Instructions to use SiddhJagani/cac-v0.1-mlx-Q2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SiddhJagani/cac-v0.1-mlx-Q2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SiddhJagani/cac-v0.1-mlx-Q2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SiddhJagani/cac-v0.1-mlx-Q2") model = AutoModelForCausalLM.from_pretrained("SiddhJagani/cac-v0.1-mlx-Q2") - MLX
How to use SiddhJagani/cac-v0.1-mlx-Q2 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("SiddhJagani/cac-v0.1-mlx-Q2") 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 SiddhJagani/cac-v0.1-mlx-Q2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SiddhJagani/cac-v0.1-mlx-Q2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SiddhJagani/cac-v0.1-mlx-Q2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SiddhJagani/cac-v0.1-mlx-Q2
- SGLang
How to use SiddhJagani/cac-v0.1-mlx-Q2 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 "SiddhJagani/cac-v0.1-mlx-Q2" \ --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": "SiddhJagani/cac-v0.1-mlx-Q2", "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 "SiddhJagani/cac-v0.1-mlx-Q2" \ --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": "SiddhJagani/cac-v0.1-mlx-Q2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - MLX LM
How to use SiddhJagani/cac-v0.1-mlx-Q2 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "SiddhJagani/cac-v0.1-mlx-Q2" --prompt "Once upon a time"
- Docker Model Runner
How to use SiddhJagani/cac-v0.1-mlx-Q2 with Docker Model Runner:
docker model run hf.co/SiddhJagani/cac-v0.1-mlx-Q2
SiddhJagani/cac-v0.1-mlx-Q2
The Model SiddhJagani/cac-v0.1-mlx-Q2 was converted to MLX format from codemateai/cac-v0.1 using mlx-lm version 0.28.3.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("SiddhJagani/cac-v0.1-mlx-Q2")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
0.6B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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2-bit
Model tree for SiddhJagani/cac-v0.1-mlx-Q2
Base model
deepseek-ai/deepseek-coder-6.7b-base Finetuned
codemateai/cac-v0.1