Text Generation
Transformers
Safetensors
MLX
English
mistral
conversational
text-generation-inference
6-bit
Instructions to use D8A/XortronCriminalComputingConfig-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use D8A/XortronCriminalComputingConfig-mlx-6Bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="D8A/XortronCriminalComputingConfig-mlx-6Bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("D8A/XortronCriminalComputingConfig-mlx-6Bit") model = AutoModelForCausalLM.from_pretrained("D8A/XortronCriminalComputingConfig-mlx-6Bit") 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 D8A/XortronCriminalComputingConfig-mlx-6Bit 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("D8A/XortronCriminalComputingConfig-mlx-6Bit") 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
- LM Studio
- vLLM
How to use D8A/XortronCriminalComputingConfig-mlx-6Bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "D8A/XortronCriminalComputingConfig-mlx-6Bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "D8A/XortronCriminalComputingConfig-mlx-6Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/D8A/XortronCriminalComputingConfig-mlx-6Bit
- SGLang
How to use D8A/XortronCriminalComputingConfig-mlx-6Bit 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 "D8A/XortronCriminalComputingConfig-mlx-6Bit" \ --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": "D8A/XortronCriminalComputingConfig-mlx-6Bit", "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 "D8A/XortronCriminalComputingConfig-mlx-6Bit" \ --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": "D8A/XortronCriminalComputingConfig-mlx-6Bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - MLX LM
How to use D8A/XortronCriminalComputingConfig-mlx-6Bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "D8A/XortronCriminalComputingConfig-mlx-6Bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "D8A/XortronCriminalComputingConfig-mlx-6Bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "D8A/XortronCriminalComputingConfig-mlx-6Bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Docker Model Runner
How to use D8A/XortronCriminalComputingConfig-mlx-6Bit with Docker Model Runner:
docker model run hf.co/D8A/XortronCriminalComputingConfig-mlx-6Bit
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# Xonaz81/XortronCriminalComputingConfig-mlx-6Bit
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## Use with mlx
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```bash
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pip install mlx-lm
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# Xonaz81/XortronCriminalComputingConfig-mlx-6Bit
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Because this model seems to be promising and there was no 6-bit version to be found, I decided to create one from the full model weights. This is a normal 6-bit MLX quant. No advanced DWQ quants for now but coming in the future! The original model [Xonaz81/XortronCriminalComputingConfig-mlx-6Bit](https://huggingface.co/Xonaz81/XortronCriminalComputingConfig-mlx-6Bit) was converted to MLX format from [darkc0de/XortronCriminalComputingConfig](https://huggingface.co/darkc0de/XortronCriminalComputingConfig) using mlx-lm
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## Use with mlx or LM-studio
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```bash
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pip install mlx-lm
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