RWKV7-G1j-1.5B-GGUF

RWKV-7 “Goose” for local inference with Ollama and llama.cpp

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This repository provides the RWKV-7 G1j 1.5B base model in GGUF format for local text generation. Start with Q4_K_M for the smallest download, use the included Ollama setup with the locally tested Q6_K file, or choose another file to balance storage, runtime memory, and weight precision.

This is a base language model, not a safety-aligned instruction-tuned assistant. The prompt formats below provide a conversational interface, but the model may not follow instructions consistently.

About RWKV-7

RWKV-7, also called Goose, is an attention-free recurrent language model. It keeps a constant-size recurrent state instead of an attention KV cache that grows with the preceding sequence. This gives RWKV constant recurrent-state size and constant inference work per generated token, while training remains parallelizable.

The fixed-state property describes the architecture's sequence state. Total memory use still depends on the selected weights, runtime, context configuration, batch size, and hardware backend.

G1j identifies this checkpoint revision. The original checkpoint is available from BlinkDL/rwkv7-g1. That immutable source file is 3,055,444,605 bytes with SHA-256 c43176881caf85fe22ad654ab02e7519260d560f3d20420ab590adb0c823860f.

Choose a file

File	Size	When to choose it
rwkv7-g1j-1.5b-Q4_K_M.gguf	0.95 GiB	Smallest provided file; choose it when download size and runtime memory matter most.
rwkv7-g1j-1.5b-Q5_K_M.gguf	1.09 GiB	A middle option between Q4 and Q6.
rwkv7-g1j-1.5b-Q6_K.gguf	1.24 GiB	Default for the included Ollama setup and the locally tested release profile.
rwkv7-g1j-1.5b-Q8_0.gguf	1.58 GiB	Largest quantized option in this repository.
rwkv7-g1j-1.5b-20260831-ctx16384-FP16.gguf	2.87 GiB	Mostly-F16 weights; use when model size is less important.

Smaller quantizations generally reduce storage and runtime memory at the cost of some output quality. File size is not a guarantee of total RAM or VRAM use.

In the Q4_K_M and Q5_K_M files, token_embd.weight and output.weight use Q6_K while the remaining quantized tensors follow the file's named preset.

File integrity

File	Bytes	SHA-256
rwkv7-g1j-1.5b-Q4_K_M.gguf	1,024,655,520	f7f479fbdf768e3ba99287af6e4d3dfb78efdb7da6dde2063ab63253796cb57f
rwkv7-g1j-1.5b-Q5_K_M.gguf	1,175,650,464	8a3affea20ee87664ed6f13d5a606b576c11f834561103897dda088c8c802600
rwkv7-g1j-1.5b-Q6_K.gguf	1,336,082,592	832d04ad19c1abad3a42ee6b53863d7401d2150967e4de9e14871e8cc39922d2
rwkv7-g1j-1.5b-Q8_0.gguf	1,693,647,008	c41b0dc20705dc8d9746ecc24cea90c28c391049152fd772d50ead707b2f2241
rwkv7-g1j-1.5b-20260831-ctx16384-FP16.gguf	3,077,767,328	d212485511d06b6762382ba27088e6b4db4af10251952f1feb354265541e21d6

Verify a downloaded file with sha256sum <filename> on Linux or Get-FileHash <filename> -Algorithm SHA256 in PowerShell.

Run with Ollama

Install Ollama 0.33.2 or later with RWKV-7 support. The following path uses the Q6_K file and the included Modelfile.ollama.

1. Download the model

With the Hugging Face hf CLI:

hf download shoumenchougou/RWKV7-G1j-1.5B-GGUF rwkv7-g1j-1.5b-Q6_K.gguf Modelfile.ollama --local-dir rwkv7-g1j-1.5b
cd rwkv7-g1j-1.5b

You can instead download both files from the repository's file browser and place them in the same directory.

2. Create and run the Ollama model

ollama create rwkv7-g1j:1.5b -f Modelfile.ollama
ollama run rwkv7-g1j:1.5b

The setup is complete when ollama create reports success and ollama run returns generated text. Q6_K was tested on Windows with Ollama 0.33.2, CPU inference, and a 16,384-token context setting.

The included Modelfile defines the tested Q6_K file, minimum Ollama version, license summary, context length, sampling defaults, and stop sequence:

FROM ./rwkv7-g1j-1.5b-Q6_K.gguf
REQUIRES 0.33.2
LICENSE """
Apache License 2.0
https://www.apache.org/licenses/LICENSE-2.0
"""
PARAMETER num_ctx 16384
PARAMETER temperature 1
PARAMETER top_k 128
PARAMETER top_p 0.5
PARAMETER frequency_penalty 0.5
PARAMETER presence_penalty 0.5
PARAMETER repeat_penalty 1
PARAMETER stop """

"""

The Modelfile intentionally does not define TEMPLATE. All five GGUF files contain the same Jinja chat template in tokenizer.chat_template; Ollama and llama.cpp can read it directly from the selected model. Adding an external Ollama TEMPLATE would override that embedded source of truth.

