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

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

aorabdel commited on 13 days ago

Commit

7f33dc9

verified ·

1 Parent(s): e919adb

Sync model repo (text/metadata)

Browse files

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@@ -180,7 +180,7 @@ This model is intended for developers evaluating text-generation workloads on Ar
 ## Additional notes
-- Peak memory is Android USS read from /proc/<pid>/smaps_rollup, not the peak RSS that server-class evaluators report. The two are not interchangeable across targets.
 - Reasoning budget matters. Traces are long and the reported score was obtained with max_tokens 12288 at n_ctx 14336, while the shipped serving window is n_ctx 4096, sized for the phone's KV-cache budget. Smaller budgets truncate traces, and a truncated trace yields no answer at all. DeepSeek advises against a system prompt and against greedy decoding for the R1-distill family.
 ## About this version

 ## Additional notes
+- Peak memory is Android USS read from `/proc/<pid>/smaps_rollup`, not the peak RSS that server-class evaluators report. The two are not interchangeable across targets.
 - Reasoning budget matters. Traces are long and the reported score was obtained with max_tokens 12288 at n_ctx 14336, while the shipped serving window is n_ctx 4096, sized for the phone's KV-cache budget. Smaller budgets truncate traces, and a truncated trace yields no answer at all. DeepSeek advises against a system prompt and against greedy decoding for the R1-distill family.
 ## About this version