Instructions to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-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/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-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/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-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/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-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/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-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/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M

Ollama
How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF with Ollama:
```
ollama run hf.co/afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

cnmoro/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF

Quantized GGUF model files for TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1 from cnmoro

Name	Quant method	Size
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v1.q2_k.gguf	q2_k	482.14 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v1.q3_k_m.gguf	q3_k_m	549.85 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v1.q4_k_m.gguf	q4_k_m	667.81 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v1.q5_k_m.gguf	q5_k_m	782.04 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v1.q6_k.gguf	q6_k	903.41 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v1.q8_0.gguf	q8_0	1.17 GB

Original Model Card:

Finetuned version of PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T, on a Portuguese instruct dataset, using axolotl.

This is a work in progress, final version will be v3 or v4.

Prompt format:

f"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n"

Downloads last month: 121

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Collection including afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v1-GGUF

TinyLlama q2_k - q8 GGUF Quants

Collection

13 items • Updated Dec 1, 2023 • 1