Instructions to use pthinc/cicikus_classic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pthinc/cicikus_classic with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="pthinc/cicikus_classic")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("pthinc/cicikus_classic")
model = AutoModelForCausalLM.from_pretrained("pthinc/cicikus_classic", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use pthinc/cicikus_classic 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 pthinc/cicikus_classic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pthinc/cicikus_classic:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf pthinc/cicikus_classic:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf pthinc/cicikus_classic:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf pthinc/cicikus_classic:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf pthinc/cicikus_classic:Q4_K_M

Use Docker

docker model run hf.co/pthinc/cicikus_classic:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use pthinc/cicikus_classic with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pthinc/cicikus_classic:Q4_K_M

SGLang

How to use pthinc/cicikus_classic with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "pthinc/cicikus_classic" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/cicikus_classic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "pthinc/cicikus_classic" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "pthinc/cicikus_classic",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use pthinc/cicikus_classic with Ollama:
```
ollama run hf.co/pthinc/cicikus_classic:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use pthinc/cicikus_classic with Docker Model Runner:
```
docker model run hf.co/pthinc/cicikus_classic:Q4_K_M
```

Lemonade

How to use pthinc/cicikus_classic with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull pthinc/cicikus_classic:Q4_K_M

Run and chat with the model

lemonade run user.cicikus_classic-Q4_K_M

List all available models

lemonade list

Atomic Chat

prometechinc commited on Mar 18

Commit

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1 Parent(s): ca54666

Update README.md

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@@ -116,7 +116,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## Training Configuration
 - **LoRA Rank:** 32
-- **Learning Rate:** 2e-4 (Cosine Scheduler)
 - **Hardware:** Optimized 1 Epoch training on a high-VRAM GPU.
 - **Format:** Instruct-based.

 ## Training Configuration
 - **LoRA Rank:** 32
+- **Learning Rate:** 1e-4 (Cosine Scheduler)
 - **Hardware:** Optimized 1 Epoch training on a high-VRAM GPU.
 - **Format:** Instruct-based.