[whatmodel.sh](../../README.md) / Falcon-H1 34B Instruct
# Falcon-H1 34B Instruct
TII · released 2025-05 · 33.64B params · other (falcon-llm-license)
**score 55.0** ±2.6 · #283 of 732 · from 3 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Falcon-H1 34B Instruct among every model on that task or test, worst to best.
other tests (3)
test result vs the others in its run difficulty counts why
AraGen-v3 3C3H score (Inception, … 45.7% ▁▁·▁▂▂▄█●▅▄▄ beats 39/66 hard · avg 29% 43% at its level
HindiGen-v1 3C3H score (Inception… 53.6% ▁▁▁▁·▂·●▆█▇▁ beats 9/31 medium · avg 46% 39% at its level
Open PT-LLM Leaderboard average (… 78.6% ▃▁▁▁▁▁▁▁▂█●▁ beats 245/276 medium · avg 72% 15% at its level · noisy
source results share of the score
Inception Arabic/Hindi leaderboards 2 82% ▇▇▇▇▇▇▇▇▇▇
Open Portuguese LLM Leaderboard 1 15% ▇▇
prior: pull toward the middle 3% ▇
weighted average = 55.0
Licences: Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Falcon-H1 34B Instruct (Q4_K_M, needs ~20.3 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1-34B-Instruct-GGUF/tree/ceb29e9c241944eb64c44325972c4e9470271d92))
```sh
llama cli -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.8 GB) `llama cli -hf tiiuae/Falcon-H1-34B-Instruct-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Falcon-H1 34B Instruct (Q4_K_M, needs ~20.3 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1-34B-Instruct-GGUF/tree/ceb29e9c241944eb64c44325972c4e9470271d92))
```sh
lms get https://huggingface.co/tiiuae/Falcon-H1-34B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~35.8 GB) `lms get https://huggingface.co/tiiuae/Falcon-H1-34B-Instruct-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Falcon-H1 34B Instruct (Q4_K_M, needs ~20.3 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1-34B-Instruct-GGUF/tree/ceb29e9c241944eb64c44325972c4e9470271d92))
```sh
ollama run hf.co/tiiuae/Falcon-H1-34B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~35.8 GB) `ollama run hf.co/tiiuae/Falcon-H1-34B-Instruct-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~67.29 GB, from [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1-34B-Instruct/tree/6e6890e26cba1f58b1e6ee70d654ddf054f9fce8))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='tiiuae/Falcon-H1-34B-Instruct', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/tiiuae](https://huggingface.co/tiiuae/Falcon-H1-34B-Instruct)
[interactive version](https://whatmodel.sh/model/falcon-h1-34b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)