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

sinimiini commited on about 11 hours ago

Commit

143f0e6

verified ·

1 Parent(s): 7d2ba56

Remove Q4 references from README and delete Q4 report

Browse files

Files changed (1) hide show

README.md +0 -5

README.md CHANGED Viewed

@@ -70,7 +70,6 @@ Only the normal causal generation path is implemented in the patched runtime. Pr
 | `reports/validation/q8_0_vs_bf16.json` | `Q8_0` vs BF16 runtime validation |
 | `reports/validation/q6_k_vs_bf16.json` | `Q6_K` vs BF16 runtime validation |
 | `reports/validation/q5_k_m_vs_bf16.json` | `Q5_K_M` vs BF16 runtime validation |
-| `reports/validation/q4_k_m_vs_bf16.json` | Failed `Q4_K_M` validation report; the `Q4_K_M` GGUF is not uploaded |
 ## Provenance
@@ -92,8 +91,6 @@ Only the normal causal generation path is implemented in the patched runtime. Pr
 | Q6_K | `HRM-Text-1B-Q6_K.gguf` | `972668704` | `24D93CA4EF4A02CFE415E3EA56A78AD65198A165A4157B928004B58DBDA2D93C` |
 | Q5_K_M | `HRM-Text-1B-Q5_K_M.gguf` | `851509024` | `F6CE71A076EC897174C555D810ED6E379767D52F9396D485B42E42BF8DB1D0B7` |
-`Q4_K_M` was generated and tested locally but is not uploaded. It introduced a new single-token repetition loop for one validation prompt, so it failed the release gate.
 ## Validation Summary
 Validation was performed from a clean source snapshot and a clean `llama.cpp` base checkout.
@@ -114,7 +111,6 @@ Quantized variants were validated against the BF16 GGUF:
 | Q8_0 | Pass | `4/4` | `9/10` | Pass | Pass |
 | Q6_K | Pass | `4/4` | `9/10` | Pass | Pass |
 | Q5_K_M | Pass | `4/4` | `9/10` | Pass | Pass |
-| Q4_K_M | Pass | `3/4` | `8/10` | Fail | Not uploaded |
 Full-vocab mean absolute logit error:
@@ -160,7 +156,6 @@ Depending on the generator binary and `llama.cpp` build type, the executable may
 - `hrm_text` is a custom GGUF architecture in this conversion.
 - Generic GGUF runners will not work until they implement the HRM runtime graph.
 - Prefix-LM bidirectional attention with `token_type_ids` is not implemented in the patched `llama.cpp` path.
-- `Q4_K_M` is intentionally not included because strict validation found a new single-token repetition loop.
 ## License

 | `reports/validation/q8_0_vs_bf16.json` | `Q8_0` vs BF16 runtime validation |
 | `reports/validation/q6_k_vs_bf16.json` | `Q6_K` vs BF16 runtime validation |
 | `reports/validation/q5_k_m_vs_bf16.json` | `Q5_K_M` vs BF16 runtime validation |
 ## Provenance
 | Q6_K | `HRM-Text-1B-Q6_K.gguf` | `972668704` | `24D93CA4EF4A02CFE415E3EA56A78AD65198A165A4157B928004B58DBDA2D93C` |
 | Q5_K_M | `HRM-Text-1B-Q5_K_M.gguf` | `851509024` | `F6CE71A076EC897174C555D810ED6E379767D52F9396D485B42E42BF8DB1D0B7` |
 ## Validation Summary
 Validation was performed from a clean source snapshot and a clean `llama.cpp` base checkout.
 | Q8_0 | Pass | `4/4` | `9/10` | Pass | Pass |
 | Q6_K | Pass | `4/4` | `9/10` | Pass | Pass |
 | Q5_K_M | Pass | `4/4` | `9/10` | Pass | Pass |
 Full-vocab mean absolute logit error:
 - `hrm_text` is a custom GGUF architecture in this conversion.
 - Generic GGUF runners will not work until they implement the HRM runtime graph.
 - Prefix-LM bidirectional attention with `token_type_ids` is not implemented in the patched `llama.cpp` path.
 ## License