Instructions to use dkudos/cinimod-devops with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use dkudos/cinimod-devops 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 dkudos/cinimod-devops:Q8_0
# Run inference directly in the terminal:
llama cli -hf dkudos/cinimod-devops:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dkudos/cinimod-devops:Q8_0
# Run inference directly in the terminal:
llama cli -hf dkudos/cinimod-devops:Q8_0

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 dkudos/cinimod-devops:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf dkudos/cinimod-devops:Q8_0

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 dkudos/cinimod-devops:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dkudos/cinimod-devops:Q8_0

Use Docker

docker model run hf.co/dkudos/cinimod-devops:Q8_0

Llama new
LM Studio
Jan

vLLM

How to use dkudos/cinimod-devops with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dkudos/cinimod-devops"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dkudos/cinimod-devops",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dkudos/cinimod-devops:Q8_0

Ollama
How to use dkudos/cinimod-devops with Ollama:
```
ollama run hf.co/dkudos/cinimod-devops:Q8_0
```
Unsloth Desktop
Docker Model Runner
How to use dkudos/cinimod-devops with Docker Model Runner:
```
docker model run hf.co/dkudos/cinimod-devops:Q8_0
```

Lemonade

How to use dkudos/cinimod-devops with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dkudos/cinimod-devops:Q8_0

Run and chat with the model

lemonade run user.cinimod-devops-Q8_0

List all available models

lemonade list

Atomic Chat

dkudos commited on Sep 14

Commit

e83a4b1

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1 Parent(s): 86ba7c5

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@@ -47,7 +47,7 @@ A 287M-parameter decoder-only causal language model (Llama-3 style architecture)
 ### Evaluation
-- **Held-out full validation** (17,492 bins / 123,656 sequences @ 4096): mean eval loss ~2.30 (perplexity ~10). Final value in `full_val_eval.log`.
 - Note: the model is trained for exactly one epoch; a small train-eval gap is expected and the held-out score is the honest number.
 ## Files

 ### Evaluation
+- **Held-out full validation** (17,492 bins / 123,656 sequences @ 4096): mean eval loss **2.3163** (perplexity **10.14**). Full log in `full_val_eval.log`.
 - Note: the model is trained for exactly one epoch; a small train-eval gap is expected and the held-out score is the honest number.
 ## Files