---
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
- reasoning
base_model: LiquidAI/LFM2.5-1.2B-Thinking
---
# LFM2.5-1.2B-Thinking-4bit
MLX export of [LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking) for Apple Silicon inference.
LFM2.5-Thinking is a reasoning model that generates chain-of-thought explanations before providing final answers.
## Model Details
| Property | Value |
|----------|-------|
| Parameters | 1.2B |
| Precision | 4-bit |
| Group Size | 64 |
| Size | 628 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-Thinking-4bit")
prompt = "solve 2+2*2"
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,
)
```
## Chat Format
The model uses the ChatML format:
```
<|startoftext|><|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
{assistant_response}<|im_end|>
```
## Thinking Format
The model outputs chain-of-thought reasoning inside `` tags before the final answer:
```
[Internal reasoning process...]
[Final answer]
```
### Example
**Prompt:**
```
<|startoftext|><|im_start|>user
solve 2+2*2<|im_end|>
<|im_start|>assistant
```
**Model output:**
```
Okay, let me try to solve this problem: "2+2*2". Hmm, first I need to make sure I understand what exactly is being asked here. The expression is 2 + 2*2. Alright, so maybe it's just a simple arithmetic problem. Let me break it down step by step.
First, looking at the expression 2 + 2*2. I know in math, the order of operations is important here. Multiplication comes before addition, right? So the 2*2 would be calculated first. So 2 times 2 is 4. Then add that to the initial 2. So 2 + 4 equals 6.
The answer is **6**.
Following the order of operations (PEMDAS/BODMAS), multiplication is performed before addition:
- First: 2 × 2 = 4
- Then: 2 + 4 = 6
```
### Stripping Thinking from History
The chat template automatically strips `` content from previous assistant messages in multi-turn conversations. To preserve thinking in history, use:
```python
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
keep_past_thinking=True # Preserve thinking in history
)
```
## Tool Calling
The model supports function calling with a specific format.
### Tool Definition
Tools are defined as JSON in the system prompt:
```
List of tools: [{"name": "tool_name", "description": "...", "parameters": {...}}]
```
### Tool Call Format
The model generates tool calls using special tokens:
```
<|tool_call_start|>[function_name(arg1="value1", arg2="value2")]<|tool_call_end|>
```
### Tool Response Format
Tool results are provided in a `tool` role message:
```
<|im_start|>tool
[{"result": "..."}]<|im_end|>
```
## License
This model is released under the [LFM 1.0 License](LICENSE).
