Instructions to use Hakureirm/rwkv7-0.1b-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Hakureirm/rwkv7-0.1b-hf with Transformers:

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

pipe = pipeline("text-generation", model="Hakureirm/rwkv7-0.1b-hf", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Hakureirm/rwkv7-0.1b-hf", trust_remote_code=True, device_map="auto")

RWKV

How to use Hakureirm/rwkv7-0.1b-hf with RWKV:

# No code snippets available yet for this library.

# To use this model, check the repository files and the library's documentation.

# Want to help? PRs adding snippets are welcome at:
# https://github.com/huggingface/huggingface.js

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Hakureirm/rwkv7-0.1b-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hakureirm/rwkv7-0.1b-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Hakureirm/rwkv7-0.1b-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hakureirm/rwkv7-0.1b-hf

SGLang

How to use Hakureirm/rwkv7-0.1b-hf 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 "Hakureirm/rwkv7-0.1b-hf" \
    --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": "Hakureirm/rwkv7-0.1b-hf",
		"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 "Hakureirm/rwkv7-0.1b-hf" \
        --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": "Hakureirm/rwkv7-0.1b-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Hakureirm/rwkv7-0.1b-hf with Docker Model Runner:
```
docker model run hf.co/Hakureirm/rwkv7-0.1b-hf
```

rwkv7-0.1b-hf / README.md

Hakureirm

Ship the implementation as remote code (auto_map): stock transformers loads this repo directly; verified CPU + Apple MPS + CUDA

9ea6007 verified 6 days ago

preview code

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	---
	license: apache-2.0
	language:
	- en
	- zh
	library_name: transformers
	pipeline_tag: text-generation
	base_model: BlinkDL/rwkv-7-world
	tags:
	- rwkv
	- rwkv7
	- goose
	- linear-attention
	- recurrent
	---

	# RWKV-7 "Goose" 0.1B — converted for the in-tree `transformers` implementation

	This is not a new model. It is a format conversion of
	[`BlinkDL/rwkv-7-world`](https://huggingface.co/BlinkDL/rwkv-7-world) →
	`RWKV-x070-World-0.1B-v2.8-20241210-ctx4096.pth`, laid out as a `transformers`
	directory so that the `rwkv7` implementation can load it with `from_pretrained`.
	All credit for the weights belongs to BlinkDL / the RWKV project; they are
	redistributed here under the Apache-2.0 licence they were released under.

	## Why this exists

	RWKV-7 weights are published in two layouts, and until now neither loaded into the
	in-tree implementation:

	- the reference `.pth` — the same parameter names, but a bare `torch.save` of a flat
	dict rather than a `transformers` directory;
	- the `fla` layout — a `transformers` directory, but a `trust_remote_code` repo whose
	modelling code and tensor names come from `flash-linear-attention`, so it loads its
	own implementation.

	The conversion is a rename, not a transformation. Every one of the 399 tensors in the
	source `.pth` is bit-identical here; the only additions are three all-zero
	placeholders for layer 0's value-residual LoRA, which that layer never reads (layer 0
	produces `v_first` rather than mixing towards it) and which exist so the state dict
	is rectangular. `bfloat16`, the dtype the source is stored in.

	## Usage

	The implementation ships inside this repo (`auto_map` remote code), so stock
	`transformers` is all you need — no fork, no extra package:

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	tokenizer = AutoTokenizer.from_pretrained("RWKV/RWKV7-Goose-World2.8-0.1B-HF", trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained("Hakureirm/rwkv7-0.1b-hf", trust_remote_code=True, dtype=torch.bfloat16)

	inputs = tokenizer("The Eiffel Tower is located in the city of", return_tensors="pt")
	print(tokenizer.decode(model.generate(**inputs, max_new_tokens=20)[0]))
	```

	The same model class is also open as an in-tree PR
	([rwkv-rs/transformers-rwkv#2](https://github.com/rwkv-rs/transformers-rwkv/pull/2));
	this repo is the way to use it today on released transformers.

	## Runs on

	Pure-PyTorch portable path, no CUDA-only code on it. Verified, greedy 20/20 against
	BlinkDL's own runtime in each case: CPU (fp32/bf16, transformers 5.12.1),
	Apple Silicon MPS (fp32 and fp16), CUDA (the full test suite). Optional Triton
	kernels engage only on CUDA and fall back to the portable path everywhere else.

	RWKV-7 is attention-free and fully recurrent: the state is a fixed-size matrix per
	head, so there is no KV cache, memory is constant in context length, and each new
	token costs the same as the first.

	## Verification

	Checked against BlinkDL's own runtime — the `rwkv` package at `cpu fp32` with
	`RWKV_V7_ON=1`, not against the implementation being loaded here, which would be
	self-certifying, and not against `fla`, whose RWKV layer prints a warning on import
	saying it is potentially buggy and that results should be cross-checked against the
	official repository.

	Greedy, 20 tokens, from "The Eiffel Tower is located in the city of":

	\| loaded as \| tokens matching the reference runtime \|
	\|---\|---\|
	\| `float32` \| 20/20 \|
	\| `bfloat16` \| 20/20 \|

	Both produce `" Paris, France. It is the tallest building in the world and is the world's tallest"`.

	Logits agree with the reference to `7.06e-05` absolute and `1.97e-06` relative, with
	argmax agreement on every prompt position.

	0.1B is a useful size to check against for a reason beyond its download: it is 768 wide
	with `head_dim` 64, i.e. twelve heads of width 64, so the head count and the head
	width differ. At larger widths where both are 64, a quantity indexed by the wrong one
	of them agrees by coincidence.

	## Reproducing the conversion

	```bash
	huggingface-cli download BlinkDL/rwkv-7-world RWKV-x070-World-0.1B-v2.8-20241210-ctx4096.pth --local-dir .
	python src/transformers/models/rwkv7/convert_rwkv7_checkpoint_to_hf.py \
	--checkpoint RWKV-x070-World-0.1B-v2.8-20241210-ctx4096.pth \
	--flavour native --dtype bfloat16 --output_dir ./rwkv7-0.1b-hf
	```

	`--flavour fla` reads the safetensors layout instead. The converter compares the
	checkpoint's tensor shapes against the ones the config implies and refuses a
	disagreement, so a config that names a different model fails rather than producing
	something that loads and generates noise.

	## Citation

	The model is RWKV-7 "Goose" by Bo Peng (BlinkDL) and the RWKV community. Reference
	implementation: [BlinkDL/RWKV-LM](https://github.com/BlinkDL/RWKV-LM).