Text Generation
GGUF
English
bf16
q8_0
q6_k
q5_k_m
quantized
llama.cpp
hrm
hierarchical-reasoning
prefix-lm
pre-alignment
non-chat
non-instruction-tuned
Instructions to use sinimiini/HRM-Text-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
Update model card for validated quantizations
Browse files
README.md
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tags:
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- gguf
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- bf16
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- quantized
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- llama.cpp
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- hrm
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# HRM-Text-1B GGUF
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This repository contains a BF16 GGUF conversion of [`sapientinc/HRM-Text-1B`](https://huggingface.co/sapientinc/HRM-Text-1B).
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The GGUF
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- `general.architecture = hrm_text`
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- BF16 tensor storage
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- the original tokenizer from `tokenizer.json`
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- no injected chat template
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| File | Description |
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| --- | --- |
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| `HRM-Text-1B-BF16.gguf` | BF16 GGUF conversion of `sapientinc/HRM-Text-1B` |
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| `runtime/llama.cpp-hrm_text.patch` | Patch adding `hrm_text` conversion and runtime support to the clean `llama.cpp` base commit |
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| `reports/validation/final_report.md` | Human-readable conversion and validation report |
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| `reports/validation/baseline_transformers.json` | Transformers baseline prompts, logits, and continuations |
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| `reports/validation/bf16_tensor_validation.json` | Tensor-level GGUF validation |
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| `reports/validation/bf16_vs_hf.json` | Runtime logit and text validation |
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## Provenance
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| Source model | `sapientinc/HRM-Text-1B` |
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| Source snapshot SHA | `2285b999f6fb8a5b16e0cc313a9e8e4fe447140d` |
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| Source `model.safetensors` SHA256 | `F8FE2B2BF6948414E8E8D6538659198726D98F967C55B533B7AABE8A1FA9A584` |
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| GGUF SHA256 | `2DD5E2EF55E40C46DB0D0CB4CF1427A4E72DA34FEE36F0D2B73D081D0E1C2010` |
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| GGUF size | `2,367,995,648` bytes |
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| llama.cpp base commit | `6a257d44633d4a752183ed778b88d2924d0a6b9d` |
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## Validation Summary
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Validation was performed from a clean source snapshot and a clean `llama.cpp` base checkout.
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| Top-10 overlap | `10/10` for all prompts |
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| Text validation | BF16 GGUF continuations are aligned with Transformers baseline |
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Full-vocab mean absolute logit error:
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| Prompt | MAE |
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- `hrm_text` is a custom GGUF architecture in this conversion.
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- Generic GGUF runners will not work until they implement the HRM runtime graph.
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- Prefix-LM bidirectional attention with `token_type_ids` is not implemented in the patched `llama.cpp` path.
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## License
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tags:
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- gguf
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- bf16
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- q8_0
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- q6_k
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- q5_k_m
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- quantized
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- llama.cpp
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- hrm
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# HRM-Text-1B GGUF
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This repository contains a BF16 GGUF conversion of [`sapientinc/HRM-Text-1B`](https://huggingface.co/sapientinc/HRM-Text-1B) and validated `Q8_0`, `Q6_K`, and `Q5_K_M` quantizations derived from that BF16 GGUF.
