Instructions to use Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M # Run inference directly in the terminal: llama cli -hf Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M # Run inference directly in the terminal: llama cli -hf Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
Use Docker
docker model run hf.co/Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
- Ollama
How to use Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300 with Ollama:
ollama run hf.co/Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300 with Docker Model Runner:
docker model run hf.co/Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
- Lemonade
How to use Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300:Q4_K_M
Run and chat with the model
lemonade run user.deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Sync model repo (text/metadata)
Browse files
README.md
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## About this version
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## About this version
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Original Model:
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deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B by DeepSeek AI - [Repository](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B)
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Optimization/conversion:
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Arm-Optimized version for execution on Arm-based platforms.
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Converted/optimized by:
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Arm
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License:
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The Original Model and the Optimized Model are subject to [MIT](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md).
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This repository contains a converted or optimized version of the Original Model (the “Optimized Model”). The Original Model has been converted or optimized as described above for execution on Arm-based platforms.
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No retraining or fine-tuning of the Original Model was performed as part of the conversion or optimization. The conversion or optimization was not intended to change the Original Model’s behavior or intended use.
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### Original Model and Documentation
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For information about the Original Model, including its development, training data, intended uses, limitations and other relevant information, please refer to the [Original Model repository](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B). Information in that repository was provided by the original developer or other third parties and, unless expressly stated otherwise, has not been independently verified by Arm.
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### Licenses and Third-Party Terms
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Use of the Original Model and the Optimized Model is subject to the applicable licenses, usage restrictions and other terms identified above and in the relevant repositories. Publication of the Optimized Model does not grant any rights beyond those provided under the applicable license terms.
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You are responsible for reviewing those terms and ensuring that your use of the Original Model and the Optimized Model is permitted.
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### Purpose of this Release
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The Optimized Model is provided as a reference implementation to demonstrate and evaluate execution and performance on Arm-based systems. It is not a production-ready or supported solution.
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Arm’s publication of the Optimized Model does not constitute an endorsement or certification of the Original Model or a representation that the Optimized Model is suitable for production use or any particular purpose.
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To the fullest extent permitted by applicable law (i) the Optimized Model is provided “as is.” Arm makes no representations or warranties that the Original Model, the Optimized Model or their outputs are accurate, safe, secure, non-infringing, legally compliant, suitable for production use or fit for any particular purpose; and (ii) Arm will not be liable for any loss or damage arising from or in connection with the Optimized Model, its use or its outputs.
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You are responsible for independently evaluating the Optimized Model, its outputs and its suitability for your intended use, including compliance with applicable legal, regulatory, safety and security requirements.
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Arm does not commit to provide ongoing support, maintenance or updates for the Optimized Model. Any use of or reliance on the Optimized Model or its outputs is at your own risk.
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benchmarks/deepseek-r1-distill-qwen-1-5b-llamacpp-vivo-x300-fp32.yaml
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prefill_encode_time_ms: 19284.2
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tokens_per_second: 10.008307
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peak_memory_mb: 3529.35546875
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prefill_encode_time_ms: 19284.2
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tokens_per_second: 10.008307
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peak_memory_mb: 3529.35546875
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accuracy:
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benchmark_name: MATH-500
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accuracy_pct: 82.8
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shot_count: 0
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benchmarks/deepseek-r1-distill-qwen-1-5b-llamacpp-vivo-x300-int4.yaml
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prefill_encode_time_ms: 2655.5
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tokens_per_second: 41.214719
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peak_memory_mb: 2012.0625
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prefill_encode_time_ms: 2655.5
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tokens_per_second: 41.214719
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peak_memory_mb: 2012.0625
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accuracy:
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benchmark_name: MATH-500
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accuracy_pct: 80.0
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shot_count: 0
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metadata.yaml
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schema_version: 1.0.0
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report_type: llm-generative
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task_type: text-generation
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title: DeepSeek-R1-Distill-Qwen-1.5B
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description: DeepSeek-R1-Distill-Qwen-1.5B text generation optimized as a Q4_K_M GGUF model for the llama.cpp runtime on Arm-based
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Premium Smartphone systems.
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id: Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300
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vendor: DeepSeek
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base_model_url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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profile: Arm-Optimized
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weight_dtype:
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quantization:
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method: K-quant
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symmetric: false
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schema_version: 1.0.0
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report_type: llm-generative
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task_type: text-generation
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title: DeepSeek-R1-Distill-Qwen-1.5B Q4_K_M
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description: DeepSeek-R1-Distill-Qwen-1.5B text generation optimized as a Q4_K_M GGUF model for the llama.cpp runtime on Arm-based
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Premium Smartphone systems.
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id: Arm/deepseek-r1-distill-qwen-1-5b-q4-k-m-llamacpp-vivo-x300
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vendor: DeepSeek
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base_model_url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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profile: Arm-Optimized
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weight_dtype: Q4_K_M
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quantization:
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method: K-quant
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symmetric: false
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