Instructions to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-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-v2-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-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-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-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-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-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-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-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-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-v2-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-v2-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-v2-GGUF:Q4_K_M

Ollama
How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF with Ollama:
```
ollama run hf.co/afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-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/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-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/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-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/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF to start chatting

Docker Model Runner
How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

cnmoro/TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2-GGUF

Quantized GGUF model files for TinyLlama-1.1B-intermediate-1.5T-PTBR-Instruct-v2 from cnmoro

Name	Quant method	Size
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v2.q2_k.gguf	q2_k	482.14 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v2.q3_k_m.gguf	q3_k_m	549.85 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v2.q4_k_m.gguf	q4_k_m	667.81 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v2.q5_k_m.gguf	q5_k_m	782.04 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v2.q6_k.gguf	q6_k	903.41 MB
tinyllama-1.1b-intermediate-1.5t-ptbr-instruct-v2.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: 125

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-v2-GGUF

TinyLlama q2_k - q8 GGUF Quants

Collection

13 items • Updated Dec 1, 2023 • 1