---
title: Falcon-H1-1.5B-Deep-Instruct
canonical_url: "https://www.modelscope.cn/models/unsloth/Falcon-H1-1.5B-Deep-Instruct"
md_url: "https://www.modelscope.cn/models/unsloth/Falcon-H1-1.5B-Deep-Instruct.md"
repository: unsloth/Falcon-H1-1.5B-Deep-Instruct
last_updated: 2025-07-10
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - falcon_h1
architectures:
  - FalconH1ForCausalLM
base_model:
  - tiiuae/Falcon-H1-1.5B-Deep-Instruct
base_model_relation: finetune
parameters: 1.6B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
inference_backends:
  - "deploy_task text/emb"
  - "vllm 0.9.2"
downloads: 242
stars: 0
tags:
  - falcon-h1
  - unsloth
---

# Falcon-H1-1.5B-Deep-Instruct

> Falcon-H1-1.5B-Deep-Instruct - unsloth 在 ModelScope 开源的模型。[!NOTE] Includes our chat template fixes! For llama.cpp, use --jinja

unsloth/Falcon-H1-1.5B-Deep-Instruct 是 ModelScope 魔搭社区上的 1.6B 参数text-generation模型，采用 other 许可，基于 tiiuae/Falcon-H1-1.5B-Deep-Instruct 构建，可用 deploy_task text/emb、vllm 0.9.2 部署。

- **Repository**: unsloth/Falcon-H1-1.5B-Deep-Instruct
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 1.6B
- **Base model**: tiiuae/Falcon-H1-1.5B-Deep-Instruct
- **Inference backends**: deploy_task text/emb, vllm 0.9.2
- **Tags**: falcon-h1, unsloth
- **Downloads**: 242
- **Stars**: 0
- **Last updated**: 2025-07-10

Source: https://www.modelscope.cn/models/unsloth/Falcon-H1-1.5B-Deep-Instruct

---

> [!NOTE]
>  Includes our **chat template fixes**! <br> For `llama.cpp`, use `--jinja`
>

<div>
<p style="margin-top: 0;margin-bottom: 0;">
    <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
  </p>
  <div style="display: flex; gap: 5px; align-items: center; ">
    <a href="https://github.com/unslothai/unsloth/">
      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
    </a>
    <a href="https://discord.gg/unsloth">
      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
    </a>
    <a href="https://docs.unsloth.ai/">
      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
    </a>
  </div>
</div>


<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/falcon_mamba/falcon-h1-logo.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, Multilingual
- **License:** Falcon-LLM License

# Training details

For more details about the training protocol of this model, please refer to the [Falcon-H1 technical blogpost](https://falcon-lm.github.io/blog/falcon-h1/).

# Usage

Currently to use this model you can either rely on Hugging Face `transformers`, `vLLM` or `llama.cpp` library.

## Inference

Make sure to install the latest version of `transformers` or `vllm`, eventually install these packages from source:

```bash
pip install git+https://github.com/huggingface/transformers.git
```

For vLLM, make sure to install `vllm>=0.9.0`:

```bash
pip install "vllm>=0.9.0"
```
### 🤗 transformers

Refer to the snippet below to run H1 models using 🤗 transformers:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "tiiuae/Falcon-H1-1B-Base"

model = AutoModelForCausalLM.from_pretrained(
  model_id,
  torch_dtype=torch.bfloat16,
  device_map="auto"
)

# Perform text generation
```

### vLLM

For vLLM, simply start a server by executing the command below:

```
# pip install vllm>=0.9.0
vllm serve tiiuae/Falcon-H1-1B-Instruct --tensor-parallel-size 2 --data-parallel-size 1
```

### `llama.cpp`

You can find all GGUF files compatible with `llama.cpp` under [our official collection](https://huggingface.co/collections/tiiuae/falcon-h1-6819f2795bc406da60fab8df)

# Evaluation

Falcon-H1 series perform very well on a variety of tasks, including reasoning tasks. 

| Tasks | Falcon-H1-1.5B-deep | Qwen3-1.7B | Qwen2.5-1.5B | Gemma3-1B | Llama3.2-1B | Falcon3-1B |
| --- | --- | --- | --- | --- | --- | --- |
| **General**  | | | | | |
| BBH | **54.43** | 35.18 | 42.41 | 35.86 | 33.21 | 34.47 |
| ARC-C | **43.86** | 34.81 | 40.53 | 34.13 | 34.64 | 43.09 |
| TruthfulQA | **50.48** | 49.39 | 47.05 | 42.17 | 42.08 | 42.31 |
| HellaSwag | **65.54** | 49.27 | 62.23 | 42.24 | 55.3 | 58.53 |
| MMLU | **66.11** | 57.04 | 59.76 | 40.87 | 45.93 | 46.1 |
| **Math**  | | | | | |
| GSM8k | **82.34** | 69.83 | 57.47 | 42.38 | 44.28 | 44.05 |
| MATH-500 | **77.8** | 73.0 | 48.4 | 45.4 | 13.2 | 19.8 |
| AMC-23 | **56.56** | 46.09 | 24.06 | 19.22 | 7.19 | 6.87 |
| AIME-24 | **14.37** | 12.5 | 2.29 | 0.42 | 1.46 | 0.41 |
| AIME-25 | **11.04** | 8.12 | 1.25 | 1.25 | 0.0 | 0.21 |
| **Science**  | | | | | |
| GPQA | **33.22** | 27.68 | 26.26 | 28.19 | 26.59 | 26.76 |
| GPQA_Diamond | **40.57** | 33.33 | 25.59 | 21.55 | 25.08 | 31.31 |
| MMLU-Pro | **41.89** | 23.54 | 28.35 | 14.46 | 16.2 | 18.49 |
| MMLU-stem | **67.3** | 54.3 | 54.04 | 35.39 | 39.16 | 39.64 |
| **Code**  | | | | | |
| HumanEval | **73.78** | 67.68 | 56.1 | 40.85 | 34.15 | 22.56 |
| HumanEval+ | **68.9** | 60.96 | 50.61 | 37.2 | 29.88 | 20.73 |
| MBPP | **68.25** | 58.73 | 64.81 | 57.67 | 33.6 | 20.63 |
| MBPP+ | **56.61** | 49.74 | 56.08 | 50.0 | 29.37 | 17.2 |
| LiveCodeBench | **23.87** | 14.87 | 12.52 | 5.09 | 2.35 | 0.78 |
| CRUXEval | **52.32** | 18.88 | 34.76 | 12.7 | 0.06 | 15.58 |
| **Instruction Following**  | | | | | |
| IFEval | **83.5** | 70.77 | 45.33 | 61.48 | 55.34 | 54.26 |
| Alpaca-Eval | **27.12** | 21.89 | 9.54 | 17.87 | 9.38 | 6.98 |
| MTBench | **8.53** | 7.61 | 7.1 | 7.03 | 6.37 | 6.03 |
| LiveBench | 36.83 | **40.73** | 21.65 | 18.79 | 14.97 | 14.1 |

You can check more in detail on our [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/), detailed benchmarks.

# Useful links

- View [our release blogpost](https://falcon-lm.github.io/blog/falcon-h1/).
- 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 family of models were helpful to your work, feel free to give us a cite.

```
@misc{tiifalconh1,
    title = {Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance},
    url = {https://falcon-lm.github.io/blog/falcon-h1},
    author = {Falcon-LLM Team},
    month = {May},
    year = {2025}
}
```