Text Generation
Transformers
Safetensors
MLX
llama
conversational
text-generation-inference
4-bit precision
Instructions to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aceeee/L3.3-Electra-R1-70b-mlx-4Bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aceeee/L3.3-Electra-R1-70b-mlx-4Bit") model = AutoModelForCausalLM.from_pretrained("aceeee/L3.3-Electra-R1-70b-mlx-4Bit") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - MLX
How to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("aceeee/L3.3-Electra-R1-70b-mlx-4Bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- vLLM
How to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aceeee/L3.3-Electra-R1-70b-mlx-4Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aceeee/L3.3-Electra-R1-70b-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aceeee/L3.3-Electra-R1-70b-mlx-4Bit
- SGLang
How to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit 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 "aceeee/L3.3-Electra-R1-70b-mlx-4Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aceeee/L3.3-Electra-R1-70b-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "aceeee/L3.3-Electra-R1-70b-mlx-4Bit" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aceeee/L3.3-Electra-R1-70b-mlx-4Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Pi
How to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "aceeee/L3.3-Electra-R1-70b-mlx-4Bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "aceeee/L3.3-Electra-R1-70b-mlx-4Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "aceeee/L3.3-Electra-R1-70b-mlx-4Bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default aceeee/L3.3-Electra-R1-70b-mlx-4Bit
Run Hermes
hermes
- MLX LM
How to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "aceeee/L3.3-Electra-R1-70b-mlx-4Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "aceeee/L3.3-Electra-R1-70b-mlx-4Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aceeee/L3.3-Electra-R1-70b-mlx-4Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use aceeee/L3.3-Electra-R1-70b-mlx-4Bit with Docker Model Runner:
docker model run hf.co/aceeee/L3.3-Electra-R1-70b-mlx-4Bit
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