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The GGUF files use:
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- `general.architecture = hrm_text`
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- BF16 source tensor storage or standard `llama.cpp` quantized tensor storage
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- the original tokenizer from `tokenizer.json`
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- no injected chat template
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| File | Description |
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| --- | --- |
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| `HRM-Text-1B-BF16.gguf` | BF16 GGUF conversion of `sapientinc/HRM-Text-1B` |
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| `HRM-Text-1B-Q8_0.gguf` | Validated `Q8_0` quantization from BF16 |
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| `HRM-Text-1B-Q6_K.gguf` | Validated `Q6_K` quantization from BF16 |
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| `HRM-Text-1B-Q5_K_M.gguf` | Validated `Q5_K_M` quantization from BF16 |
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| `runtime/llama.cpp-hrm_text.patch` | Patch adding `hrm_text` conversion and runtime support to the clean `llama.cpp` base commit |
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| `reports/validation/final_report.md` | Human-readable conversion and validation report |
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| `reports/validation/quantization_report.md` | Quantization report, hashes, and pass/fail summary |
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| `reports/validation/baseline_transformers.json` | Transformers baseline prompts, logits, and continuations |
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| `reports/validation/bf16_tensor_validation.json` | Tensor-level GGUF validation |
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| `reports/validation/bf16_vs_hf.json` | Runtime logit and text validation |
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| `reports/validation/q8_0_vs_bf16.json` | `Q8_0` vs BF16 runtime validation |
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| `reports/validation/q6_k_vs_bf16.json` | `Q6_K` vs BF16 runtime validation |
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| `reports/validation/q5_k_m_vs_bf16.json` | `Q5_K_M` vs BF16 runtime validation |
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| `reports/validation/q4_k_m_vs_bf16.json` | Failed `Q4_K_M` validation report; the `Q4_K_M` GGUF is not uploaded |
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## Provenance
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| Source model | `sapientinc/HRM-Text-1B` |
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| Source snapshot SHA | `2285b999f6fb8a5b16e0cc313a9e8e4fe447140d` |
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| Source `model.safetensors` SHA256 | `F8FE2B2BF6948414E8E8D6538659198726D98F967C55B533B7AABE8A1FA9A584` |
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| BF16 GGUF SHA256 | `2DD5E2EF55E40C46DB0D0CB4CF1427A4E72DA34FEE36F0D2B73D081D0E1C2010` |
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| BF16 GGUF size | `2,367,995,648` bytes |
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| llama.cpp base commit | `6a257d44633d4a752183ed778b88d2924d0a6b9d` |
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## Available GGUF Files
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| Variant | File | Size (bytes) | SHA256 |
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| --- | --- | ---: | --- |
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| BF16 | `HRM-Text-1B-BF16.gguf` | `2367995648` | `2DD5E2EF55E40C46DB0D0CB4CF1427A4E72DA34FEE36F0D2B73D081D0E1C2010` |
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| Q8_0 | `HRM-Text-1B-Q8_0.gguf` | `1259126560` | `C0729C267C3421E1F6DE0488AC5448E98EA30E56514DAF210596B70AC3F9786D` |
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| Q6_K | `HRM-Text-1B-Q6_K.gguf` | `972668704` | `24D93CA4EF4A02CFE415E3EA56A78AD65198A165A4157B928004B58DBDA2D93C` |
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| Q5_K_M | `HRM-Text-1B-Q5_K_M.gguf` | `851509024` | `F6CE71A076EC897174C555D810ED6E379767D52F9396D485B42E42BF8DB1D0B7` |
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`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.
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## Validation Summary
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Validation was performed from a clean source snapshot and a clean `llama.cpp` base checkout.
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| Top-10 overlap | `10/10` for all prompts |
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| Text validation | BF16 GGUF continuations are aligned with Transformers baseline |
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Quantized variants were validated against the BF16 GGUF:
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| Variant | Token IDs | Top-1 matches | Min top-10 overlap | New loop check | Result |
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| --- | --- | ---: | ---: | --- | --- |
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| Q8_0 | Pass | `4/4` | `9/10` | Pass | Pass |
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| Q6_K | Pass | `4/4` | `9/10` | Pass | Pass |
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| Q5_K_M | Pass | `4/4` | `9/10` | Pass | Pass |
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| Q4_K_M | Pass | `3/4` | `8/10` | Fail | Not uploaded |
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Full-vocab mean absolute logit error:
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| Prompt | MAE |
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- `hrm_text` is a custom GGUF architecture in this conversion.
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- Generic GGUF runners will not work until they implement the HRM runtime graph.
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- Prefix-LM bidirectional attention with `token_type_ids` is not implemented in the patched `llama.cpp` path.
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- `Q4_K_M` is intentionally not included because strict validation found a new single-token repetition loop.
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## License
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