Instructions to use afrideva/smartyplats-1.1b-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/smartyplats-1.1b-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/smartyplats-1.1b-v1-GGUF",
	filename="smartyplats-1.1b-v1.q2_k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use afrideva/smartyplats-1.1b-v1-GGUF 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 afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/smartyplats-1.1b-v1-GGUF: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 afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/smartyplats-1.1b-v1-GGUF: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 afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/smartyplats-1.1b-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/smartyplats-1.1b-v1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/smartyplats-1.1b-v1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M

Ollama
How to use afrideva/smartyplats-1.1b-v1-GGUF with Ollama:
```
ollama run hf.co/afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/smartyplats-1.1b-v1-GGUF 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 afrideva/smartyplats-1.1b-v1-GGUF 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 afrideva/smartyplats-1.1b-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for afrideva/smartyplats-1.1b-v1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use afrideva/smartyplats-1.1b-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/smartyplats-1.1b-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/smartyplats-1.1b-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.smartyplats-1.1b-v1-GGUF-Q4_K_M

List all available models

lemonade list

afrideva commited on Nov 12, 2023

Commit

6d65121

1 Parent(s): 0db1e3e

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README.md +44 -0

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	@@ -0,0 +1,44 @@

+---
+base_model: vihangd/smartyplats-1.1b-v1
+inference: false
+license: apache-2.0
+model_creator: vihangd
+model_name: smartyplats-1.1b-v1
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# vihangd/smartyplats-1.1b-v1-GGUF
+Quantized GGUF model files for [smartyplats-1.1b-v1](https://huggingface.co/vihangd/smartyplats-1.1b-v1) from [vihangd](https://huggingface.co/vihangd)
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [smartyplats-1.1b-v1.q2_k.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q2_k.gguf) | q2_k | 482.14 MB  |
+| [smartyplats-1.1b-v1.q3_k_m.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q3_k_m.gguf) | q3_k_m | 549.85 MB  |
+| [smartyplats-1.1b-v1.q4_k_m.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q4_k_m.gguf) | q4_k_m | 667.81 MB  |
+| [smartyplats-1.1b-v1.q5_k_m.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q5_k_m.gguf) | q5_k_m | 782.04 MB  |
+| [smartyplats-1.1b-v1.q6_k.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q6_k.gguf) | q6_k | 903.41 MB  |
+| [smartyplats-1.1b-v1.q8_0.gguf](https://huggingface.co/afrideva/smartyplats-1.1b-v1-GGUF/resolve/main/smartyplats-1.1b-v1.q8_0.gguf) | q8_0 | 1.17 GB  |
+## Original Model Card:
+<p><h1> SmartyPlats-1.1b V1 </h1></p>
+An experimental finetune of TinyLLaMA 1T with QLoRA
+<h2> Datasets </h2>
+Trained on alpca style datasets
+<p><h2> Prompt Template </h2></p>
+Uses alpaca style prompt template