Instructions to use rizla/trrapi-16b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use rizla/trrapi-16b with PEFT:
```
Task type is invalid.
```

How to use rizla/trrapi-16b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rizla/trrapi-16b",
	filename="trrapi-q5km.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rizla/trrapi-16b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rizla/trrapi-16b
# Run inference directly in the terminal:
llama-cli -hf rizla/trrapi-16b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rizla/trrapi-16b
# Run inference directly in the terminal:
llama-cli -hf rizla/trrapi-16b

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 rizla/trrapi-16b
# Run inference directly in the terminal:
./llama-cli -hf rizla/trrapi-16b

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 rizla/trrapi-16b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rizla/trrapi-16b

Use Docker

docker model run hf.co/rizla/trrapi-16b

LM Studio
Jan
Ollama
How to use rizla/trrapi-16b with Ollama:
```
ollama run hf.co/rizla/trrapi-16b
```

Unsloth Studio new

How to use rizla/trrapi-16b 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 rizla/trrapi-16b 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 rizla/trrapi-16b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rizla/trrapi-16b to start chatting

Docker Model Runner
How to use rizla/trrapi-16b with Docker Model Runner:
```
docker model run hf.co/rizla/trrapi-16b
```

Lemonade

How to use rizla/trrapi-16b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rizla/trrapi-16b

Run and chat with the model

lemonade run user.trrapi-16b-{{QUANT_TAG}}

List all available models

lemonade list

Adding Evaluation Results

by leaderboard-pr-bot - opened Mar 4, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+16

-2

Files changed (1) hide show

README.md +16 -2

README.md CHANGED Viewed

@@ -3,12 +3,12 @@ license: cc-by-nc-nd-4.0
 library_name: peft
 tags:
 - generated_from_trainer
 base_model: rizla/rizla-17
 model-index:
 - name: trrapi-16
   results: []
-datasets:
-- noxneural/lilium_albanicum_eng_alb
 ---
@@ -28,3 +28,17 @@ git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make -j
 ./server -m ../trrapi-q5km.gguf --port 8080 -c 2000 -cb -t 8 -ngl 80
 ```

 library_name: peft
 tags:
 - generated_from_trainer
+datasets:
+- noxneural/lilium_albanicum_eng_alb
 base_model: rizla/rizla-17
 model-index:
 - name: trrapi-16
   results: []
 ---
 ./server -m ../trrapi-q5km.gguf --port 8080 -c 2000 -cb -t 8 -ngl 80
 ```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rizla__trrapi-16b)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |74.48|
+|AI2 Reasoning Challenge (25-Shot)|72.10|
+|HellaSwag (10-Shot)              |88.88|
+|MMLU (5-Shot)                    |64.26|
+|TruthfulQA (0-shot)              |74.13|
+|Winogrande (5-shot)              |86.35|
+|GSM8k (5-shot)                   |61.18|