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README.md

@@ -1,7 +1,192 @@
 ---
-language: en
-pipeline_tag: text-generation
 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
 ---
+
+<div align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" alt="Liquid AI" style="width: 100%; max-width: 100%;">
+
+  <p>
+    <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> •
+    <a href="https://www.liquid.ai/blog/"><strong>Blog</strong></a>
+  </p>
+</div>
+
+# LFM2.5-1.2B-Thinking-5bit
+
+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 | 5-bit |
+| Group Size | 64 |
+| Size | 768 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-5bit")
+
+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 `<think>` tags before the final answer:
+
+```
+<think>
+[Internal reasoning process...]
+</think>
+
+[Final answer]
+```
+
+### Example
+
+**Prompt:**
+
+```
+<|startoftext|><|im_start|>user
+solve 2+2*2<|im_end|>
+<|im_start|>assistant
+```
+
+**Model output:**
+
+```
+<think> 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.
+</think>
+
+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 `<think>` 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).