Instructions to use rootsec1/gemma-2B-inst-aipi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rootsec1/gemma-2B-inst-aipi with Transformers:

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

pipe = pipeline("text-generation", model="rootsec1/gemma-2B-inst-aipi")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rootsec1/gemma-2B-inst-aipi")
model = AutoModelForCausalLM.from_pretrained("rootsec1/gemma-2B-inst-aipi")
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 rootsec1/gemma-2B-inst-aipi with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rootsec1/gemma-2B-inst-aipi",
	filename="gemma-2b-it.gguf",
)

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

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rootsec1/gemma-2B-inst-aipi with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rootsec1/gemma-2B-inst-aipi
# Run inference directly in the terminal:
llama-cli -hf rootsec1/gemma-2B-inst-aipi

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rootsec1/gemma-2B-inst-aipi
# Run inference directly in the terminal:
llama-cli -hf rootsec1/gemma-2B-inst-aipi

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 rootsec1/gemma-2B-inst-aipi
# Run inference directly in the terminal:
./llama-cli -hf rootsec1/gemma-2B-inst-aipi

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 rootsec1/gemma-2B-inst-aipi
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rootsec1/gemma-2B-inst-aipi

Use Docker

docker model run hf.co/rootsec1/gemma-2B-inst-aipi

LM Studio
Jan

vLLM

How to use rootsec1/gemma-2B-inst-aipi with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rootsec1/gemma-2B-inst-aipi"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootsec1/gemma-2B-inst-aipi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rootsec1/gemma-2B-inst-aipi

SGLang

How to use rootsec1/gemma-2B-inst-aipi 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 "rootsec1/gemma-2B-inst-aipi" \
    --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": "rootsec1/gemma-2B-inst-aipi",
		"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 "rootsec1/gemma-2B-inst-aipi" \
        --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": "rootsec1/gemma-2B-inst-aipi",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use rootsec1/gemma-2B-inst-aipi with Ollama:
```
ollama run hf.co/rootsec1/gemma-2B-inst-aipi
```

Unsloth Studio

How to use rootsec1/gemma-2B-inst-aipi 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 rootsec1/gemma-2B-inst-aipi 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 rootsec1/gemma-2B-inst-aipi to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rootsec1/gemma-2B-inst-aipi to start chatting

Docker Model Runner
How to use rootsec1/gemma-2B-inst-aipi with Docker Model Runner:
```
docker model run hf.co/rootsec1/gemma-2B-inst-aipi
```

Lemonade

How to use rootsec1/gemma-2B-inst-aipi with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rootsec1/gemma-2B-inst-aipi

Run and chat with the model

lemonade run user.gemma-2B-inst-aipi-{{QUANT_TAG}}

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

gemma-2B Fine-Tuning on SAIL/Symbolic-Instruction-Tuning

This repository contains the gemma-2B model fine-tuned on the sail/symbolic-instruction-tuning dataset. The model is designed to interpret and execute symbolic instructions with improved accuracy and efficiency.

Overview

The gemma-2B model, originally known for its robust language understanding capabilities, has been fine-tuned to enhance its performance on symbolic instruction data. This involves retraining the model on the sail/symbolic-instruction-tuning dataset, which comprises a diverse range of instructional data that tests a model's ability to follow abstract and complex directives.

Motivation

The motivation behind fine-tuning gemma-2B on this particular dataset is to bridge the gap between language understanding and execution in a symbolic context. This has wide applications in areas such as code generation, automated reasoning, and more sophisticated AI instruction following.

Getting Started

To use this model, you'll need to have an account on Hugging Face and the transformers library installed. You can install the library using pip:

pip install transformers

Once installed, you can use the following code to load and use the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "your-huggingface-username/gemma-2B-fine-tuned"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Now you can use the model for inference
input_text = "Your symbolic instruction here"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate the output
output = model.generate(input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Fine-Tuning Process

The model was fine-tuned using the following process:

Preprocessing: The sail/symbolic-instruction-tuning dataset was preprocessed to conform with the input format required by gemma-2B.
Training: The model was fine-tuned using a custom training loop that monitors loss and evaluates on a held-out validation set.
Hyperparameters: The fine-tuning used specific hyperparameters, which you can find in the training_script.py file.
Evaluation: The fine-tuned model was evaluated against a benchmark to ensure that it meets our performance standards.

Downloads last month: 43

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for rootsec1/gemma-2B-inst-aipi

Quantizations

1 model

rootsec1
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gemma-2B-inst-aipi