Instructions to use QuantFactory/Llama-3-8B-WildChat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama-3-8B-WildChat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama-3-8B-WildChat-GGUF",
	filename="Llama-3-8B-WildChat.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Llama-3-8B-WildChat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-8B-WildChat-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 QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama-3-8B-WildChat-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 QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama-3-8B-WildChat-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 QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama-3-8B-WildChat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama-3-8B-WildChat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Llama-3-8B-WildChat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Llama-3-8B-WildChat-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Llama-3-8B-WildChat-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 QuantFactory/Llama-3-8B-WildChat-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 QuantFactory/Llama-3-8B-WildChat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Llama-3-8B-WildChat-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Llama-3-8B-WildChat-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama-3-8B-WildChat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama-3-8B-WildChat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-8B-WildChat-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Llama-3-8B-WildChat-GGUF

This is quantized version of Magpie-Align/Llama-3-8B-WildChat created using llama.cpp

Model Description

See axolotl config

axolotl version: 0.4.0


base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: flydust/WildChat_ShareGPT
    type: sharegpt
    conversation: llama3
dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: ./out_Llama-3-WildChat

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project: SynDa
wandb_entity:
wandb_watch:
wandb_name: Llama-3-WildChat
wandb_log_model:
hub_model_id: SynDa/Llama-3-8B-WildChat

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 3
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|end_of_text|>

Llama-3-8B-WildChat

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8197

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 32
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss
1.1455	0.0003	1	1.3389
0.9084	0.3333	1128	0.8551
0.9265	0.6667	2256	0.8363
0.9086	1.0	3384	0.8210
0.8257	1.3164	4512	0.8214
0.8306	1.6497	5640	0.8197
0.8252	1.9831	6768	0.8197

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 77

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Llama-3-8B-WildChat-GGUF

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

Magpie-Align/Llama-3-8B-WildChat

Quantized

(3)

this model