Keep the stop value on three lines as shown. Ollama 0.33.2 parses the triple-quoted blank line as two newline characters; writing the escaped text \n\n instead stores literal backslashes.

See Ollama's official guides for importing a GGUF model and the complete Modelfile reference.

Run with llama.cpp

Use a recent llama.cpp build that recognizes the rwkv7 architecture. From the directory containing the GGUF file, run:

llama-cli -m rwkv7-g1j-1.5b-Q4_K_M.gguf -c 16384 -n 256 -cnv

The GGUF contains its chat template, so current llama.cpp builds can use conversation mode without a separate template file. A local llama.cpp build 8419 successfully recognized the GGUF and its RWKV-7 architecture; direct llama.cpp generation was not part of that local validation.

Prompt format

Runtimes that honor the embedded chat template apply the format automatically. When enable_thinking is omitted, the template ends at Assistant:. When it is false, the template adds an empty <think> block; when it is true, it opens a <think> block for generation.

For raw completion interfaces, use one of these generation prefixes.

Standard response:

User: Explain why recurrent models can use constant state.

Assistant:

Thinking response:

User: Solve the problem step by step.

Assistant: <think>

If your runtime requires explicit stop conditions, use token ID 0 or the text sequence \n\nUser:.

Context length

Use 16,384 tokens as the release context setting. Some GGUF inspection tools may display 1,048,576 from the RWKV-7 architecture metadata; that value is an architectural capacity field, not a claim that this checkpoint was trained or validated at that length.

If your runtime cannot allocate the requested context, lower the context value. This reduces the amount of preceding text available to the model.

Model details

Field	Value
Architecture	RWKV-7 “Goose”
Checkpoint revision	G1j, released 2026-08-31
Model type	Base causal language model
Original checkpoint parameters	1,527,668,736
GGUF tensor elements	1,527,799,808
Layers	24
Hidden size	2,048
FFN size	8,192
Heads / head size	32 / 64
Vocabulary size	65,536
Release context	16,384 tokens
Format	GGUF v3; 678 tensors
License	Apache License 2.0

The effective head count is 32 (2,048 / 64). GGUF inspectors may also show rwkv7.attention.head_count = 0; llama.cpp reserves that field but does not use it for RWKV-7.

Compatibility and troubleshooting

Symptom	What to do
The runtime reports an unknown rwkv7 architecture	Update to a recent Ollama or llama.cpp build with RWKV-7 support.
Ollama cannot find the GGUF file	Keep Modelfile.ollama and the Q6_K file in the same directory, or change FROM to the correct path.
A valid GGUF cannot be opened from a Windows path	Move the files to a short ASCII-only path such as C:\models\rwkv7 and try again.
The model does not follow the requested instruction	Confirm that the embedded template is active, or use the raw prompt format above. Remember that this is a base model.
The model does not fit in memory	Start with Q4_K_M, close other memory-heavy applications, or reduce the configured context length.

Runtime support changes over time. Q6_K import and generation have been smoke-tested with Ollama 0.33.2; other files, runtimes, accelerators, and operating systems may behave differently.

Limitations

• Quantization can change model outputs, and the effect varies by task.
• This release does not claim systematic benchmark parity between quantizations.
• The model may produce inaccurate, biased, unsafe, or otherwise undesirable text.
• The model is not a substitute for qualified professional judgment in high-stakes settings.
• The presence of chat and thinking prompts does not make the base model reliably instruction-following or safety-aligned.

License

The model is distributed under the Apache License 2.0. Review the source repository and license terms before redistribution or deployment.

Links

• RWKV website
• Original G1j checkpoint
• RWKV-LM repository
• RWKV-7 paper
• llama.cpp
• Ollama documentation

Citation

@misc{peng2025250314456,
  title         = {RWKV-7 "Goose" with Expressive Dynamic State Evolution},
  author        = {Bo Peng and Ruichong Zhang and Daniel Goldstein and Eric Alcaide and Xingjian Du and Haowen Hou and Jiaju Lin and Jiaxing Liu and Janna Lu and William Merrill and Guangyu Song and Kaifeng Tan and Saiteja Utpala and Nathan Wilce and Johan S. Wind and Tianyi Wu and Daniel Wuttke and Christian Zhou-Zheng},
  year          = {2025},
  eprint        = {2503.14456v2},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2503.14456v2},
}