初始化项目，由ModelHub XC社区提供模型

Model: tiiuae/Falcon-H1-Tiny-Multilingual-100M-Base
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- falcon-h1
+- edge
+license: other
+license_name: falcon-llm-license
+license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
+---
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/l1du02RjuAZJcksI5tQ-F.png" alt="drawing" width="800"/>
+
+#  Table of Contents
+
+0. [TL;DR](#TL;DR)
+1. [Model Details](#model-details)
+2. [Training Details](#training-details)
+3. [Usage](#usage)
+4. [Evaluation](#evaluation)
+5. [Citation](#citation)
+
+# TL;DR
+
+# Model Details
+
+## Model Description
+
+- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
+- **Model type:** Causal decoder-only
+- **Architecture:** Hybrid Transformers + Mamba architecture
+- **Language(s) (NLP):** English
+- **Number of Parameters:** 90M
+- **License:** Falcon-LLM License
+
+# Training details
+
+For more details about the training protocol of this model, please refer to the [Falcon-H1-Tiny technical blogpost](https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost).
+
+# Usage
+
+Currently to use this model you can either rely on Hugging Face `transformers`, `vLLM`, `sglang`, `llama.cpp`, `ollama` or `mlx` library.
+
+## Inference
+
+### 🤗 transformers
+
+Refer to the snippet below to run H1 models using 🤗 transformers:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "tiiuae/Falcon-H1-Tiny-100M-Multilingual-Base"
+
+model = AutoModelForCausalLM.from_pretrained(
+  model_id,
+  torch_dtype=torch.bfloat16,
+  device_map="auto"
+)
+
+# Perform text generation
+```
+
+or
+
+```bash
+transformers serve tiiuae/Falcon-H1-Tiny-100M-Multilingual-Base
+```
+
+### `llama.cpp`
+
+You can find all GGUF files compatible with `llama.cpp` under [our official collection]() - an example setup could be:
+
+```bash
+brew install llama.cpp 
+pip install huggingface_hub 
+hf download tiiuae/Falcon-H1-Tiny-100M-Multilingual-Base Falcon-H1-Tiny-100M-Multilingual-Base-Q8_0.gguf --local-dir ./ 
+llama-cli ./ Falcon-H1-Tiny-100M-Multilingual-Base-Q8_0.gguf -cnv 
+```
+
+### `ollama`
+
+```bash
+ollama run hf.co/tiiuae/Falcon-H1-Tiny-100M-Multilingual-Base:Q8_0 
+```
+
+### Apple `mlx` 
+
+```bash
+mlx_lm.chat --model tiiuae/Falcon-H1-Tiny-100M-Multilingual-Base
+```
+
+### vLLM
+
+For vLLM, simply start a server by executing the command below:
+
+```bash
+# pip install vllm>=0.9.0
+vllm serve tiiuae/Falcon-H1-Tiny-100M-Multilingual-Base --tensor-parallel-size 2 --data-parallel-size 1
+```
+
+### sglang
+
+```bash
+python -m sglang.launch_server \
+  --model ttiiuae/Falcon-H1-Tiny-100M-Multilingual-Base \
+  --tensor-parallel-size 1 
+```
+
+# Evaluation
+
+For detailed evaluation of Falcon-H1-Tiny series, please refer to our [technical blogpost](https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost)
+
+# Useful links
+
+- View [our release blogpost](https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost).
+- Feel free to join [our discord server](https://discord.gg/trwMYP9PYm) if you have any questions or to interact with our researchers and developers.
+
+# Citation
+
+If the Falcon-H1-Tiny family of models were helpful to your work, feel free to give us a cite.
+
+```
+@misc{falcon_h1_tiny,
+  title={Falcon-H1-Tiny: A series of extremely small, yet powerful language models redefining capabilities at small scale},
+  author={Falcon-LLM Team},
+  year={2026}, 
+}
+```