Instructions to use ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16 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("ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16") 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) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16"
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": "ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16 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 "ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16"
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 ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16
Run Hermes
hermes
- MLX LM
How to use ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16", "messages": [ {"role": "user", "content": "Hello"} ] }'
ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16
The Model ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16 was converted to MLX format from dicta-il/DictaLM-3.0-1.7B-Thinking using mlx-lm version 0.29.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16")
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
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Tensor type
F16
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Model tree for ssdataanalysis/DictaLM-3.0-1.7B-Thinking-mlx-fp16
Base model
dicta-il/DictaLM-3.0-1.7B-Thinking