yahma/alpaca-cleaned
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How to use afrideva/TinyAlpaca-v0.1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="afrideva/TinyAlpaca-v0.1-GGUF", filename="tinyalpaca-v0.1.fp16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
How to use afrideva/TinyAlpaca-v0.1-GGUF with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
# 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/TinyAlpaca-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
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/TinyAlpaca-v0.1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
docker model run hf.co/afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
How to use afrideva/TinyAlpaca-v0.1-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/TinyAlpaca-v0.1-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/TinyAlpaca-v0.1-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
How to use afrideva/TinyAlpaca-v0.1-GGUF with Ollama:
ollama run hf.co/afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
How to use afrideva/TinyAlpaca-v0.1-GGUF with Unsloth Studio:
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/TinyAlpaca-v0.1-GGUF to start chatting
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/TinyAlpaca-v0.1-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for afrideva/TinyAlpaca-v0.1-GGUF to start chatting
How to use afrideva/TinyAlpaca-v0.1-GGUF with Docker Model Runner:
docker model run hf.co/afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
How to use afrideva/TinyAlpaca-v0.1-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull afrideva/TinyAlpaca-v0.1-GGUF:Q4_K_M
lemonade run user.TinyAlpaca-v0.1-GGUF-Q4_K_M
lemonade list
Quantized GGUF model files for TinyAlpaca-v0.1 from blueapple8259
| Name | Quant method | Size |
|---|---|---|
| tinyalpaca-v0.1.q2_k.gguf | q2_k | 482.14 MB |
| tinyalpaca-v0.1.q3_k_m.gguf | q3_k_m | 549.85 MB |
| tinyalpaca-v0.1.q4_k_m.gguf | q4_k_m | 667.81 MB |
| tinyalpaca-v0.1.q5_k_m.gguf | q5_k_m | 782.04 MB |
| tinyalpaca-v0.1.q6_k.gguf | q6_k | 903.41 MB |
| tinyalpaca-v0.1.q8_0.gguf | q8_0 | 1.17 GB |
This model is a TinyLlama model fine-tuned with the yahma/alpaca-cleaned dataset.
prompt:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Input:
{input}
### Response:
Base model
blueapple8259/TinyAlpaca-v0.1