Instructions to use webbigdata/C3TR-Adapter_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webbigdata/C3TR-Adapter_gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="webbigdata/C3TR-Adapter_gguf",
	filename="C3TR-Adapter-IQ3_XXS.gguf",
)

llm.create_chat_completion(
	messages = "\"Меня зовут Вольфганг и я живу в Берлине\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use webbigdata/C3TR-Adapter_gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf webbigdata/C3TR-Adapter_gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf webbigdata/C3TR-Adapter_gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf webbigdata/C3TR-Adapter_gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf webbigdata/C3TR-Adapter_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 webbigdata/C3TR-Adapter_gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf webbigdata/C3TR-Adapter_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 webbigdata/C3TR-Adapter_gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf webbigdata/C3TR-Adapter_gguf:Q4_K_M

Use Docker

docker model run hf.co/webbigdata/C3TR-Adapter_gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use webbigdata/C3TR-Adapter_gguf with Ollama:
```
ollama run hf.co/webbigdata/C3TR-Adapter_gguf:Q4_K_M
```

Unsloth Studio new

How to use webbigdata/C3TR-Adapter_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 webbigdata/C3TR-Adapter_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 webbigdata/C3TR-Adapter_gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for webbigdata/C3TR-Adapter_gguf to start chatting

Docker Model Runner
How to use webbigdata/C3TR-Adapter_gguf with Docker Model Runner:
```
docker model run hf.co/webbigdata/C3TR-Adapter_gguf:Q4_K_M
```

Lemonade

How to use webbigdata/C3TR-Adapter_gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull webbigdata/C3TR-Adapter_gguf:Q4_K_M

Run and chat with the model

lemonade run user.C3TR-Adapter_gguf-Q4_K_M

List all available models

lemonade list

dahara1 commited on Aug 14, 2024

Commit

9596d9a

verified ·

1 Parent(s): 51ab5fb

Update README.md

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@@ -7,6 +7,7 @@ tags:
 - gemma2
 - llama.cpp
 - gguf
 ---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630469550907b9a115c91e62/m11e35NrZMi7ZpBQ7C6KV.png)
@@ -172,6 +173,4 @@ The following are the [recommended parameters](https://huggingface.co/google/gem
 - -e (escape newlines (\n))
 - --temp 0(pick most probable tokens)
 - --repeat-penalty 1.0(disable repetition penalty (it's never a good idea to have this with instruction tuned models)~~ latest llama.cpp default behavior, so don't mind.
-- ~~--no-penalize-nl(do not penalize repeating newlines)~~ latest llama.cpp's default behavior so you need not this option.

 - gemma2
 - llama.cpp
 - gguf
+license: gemma
 ---
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630469550907b9a115c91e62/m11e35NrZMi7ZpBQ7C6KV.png)
 - -e (escape newlines (\n))
 - --temp 0(pick most probable tokens)
 - --repeat-penalty 1.0(disable repetition penalty (it's never a good idea to have this with instruction tuned models)~~ latest llama.cpp default behavior, so don't mind.
+- ~~--no-penalize-nl(do not penalize repeating newlines)~~ latest llama.cpp's default behavior so you need not this option.