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sighrobot/
ReaderLM-v2-q4f16_1-MLC

Name: ReaderLM-v2-q4f16_1-MLC
Availability: InStock

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ReaderLM-v2-q4f16_1-MLC

This is jinaai/ReaderLM-v2 compiled for WebLLM/MLC-LLM with q4f16_1 quantization.

Model Details

Base Model: jinaai/ReaderLM-v2 (based on Qwen2.5-1.5B-Instruct)
Quantization: q4f16_1 (4-bit weights, 16-bit activations)
Context Window: 8192 tokens
Total Size: ~850MB

Usage with WebLLM

typescript

import * as webllm from "@mlc-ai/web-llm";

const MODEL_ID = "ReaderLM-v2-q4f16_1-MLC";
const MODEL_URL = "https://huggingface.co/YOUR_USERNAME/ReaderLM-v2-q4f16_1-MLC/resolve/main";

const engine = await webllm.CreateMLCEngine(MODEL_ID, {
  appConfig: {
    model_list: [
      {
        model_id: MODEL_ID,
        model: MODEL_URL,
        model_lib: `${MODEL_URL}/model.wasm`,
      },
    ],
  },
});

const response = await engine.chat.completions.create({
  messages: [{ role: "user", content: "Your prompt here" }],
  temperature: 0.3,
});

console.log(response.choices[0].message.content);

Files

model.wasm - Compiled WebGPU model
params_shard_*.bin - Quantized model weights (30 shards)
mlc-chat-config.json - Model configuration
tokenizer.json, vocab.json, merges.txt - Tokenizer files

Compilation

This model was compiled using MLC-LLM with the following settings:

bash

# Convert weights
python -m mlc_llm convert_weight ./ReaderLM-v2 --quantization q4f16_1 --output ./dist

# Generate config
python -m mlc_llm gen_config ./ReaderLM-v2 --quantization q4f16_1 --conv-template chatml --context-window-size 8192 --output ./dist

# Compile for WebGPU
python -m mlc_llm compile ./dist --device webgpu --output ./dist/model.wasm

License

Same as the original ReaderLM-v2 model.

sighrobot/ReaderLM-v2-q4f16_1-MLC