Instructions to use LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use LiquidAI/LFM2.5-1.2B-Instruct-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("LiquidAI/LFM2.5-1.2B-Instruct-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 Settings
- LM Studio
- Pi
How to use LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit"
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": "LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit 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 "LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit"
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 LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit
Run Hermes
hermes
- OpenClaw new
How to use LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use LiquidAI/LFM2.5-1.2B-Instruct-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 "LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "LiquidAI/LFM2.5-1.2B-Instruct-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": "LiquidAI/LFM2.5-1.2B-Instruct-MLX-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 2,219 Bytes
1c2abe0 300f822 1c2abe0 300f822 1c2abe0 300f822 1c2abe0 300f822 7605ffe 300f822 0a6ae6d 300f822 0a6ae6d 300f822 0a6ae6d 300f822 0a6ae6d 300f822 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | ---
library_name: mlx
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
- ja
- ko
- fr
- es
- de
- it
- pt
- ar
- zh
pipeline_tag: text-generation
tags:
- liquid
- lfm2.5
- edge
- mlx
base_model: LiquidAI/LFM2.5-1.2B-Instruct
---
<div align="center">
<img
src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
alt="Liquid AI"
style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
/>
<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •
<a href="https://docs.liquid.ai/lfm"><strong>Documentation</strong></a> •
<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a>
</div>
</div>
# LFM2.5-1.2B-Instruct-6bit
MLX export of [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) for Apple Silicon inference.
## Model Details
| Property | Value |
|----------|-------|
| Parameters | 1.2B |
| Precision | 6-bit |
| Group Size | 64 || Size | 907 MB |
| Context Length | 128K |
## Recommended Sampling Parameters
| Parameter | Value |
|-----------|-------|
| temperature | 0.1 |
| top_k | 50 |
| top_p | 0.1 |
| repetition_penalty | 1.05 |
| max_tokens | 512 |
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors
model, tokenizer = load("LiquidAI/LFM2.5-1.2B-Instruct-6bit")
prompt = "What is the capital of France?"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
sampler = make_sampler(temp=0.1, top_k=50, top_p=0.1)
logits_processors = make_logits_processors(repetition_penalty=1.05)
response = generate(
model,
tokenizer,
prompt=prompt,
max_tokens=512,
sampler=sampler,
logits_processors=logits_processors,
verbose=True,
)
```
## License
This model is released under the [LFM 1.0 License](LICENSE).
|