Instructions to use sinimiini/HRM-Text-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sinimiini/HRM-Text-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sinimiini/HRM-Text-1B-GGUF",
	filename="HRM-Text-1B-BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sinimiini/HRM-Text-1B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sinimiini/HRM-Text-1B-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf sinimiini/HRM-Text-1B-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sinimiini/HRM-Text-1B-GGUF:BF16
# Run inference directly in the terminal:
llama-cli -hf sinimiini/HRM-Text-1B-GGUF:BF16

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 sinimiini/HRM-Text-1B-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf sinimiini/HRM-Text-1B-GGUF:BF16

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 sinimiini/HRM-Text-1B-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sinimiini/HRM-Text-1B-GGUF:BF16

Use Docker

docker model run hf.co/sinimiini/HRM-Text-1B-GGUF:BF16

LM Studio
Jan

vLLM

How to use sinimiini/HRM-Text-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sinimiini/HRM-Text-1B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sinimiini/HRM-Text-1B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sinimiini/HRM-Text-1B-GGUF:BF16

Ollama
How to use sinimiini/HRM-Text-1B-GGUF with Ollama:
```
ollama run hf.co/sinimiini/HRM-Text-1B-GGUF:BF16
```

Unsloth Studio new

How to use sinimiini/HRM-Text-1B-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 sinimiini/HRM-Text-1B-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 sinimiini/HRM-Text-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sinimiini/HRM-Text-1B-GGUF to start chatting

Docker Model Runner
How to use sinimiini/HRM-Text-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/sinimiini/HRM-Text-1B-GGUF:BF16
```

Lemonade

How to use sinimiini/HRM-Text-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sinimiini/HRM-Text-1B-GGUF:BF16

Run and chat with the model

lemonade run user.HRM-Text-1B-GGUF-BF16

List all available models

lemonade list

HRM-Text-1B-GGUF / reports /validation /quantization_report.md

sinimiini

Remove Q4 references from quantization report

d68d508 verified about 15 hours ago

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raw

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HRM-Text-1B GGUF Quantization Report

Date: 2026-05-21

Source

Source GGUF: fresh\out\gguf\HRM-Text-1B-BF16.gguf
Source GGUF SHA256: 2DD5E2EF55E40C46DB0D0CB4CF1427A4E72DA34FEE36F0D2B73D081D0E1C2010
Quantization tool: fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-quantize.exe
llama.cpp base commit: 6a257d44633d4a752183ed778b88d2924d0a6b9d
Importance matrix: not used

Generated Quantizations

Variant	File	Size (bytes)	SHA256	Validation	Upload
BF16	`HRM-Text-1B-BF16.gguf`	`2367995648`	`2DD5E2EF55E40C46DB0D0CB4CF1427A4E72DA34FEE36F0D2B73D081D0E1C2010`	Source of truth	Yes
Q8_0	`HRM-Text-1B-Q8_0.gguf`	`1259126560`	`C0729C267C3421E1F6DE0488AC5448E98EA30E56514DAF210596B70AC3F9786D`	Pass	Yes
Q6_K	`HRM-Text-1B-Q6_K.gguf`	`972668704`	`24D93CA4EF4A02CFE415E3EA56A78AD65198A165A4157B928004B58DBDA2D93C`	Pass	Yes
Q5_K_M	`HRM-Text-1B-Q5_K_M.gguf`	`851509024`	`F6CE71A076EC897174C555D810ED6E379767D52F9396D485B42E42BF8DB1D0B7`	Pass	Yes

Validation Summary

Each quantized GGUF was compared against the BF16 GGUF with the patched HRM-enabled runtime on four fixed prompts.

Variant	Token IDs	Top-1 matches	Min top-10 overlap	`hrm_text` metadata	New loop check	Result
Q8_0	Pass	`4/4`	`9/10`	Pass	Pass	Pass
Q6_K	Pass	`4/4`	`9/10`	Pass	Pass	Pass
Q5_K_M	Pass	`4/4`	`9/10`	Pass	Pass	Pass

Commands

fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-quantize.exe fresh\out\gguf\HRM-Text-1B-BF16.gguf fresh\out\gguf\HRM-Text-1B-Q8_0.gguf Q8_0 4
fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-quantize.exe fresh\out\gguf\HRM-Text-1B-BF16.gguf fresh\out\gguf\HRM-Text-1B-Q6_K.gguf Q6_K 4
fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-quantize.exe fresh\out\gguf\HRM-Text-1B-BF16.gguf fresh\out\gguf\HRM-Text-1B-Q5_K_M.gguf Q5_K_M 4

python fresh\tools\validate_quant_runtime.py --bf16 fresh\out\gguf\HRM-Text-1B-BF16.gguf --quant fresh\out\gguf\HRM-Text-1B-Q8_0.gguf --quant-name Q8_0 --llama-results fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-results.exe --llama-completion fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-completion.exe --out fresh\reports\validation\q8_0_vs_bf16.json --work-dir fresh\reports\validation\runtime_tmp\q8_0 --n-generate 32
python fresh\tools\validate_quant_runtime.py --bf16 fresh\out\gguf\HRM-Text-1B-BF16.gguf --quant fresh\out\gguf\HRM-Text-1B-Q6_K.gguf --quant-name Q6_K --llama-results fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-results.exe --llama-completion fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-completion.exe --out fresh\reports\validation\q6_k_vs_bf16.json --work-dir fresh\reports\validation\runtime_tmp\q6_k --n-generate 32
python fresh\tools\validate_quant_runtime.py --bf16 fresh\out\gguf\HRM-Text-1B-BF16.gguf --quant fresh\out\gguf\HRM-Text-1B-Q5_K_M.gguf --quant-name Q5_K_M --llama-results fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-results.exe --llama-completion fresh\third_party\llama.cpp\build-hrm-nmake\bin\llama-completion.exe --out fresh\reports\validation\q5_k_m_vs_bf16.json --work-dir fresh\reports\validation\runtime_tmp\q5_k_m --n-generate 32