Instructions to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF",
	filename="Falcon-H1R-7B-bf16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF to start chatting

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.tiiuae_Falcon-H1R-7B-GGUF-Q4_K_M

List all available models

lemonade list

tiiuae_Falcon-H1R-7B-GGUF

Falcon-H1R-7B from TII (Technology Innovation Institute) is a 7-billion-parameter reasoning-specialized causal decoder-only model built on the Falcon-H1-7B-Base foundation, featuring a hybrid Transformer + Mamba2 architecture trained via cold-start supervised fine-tuning with long reasoning traces and scaled RL using GRPO (Generalized Reward Preference Optimization) for exceptional performance in mathematics, programming, instruction following, and general logic. It achieves state-of-the-art results among <8B models across benchmarks like 88.1% on AIME24 (96.7% with test-time scaling), 68.6% on LiveCodeBench v5-v6, 61.3% on GPQA-Diamond, 72.1% on MMLU-Pro, and 53.4% on IFBench—often matching or exceeding 14B-47B competitors like Qwen3-32B, Phi-4-14B, and Nemotron-H-47B while enabling 2x faster inference (e.g., ~1800 tokens/s/GPU at batch=64) and up to 262k context length with low memory footprint. Optimized for multilingual use (English primary, trained on 18 languages including Arabic, Hindi, Chinese) under Falcon-LLM License, it generates structured ... reasoning blocks followed by final answers, deployable via Transformers (temperature=0.6, top_p=0.95, max_new_tokens=65536), vLLM (>=0.11.0, --reasoning-parser deepseek_r1), or SGLang for efficient real-world applications on TP=2 setups.

Quick Start with llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF",
    filename="Falcon-H1R-7B.Q4_K_M.gguf",
)

llm.create_chat_completion(
    messages = [
        {
            "role": "user",
            "content": "What is the capital of France?"
        }
    ]
)

Falcon-H1R-7B [GGUF]

File Name	Quant Type	File Size	File Link
Falcon-H1R-7B-bf16.gguf	BF16	15.2 GB	Download
Falcon-H1R-7B-f32.gguf	F32	30.3 GB	Download
Falcon-H1R-7B.IQ4_XS.gguf	IQ4_XS	4.19 GB	Download
Falcon-H1R-7B.Q2_K.gguf	Q2_K	2.89 GB	Download
Falcon-H1R-7B.Q3_K_L.gguf	Q3_K_L	3.92 GB	Download
Falcon-H1R-7B.Q3_K_M.gguf	Q3_K_M	3.69 GB	Download
Falcon-H1R-7B.Q3_K_S.gguf	Q3_K_S	3.43 GB	Download
Falcon-H1R-7B.Q4_K_M.gguf	Q4_K_M	4.6 GB	Download
Falcon-H1R-7B.Q4_K_S.gguf	Q4_K_S	4.4 GB	Download
Falcon-H1R-7B.Q5_K_M.gguf	Q5_K_M	5.39 GB	Download
Falcon-H1R-7B.Q5_K_S.gguf	Q5_K_S	5.28 GB	Download
Falcon-H1R-7B.Q6_K.gguf	Q6_K	6.23 GB	Download
Falcon-H1R-7B.Q8_0.gguf	Q8_0	8.07 GB	Download
Falcon-H1R-7B.f16.gguf	F16	15.2 GB	Download

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

Downloads last month: 511

GGUF

Model size

8B params

Architecture

falcon-h1

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for prithivMLmods/tiiuae_Falcon-H1R-7B-GGUF

Base model

tiiuae/Falcon-H1-7B-Base

Finetuned

tiiuae/Falcon-H1R-7B

Quantized

(16)

this model