darkmaniac7/DeepSeek-R1-Distill-Llama-8B-MNN

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Published by TokForge for on-device inference.

TokForge

Runs on-device in the TokForge app.

DeepSeek-R1-Distill-Llama-8B-MNN

Pre-converted DeepSeek R1 Distill Llama 8B in MNN format for on-device inference with TokForge.

Original model by DeepSeek, converted to MNN Q4 for mobile deployment.

Model Details

Architecture Llama 3.1 (standard attention, 32 layers, GQA, distilled from DeepSeek-R1)
Parameters 8B (4-bit quantized)
Format MNN (Alibaba Mobile Neural Network)
Quantization W4A16 (4-bit weights, block size 128)
Vocab 128,256 tokens
Source deepseek-ai/DeepSeek-R1-Distill-Llama-8B

Description

DeepSeek's R1 reasoning capability distilled into a Llama 3.1 8B body. Brings chain-of-thought reasoning to mobile devices. Shows its thinking process step-by-step, making it excellent for math, logic puzzles, coding, and complex analysis.

Files

File Description
llm.mnn Model computation graph
llm.mnn.weight Quantized weight data (Q4, block=128)
llm_config.json Model config with Jinja chat template
tokenizer.txt Tokenizer vocabulary
config.json MNN runtime config

Usage with TokForge

This model is optimized for TokForge, a free Android app for private, on-device LLM inference.

  1. Download TokForge from Google Play
  2. Open the app → Models → Download this model
  3. Start chatting: runs 100% locally, no internet required

Recommended Settings

Setting Value
Backend OpenCL (Qualcomm) / Vulkan (MediaTek) / CPU (fallback)
Precision Low
Threads 4
Thinking Off (or On for thinking-capable models)

Credits

  • DeepSeek-R1-Distill-Llama-8B by DeepSeek (Hugging Face). Used under the MIT license.
  • Llama 3.1 8B by Meta (Hugging Face). Used under the Llama 3.1 Community License. The distilled weights derive from it.
  • Built with Llama. Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. The license text and this notice are in the LICENSE and NOTICE files in this repo.
  • Converted with llmexport from MNN by Alibaba (Apache-2.0).

Limitations

  • Intended for TokForge / MNN on-device inference on Android
  • This is a runtime bundle, not a standard Transformers training checkpoint
  • Quantization (Q4) may slightly reduce quality compared to the full-precision original

Community

Export Details

Converted using MNN's llmexport pipeline:

python llmexport.py --path deepseek-ai/DeepSeek-R1-Distill-Llama-8B --export mnn --quant_bit 4 --quant_block 128