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
- llama.cpp
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"
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