Instructions to use FritzStack/Llama3B-GoEmotions-mlx-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FritzStack/Llama3B-GoEmotions-mlx-Q4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FritzStack/Llama3B-GoEmotions-mlx-Q4")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FritzStack/Llama3B-GoEmotions-mlx-Q4")
model = AutoModelForCausalLM.from_pretrained("FritzStack/Llama3B-GoEmotions-mlx-Q4")

MLX

How to use FritzStack/Llama3B-GoEmotions-mlx-Q4 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("FritzStack/Llama3B-GoEmotions-mlx-Q4")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use FritzStack/Llama3B-GoEmotions-mlx-Q4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FritzStack/Llama3B-GoEmotions-mlx-Q4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FritzStack/Llama3B-GoEmotions-mlx-Q4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FritzStack/Llama3B-GoEmotions-mlx-Q4

SGLang

How to use FritzStack/Llama3B-GoEmotions-mlx-Q4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FritzStack/Llama3B-GoEmotions-mlx-Q4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FritzStack/Llama3B-GoEmotions-mlx-Q4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FritzStack/Llama3B-GoEmotions-mlx-Q4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FritzStack/Llama3B-GoEmotions-mlx-Q4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use FritzStack/Llama3B-GoEmotions-mlx-Q4 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 FritzStack/Llama3B-GoEmotions-mlx-Q4 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 FritzStack/Llama3B-GoEmotions-mlx-Q4 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FritzStack/Llama3B-GoEmotions-mlx-Q4 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="FritzStack/Llama3B-GoEmotions-mlx-Q4",
    max_seq_length=2048,
)

MLX LM

How to use FritzStack/Llama3B-GoEmotions-mlx-Q4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "FritzStack/Llama3B-GoEmotions-mlx-Q4" --prompt "Once upon a time"

Docker Model Runner
How to use FritzStack/Llama3B-GoEmotions-mlx-Q4 with Docker Model Runner:
```
docker model run hf.co/FritzStack/Llama3B-GoEmotions-mlx-Q4
```

Llama3B-GoEmotions-mlx-Q4 / README.md

FritzStack

Update README.md

711913d verified 3 months ago

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metadata

base_model: FritzStack/Llama3B-emotions-merged
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - mlx
  - mlx-my-repo
license: apache-2.0
language:
  - en

FritzStack/Llama3B-emotions-merged-mlx-4Bit

The Model FritzStack/Llama3B-emotions-merged-mlx-4Bit was converted to MLX format from FritzStack/Llama3B-emotions-merged using mlx-lm version 0.29.1.

Use with mlx

pip install mlx-lm
!pip install git+https://github.com/Fede-stack/TONYpy.git

from TONY.Emotions import Emotions_Predictor_mlx
emotions = Emotions_Predictor_mlx(model_name = 'FritzStack/Llama3B-GoEmotions-mlx-Q4')
emotions.predict_emotions(text)