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README.md

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+---
+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
+- edge
+- lfm2.5
+- onnx
+- onnxruntime
+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%;">
+
+  <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-Instruct-ONNX
+
+ONNX export of [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) for cross-platform inference.
+
+LFM2.5 is a hybrid architecture combining multiplicative gates and short convolutions, optimized for edge deployment with fast inference on CPU, GPU, and NPU hardware.
+
+## Recommended Variants
+
+| Precision | Size | Use Case |
+|-----------|------|----------|
+| Q4 | ~1.2GB | Recommended for most uses |
+| FP16 | ~2.4GB | Higher quality |
+| Q8 | ~1.7GB | Balance of quality and size |
+
+## Model Files
+
+```
+onnx/
+├── model.onnx              # FP32
+├── model_fp16.onnx         # FP16
+├── model_q4.onnx           # Q4 (recommended)
+└── model_q8.onnx           # Q8
+```
+
+## Python
+
+### Installation
+
+```bash
+pip install onnxruntime transformers numpy huggingface_hub
+# or with GPU support:
+pip install onnxruntime-gpu transformers numpy huggingface_hub
+```
+
+### Inference
+
+```python
+import numpy as np
+import onnxruntime as ort
+from huggingface_hub import hf_hub_download
+from transformers import AutoTokenizer
+
+# Download model (Q4 recommended)
+model_id = "LiquidAI/LFM2.5-1.2B-Instruct-ONNX"
+model_path = hf_hub_download(model_id, "onnx/model_q4.onnx")
+data_path = hf_hub_download(model_id, "onnx/model_q4.onnx_data")
+
+# Load model and tokenizer
+session = ort.InferenceSession(model_path)
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+
+# Prepare chat input
+messages = [{"role": "user", "content": "What is the capital of France?"}]
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+input_ids = np.array([tokenizer.encode(prompt, add_special_tokens=False)], dtype=np.int64)
+
+# Initialize KV cache
+ONNX_DTYPE = {"tensor(float)": np.float32, "tensor(float16)": np.float16, "tensor(int64)": np.int64}
+cache = {}
+for inp in session.get_inputs():
+    if inp.name in {"input_ids", "attention_mask", "position_ids"}:
+        continue
+    shape = [d if isinstance(d, int) else 1 for d in inp.shape]
+    for i, d in enumerate(inp.shape):
+        if isinstance(d, str) and "sequence" in d.lower():
+            shape[i] = 0
+    cache[inp.name] = np.zeros(shape, dtype=ONNX_DTYPE.get(inp.type, np.float32))
+
+# Check if model uses position_ids
+input_names = {inp.name for inp in session.get_inputs()}
+use_position_ids = "position_ids" in input_names
+
+# Generate tokens
+seq_len = input_ids.shape[1]
+generated_tokens = []
+
+for step in range(100):  # max tokens
+    if step == 0:
+        ids = input_ids
+        pos = np.arange(seq_len, dtype=np.int64).reshape(1, -1)
+    else:
+        ids = np.array([[generated_tokens[-1]]], dtype=np.int64)
+        pos = np.array([[seq_len + len(generated_tokens) - 1]], dtype=np.int64)
+
+    attn_mask = np.ones((1, seq_len + len(generated_tokens)), dtype=np.int64)
+    feed = {"input_ids": ids, "attention_mask": attn_mask, **cache}
+    if use_position_ids:
+        feed["position_ids"] = pos
+
+    outputs = session.run(None, feed)
+    next_token = int(np.argmax(outputs[0][0, -1]))
+    generated_tokens.append(next_token)
+
+    # Update cache
+    for i, out in enumerate(session.get_outputs()[1:], 1):
+        name = out.name.replace("present_conv", "past_conv").replace("present.", "past_key_values.")
+        if name in cache:
+            cache[name] = outputs[i]
+
+    if next_token == tokenizer.eos_token_id:
+        break
+
+print(tokenizer.decode(generated_tokens, skip_special_tokens=True))
+```
+
+## License
+
+This model is released under the [LFM 1.0 License](LICENSE).