Instructions to use afrideva/tiny-llama-miniguanaco-1.5T-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/tiny-llama-miniguanaco-1.5T-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="afrideva/tiny-llama-miniguanaco-1.5T-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("afrideva/tiny-llama-miniguanaco-1.5T-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use afrideva/tiny-llama-miniguanaco-1.5T-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/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/tiny-llama-miniguanaco-1.5T-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/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/tiny-llama-miniguanaco-1.5T-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/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/tiny-llama-miniguanaco-1.5T-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/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/tiny-llama-miniguanaco-1.5T-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/tiny-llama-miniguanaco-1.5T-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/tiny-llama-miniguanaco-1.5T-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M

SGLang

How to use afrideva/tiny-llama-miniguanaco-1.5T-GGUF 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 "afrideva/tiny-llama-miniguanaco-1.5T-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/tiny-llama-miniguanaco-1.5T-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "afrideva/tiny-llama-miniguanaco-1.5T-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/tiny-llama-miniguanaco-1.5T-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use afrideva/tiny-llama-miniguanaco-1.5T-GGUF with Ollama:
```
ollama run hf.co/afrideva/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use afrideva/tiny-llama-miniguanaco-1.5T-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/tiny-llama-miniguanaco-1.5T-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/tiny-llama-miniguanaco-1.5T-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.tiny-llama-miniguanaco-1.5T-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Corianas/tiny-llama-miniguanaco-1.5T-GGUF

Quantized GGUF model files for tiny-llama-miniguanaco-1.5T from Corianas

Name	Quant method	Size
tiny-llama-miniguanaco-1.5t.q2_k.gguf	q2_k	482.14 MB
tiny-llama-miniguanaco-1.5t.q3_k_m.gguf	q3_k_m	549.85 MB
tiny-llama-miniguanaco-1.5t.q4_k_m.gguf	q4_k_m	667.81 MB
tiny-llama-miniguanaco-1.5t.q5_k_m.gguf	q5_k_m	782.04 MB
tiny-llama-miniguanaco-1.5t.q6_k.gguf	q6_k	903.41 MB
tiny-llama-miniguanaco-1.5t.q8_0.gguf	q8_0	1.17 GB

Original Model Card:

TinyLLama 1.5T checkpoint trained to answer questions.

f"{'prompt'}\n{'completion'}\n<END>"

No input/output, just question, then newline to begin the answer.

Downloads last month: 564

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for afrideva/tiny-llama-miniguanaco-1.5T-GGUF

Base model

Corianas/tiny-llama-miniguanaco-1.5T

Quantized

(2)

this model

Collection including afrideva/tiny-llama-miniguanaco-1.5T-GGUF

TinyLlama q2_k - q8 GGUF Quants

Collection

13 items • Updated Dec 1, 2023 • 1