Unsloth: 2026.3.17
TRL: 0.22.2
Transformers: 4.56.2
Pytorch: 2.10.0+cu128
Datasets: 4.8.4
Tokenizers: 0.22.2

License

This model is released under the Gemma license. See the Gemma Terms of Use and Prohibited Use Policy regarding the use of Gemma-generated content.

Downloads last month: 14

Safetensors

Model size

0.3B params

Tensor type

BF16

Model tree for kth8/gemma-3-270m-it-System-Prompt-Generator

Base model

google/gemma-3-270m

Finetuned

google/gemma-3-270m-it

Finetuned

unsloth/gemma-3-270m-it

Finetuned

(395)

this model

Quantizations

1 model

kth8
/

gemma-3-270m-it-System-Prompt-Generator

Usage example

Model Details

Hardware

Training Settings

PEFT

SFT

Training stats

Framework versions

License

Model tree for kth8/gemma-3-270m-it-System-Prompt-Generator

Datasets used to train kth8/gemma-3-270m-it-System-Prompt-Generator