Instructions to use Salexoid/tiny-rullama-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Salexoid/tiny-rullama-1b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "Salexoid/tiny-rullama-1b")

llama-cpp-python

How to use Salexoid/tiny-rullama-1b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Salexoid/tiny-rullama-1b",
	filename="ggml-model-q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Salexoid/tiny-rullama-1b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Salexoid/tiny-rullama-1b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Salexoid/tiny-rullama-1b:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Salexoid/tiny-rullama-1b:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Salexoid/tiny-rullama-1b: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 Salexoid/tiny-rullama-1b:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Salexoid/tiny-rullama-1b: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 Salexoid/tiny-rullama-1b:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Salexoid/tiny-rullama-1b:Q8_0

Use Docker

docker model run hf.co/Salexoid/tiny-rullama-1b:Q8_0

LM Studio
Jan
Ollama
How to use Salexoid/tiny-rullama-1b with Ollama:
```
ollama run hf.co/Salexoid/tiny-rullama-1b:Q8_0
```

Unsloth Studio new

How to use Salexoid/tiny-rullama-1b 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 Salexoid/tiny-rullama-1b 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 Salexoid/tiny-rullama-1b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Salexoid/tiny-rullama-1b to start chatting

Docker Model Runner
How to use Salexoid/tiny-rullama-1b with Docker Model Runner:
```
docker model run hf.co/Salexoid/tiny-rullama-1b:Q8_0
```

Lemonade

How to use Salexoid/tiny-rullama-1b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Salexoid/tiny-rullama-1b:Q8_0

Run and chat with the model

lemonade run user.tiny-rullama-1b-Q8_0

List all available models

lemonade list

tiny-rullama-1b

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.002
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.03
training_steps: 4000

Training results

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.0
Tokenizers 0.15.0

Downloads last month: 5

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

8-bit

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