Instructions to use DownFlow/webasmr-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use DownFlow/webasmr-llm with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'DownFlow/webasmr-llm');
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Download README.md from DownFlow/webasmr-llm: direct link, hf CLI and curl.
- Browser
- Download file 769 Bytes
-
https://huggingface.co/DownFlow/webasmr-llm/resolve/main/README.md
- Command line
-
hf download hf://DownFlow/webasmr-llm/README.md
-
curl -L -o README.md https://huggingface.co/DownFlow/webasmr-llm/resolve/main/README.md
769 Bytes
metadata
library_name: transformers.js
base_model: Qwen/Qwen3.5-0.8B
tags:
- qwen
- asmr
- lora
- onnx
- webgpu
WebASMR director (Qwen3.5-0.8B + LoRA, q4f16)
Browser director for the WebASMR night studio. The DownFlow/webasmr-qwen3.5-0.8b-lora
adapter was merged into the text-only base and requantized to 4-bit blockwise
ONNX (MatMulNBits, block 32) without changing the hybrid-cache graph.
onnx/model_q4f16.onnx+onnx/model_q4f16.onnx_data: prefill/decode graph- tokenizer/config from
onnx-community/Qwen3.5-0.8B-Text-ONNX - prompt format: raw
Scenario: … / Tone: … / Tags: …, then[tone: - generation: temperature 0.8, top_p 0.9, repetition 1.1, stop at
<|im_end|>
The adapter is trained on a purchased ASMR corpus; use is non-commercial.