---
title: Falcon-H1-34B-Instruct-GGUF
canonical_url: "https://www.modelscope.cn/models/unsloth/Falcon-H1-34B-Instruct-GGUF"
md_url: "https://www.modelscope.cn/models/unsloth/Falcon-H1-34B-Instruct-GGUF.md"
repository: unsloth/Falcon-H1-34B-Instruct-GGUF
last_updated: 2025-07-10
license: other
base_model:
  - tiiuae/Falcon-H1-34B-Instruct
base_model_relation: quantized
library_name:
  - pytorch
  - transformer
  - gguf
frameworks:
  - pytorch
downloads: 286
stars: 0
tags:
  - falcon-h1
  - unsloth
  - gguf
---

# Falcon-H1-34B-Instruct-GGUF

> Falcon-H1-34B-Instruct-GGUF - unsloth 在 ModelScope 开源的模型。[!NOTE] Includes our chat template fixes! For llama.cpp, use --jinja

unsloth/Falcon-H1-34B-Instruct-GGUF 是 ModelScope 魔搭社区上的机器学习模型，采用 other 许可，基于 tiiuae/Falcon-H1-34B-Instruct 构建。

- **Repository**: unsloth/Falcon-H1-34B-Instruct-GGUF
- **License**: other
- **Base model**: tiiuae/Falcon-H1-34B-Instruct
- **Tags**: falcon-h1, unsloth, gguf
- **Downloads**: 286
- **Stars**: 0
- **Last updated**: 2025-07-10

Source: https://www.modelscope.cn/models/unsloth/Falcon-H1-34B-Instruct-GGUF

---

> [!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 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-H1-34B | Qwen3-32B | Qwen2.5-72B | Qwen2.5-32B | Gemma3-27B | Llama3.3-70B | Llama4-scout |
| --- | --- | --- | --- | --- | --- | --- | --- |
| **General**  | | | | | | |
| BBH | 70.68 | 62.47 | **72.52** | 68.72 | 67.28 | 69.15 | 64.9 |
| ARC-C | 61.01 | 48.98 | 46.59 | 44.54 | 54.52 | **63.65** | 56.14 |
| TruthfulQA | 65.27 | 58.58 | 69.8 | **70.28** | 64.26 | 66.15 | 62.74 |
| HellaSwag | **81.94** | 68.89 | 68.79 | 73.95 | 57.25 | 70.24 | 65.03 |
| MMLU | 84.05 | 80.89 | **84.42** | 82.8 | 78.01 | 82.08 | 80.4 |
| **Math**  | | | | | | |
| GSM8k | 83.62 | 88.78 | 82.26 | 78.47 | 90.37 | **93.71** | 90.37 |
| MATH-500 | 83.8 | 82.0 | 83.6 | 82.2 | **90.0** | 70.6 | 83.2 |
| AMC-23 | 69.38 | 67.34 | 67.34 | 68.75 | **77.81** | 39.38 | 69.06 |
| AIME-24 | 23.75 | 27.71 | 17.29 | 17.92 | 27.5 | 12.92 | **27.92** |
| AIME-25 | 16.67 | 19.79 | 15.21 | 11.46 | **22.71** | 1.25 | 8.96 |
| **Science**  | | | | | | |
| GPQA | **41.53** | 30.2 | 37.67 | 34.31 | 36.49 | 31.99 | 31.8 |
| GPQA_Diamond | 49.66 | 49.49 | 44.95 | 40.74 | 47.47 | 42.09 | **51.18** |
| MMLU-Pro | **58.73** | 54.68 | 56.35 | 56.63 | 47.81 | 53.29 | 55.58 |
| MMLU-stem | **83.57** | 81.64 | 82.59 | 82.37 | 73.55 | 74.88 | 75.2 |
| **Code**  | | | | | | |
| HumanEval | 87.2 | **90.85** | 87.2 | 90.24 | 86.59 | 83.53 | 85.4 |
| HumanEval+ | 81.71 | **85.37** | 80.49 | 82.32 | 78.05 | 79.87 | 78.7 |
| MBPP | 83.86 | 86.24 | **89.68** | 87.83 | 88.36 | 88.09 | 81.5 |
| MBPP+ | 71.43 | 71.96 | **75.4** | 74.07 | 74.07 | 73.81 | 64.8 |
| LiveCodeBench | 49.71 | 45.01 | **54.6** | 49.12 | 39.53 | 40.31 | 40.12 |
| CRUXEval | 73.07 | **78.45** | 75.63 | 73.5 | 74.82 | 69.53 | 68.32 |
| **Instruction Following**  | | | | | | |
| IFEval | 89.37 | 86.97 | 86.35 | 81.79 | 83.19 | **89.94** | 86.32 |
| Alpaca-Eval | 48.32 | **64.21** | 49.29 | 39.26 | 56.16 | 38.27 | 36.26 |
| MTBench | **9.2** | 9.05 | 9.16 | 9.09 | 8.75 | 8.98 | 8.98 |
| LiveBench | 46.26 | **63.05** | 54.03 | 52.92 | 55.41 | 53.11 | 54.21 |

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}
}
```