Instructions to use smangrul/llama-3-8B-instruct-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use smangrul/llama-3-8B-instruct-function-calling with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "smangrul/llama-3-8B-instruct-function-calling")

llama-cpp-python

How to use smangrul/llama-3-8B-instruct-function-calling with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="smangrul/llama-3-8B-instruct-function-calling",
	filename="llama-3-8B-instruct-function-calling-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use smangrul/llama-3-8B-instruct-function-calling 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 smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf smangrul/llama-3-8B-instruct-function-calling:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf smangrul/llama-3-8B-instruct-function-calling: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 smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf smangrul/llama-3-8B-instruct-function-calling: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 smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf smangrul/llama-3-8B-instruct-function-calling:Q4_K_M

Use Docker

docker model run hf.co/smangrul/llama-3-8B-instruct-function-calling:Q4_K_M

LM Studio
Jan
Ollama
How to use smangrul/llama-3-8B-instruct-function-calling with Ollama:
```
ollama run hf.co/smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
```

Unsloth Studio

How to use smangrul/llama-3-8B-instruct-function-calling 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 smangrul/llama-3-8B-instruct-function-calling 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 smangrul/llama-3-8B-instruct-function-calling to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for smangrul/llama-3-8B-instruct-function-calling to start chatting

Atomic Chat new
Docker Model Runner
How to use smangrul/llama-3-8B-instruct-function-calling with Docker Model Runner:
```
docker model run hf.co/smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
```

Lemonade

How to use smangrul/llama-3-8B-instruct-function-calling with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull smangrul/llama-3-8B-instruct-function-calling:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-8B-instruct-function-calling-Q4_K_M

List all available models

lemonade list

llama-3-8B-instruct-function-calling

This model is a fine-tuned version of unsloth/llama-3-8b-Instruct-bnb-4bit on the generator dataset. It achieves the following results on the evaluation set:

Loss: 0.3908

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
0.386	1.0	766	0.3908

Framework versions

PEFT 0.10.0
Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.15.2

Downloads last month: 26

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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Model tree for smangrul/llama-3-8B-instruct-function-calling

Base model

unsloth/llama-3-8b-Instruct-bnb-4bit

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this model

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llama-3-8B-instruct-function-calling

llama-3-8B-instruct-function-calling

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for smangrul/llama-3-8B-instruct-function-calling

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