Instructions to use SY0227/llama_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use SY0227/llama_finetune 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 SY0227/llama_finetune:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SY0227/llama_finetune:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SY0227/llama_finetune:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SY0227/llama_finetune: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 SY0227/llama_finetune:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SY0227/llama_finetune: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 SY0227/llama_finetune:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SY0227/llama_finetune:Q4_K_M

Use Docker

docker model run hf.co/SY0227/llama_finetune:Q4_K_M

LM Studio
Jan
Ollama
How to use SY0227/llama_finetune with Ollama:
```
ollama run hf.co/SY0227/llama_finetune:Q4_K_M
```

Unsloth Studio

How to use SY0227/llama_finetune 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 SY0227/llama_finetune 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 SY0227/llama_finetune to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SY0227/llama_finetune to start chatting

Docker Model Runner
How to use SY0227/llama_finetune with Docker Model Runner:
```
docker model run hf.co/SY0227/llama_finetune:Q4_K_M
```

Lemonade

How to use SY0227/llama_finetune with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SY0227/llama_finetune:Q4_K_M

Run and chat with the model

lemonade run user.llama_finetune-Q4_K_M

List all available models

lemonade list

Atomic Chat

SY0227 commited on Feb 24

Commit

b8a88a8

verified ·

1 Parent(s): 2b9055c

Trained with Unsloth

Browse files

Upload model trained with Unsloth 2x faster

Files changed (2) hide show

.gitattributes +1 -0
meta-llama-3.1-8b-instruct.F16.gguf +3 -0

.gitattributes CHANGED Viewed

@@ -39,3 +39,4 @@ llama-3.1-8b.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
 meta-llama-3.1-8b-instruct.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 meta-llama-3.1-8b-instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
 simon-llama-3.1-8b-instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

 meta-llama-3.1-8b-instruct.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
 meta-llama-3.1-8b-instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
 simon-llama-3.1-8b-instruct.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+meta-llama-3.1-8b-instruct.F16.gguf filter=lfs diff=lfs merge=lfs -text

meta-llama-3.1-8b-instruct.F16.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:941c3e8b885f7f800daaa01cd9a39868170158bb4ea7727677fc977eac5722ac
+size 16068895872