Text Generation
MLX
Safetensors
Transformers
English
llama
text-generation-inference
edit-prediction
next-edit-suggestion
6-bit
Instructions to use riyajm/zeta-2-6bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use riyajm/zeta-2-6bit-mlx 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("riyajm/zeta-2-6bit-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Transformers
How to use riyajm/zeta-2-6bit-mlx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="riyajm/zeta-2-6bit-mlx")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("riyajm/zeta-2-6bit-mlx") model = AutoModelForCausalLM.from_pretrained("riyajm/zeta-2-6bit-mlx") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- vLLM
How to use riyajm/zeta-2-6bit-mlx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "riyajm/zeta-2-6bit-mlx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "riyajm/zeta-2-6bit-mlx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/riyajm/zeta-2-6bit-mlx
- SGLang
How to use riyajm/zeta-2-6bit-mlx 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 "riyajm/zeta-2-6bit-mlx" \ --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": "riyajm/zeta-2-6bit-mlx", "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 "riyajm/zeta-2-6bit-mlx" \ --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": "riyajm/zeta-2-6bit-mlx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use riyajm/zeta-2-6bit-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "riyajm/zeta-2-6bit-mlx" --prompt "Once upon a time"
- Docker Model Runner
How to use riyajm/zeta-2-6bit-mlx with Docker Model Runner:
docker model run hf.co/riyajm/zeta-2-6bit-mlx
riyajm/zeta-2-6bit-mlx
This model riyajm/zeta-2-6bit-mlx was converted to MLX format from zed-industries/zeta-2 using mlx-lm version 0.31.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("riyajm/zeta-2-6bit-mlx")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
8B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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6-bit
Model tree for riyajm/zeta-2-6bit-mlx
Base model
ByteDance-Seed/Seed-Coder-8B-Base Finetuned
zed-industries/zeta-2