Instructions to use thealper2/MiniCPM5-1B-Turkish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thealper2/MiniCPM5-1B-Turkish with Transformers:

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

pipe = pipeline("text-generation", model="thealper2/MiniCPM5-1B-Turkish")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thealper2/MiniCPM5-1B-Turkish")
model = AutoModelForCausalLM.from_pretrained("thealper2/MiniCPM5-1B-Turkish", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use thealper2/MiniCPM5-1B-Turkish 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 thealper2/MiniCPM5-1B-Turkish:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thealper2/MiniCPM5-1B-Turkish:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf thealper2/MiniCPM5-1B-Turkish:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf thealper2/MiniCPM5-1B-Turkish: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 thealper2/MiniCPM5-1B-Turkish:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf thealper2/MiniCPM5-1B-Turkish: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 thealper2/MiniCPM5-1B-Turkish:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf thealper2/MiniCPM5-1B-Turkish:Q4_K_M

Use Docker

docker model run hf.co/thealper2/MiniCPM5-1B-Turkish:Q4_K_M

LM Studio
Jan

vLLM

How to use thealper2/MiniCPM5-1B-Turkish with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thealper2/MiniCPM5-1B-Turkish"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/MiniCPM5-1B-Turkish",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thealper2/MiniCPM5-1B-Turkish:Q4_K_M

SGLang

How to use thealper2/MiniCPM5-1B-Turkish 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 "thealper2/MiniCPM5-1B-Turkish" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/MiniCPM5-1B-Turkish",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "thealper2/MiniCPM5-1B-Turkish" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thealper2/MiniCPM5-1B-Turkish",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use thealper2/MiniCPM5-1B-Turkish with Ollama:
```
ollama run hf.co/thealper2/MiniCPM5-1B-Turkish:Q4_K_M
```
Unsloth Desktop

How to use thealper2/MiniCPM5-1B-Turkish with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thealper2/MiniCPM5-1B-Turkish:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "thealper2/MiniCPM5-1B-Turkish:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use thealper2/MiniCPM5-1B-Turkish with Docker Model Runner:
```
docker model run hf.co/thealper2/MiniCPM5-1B-Turkish:Q4_K_M
```

Lemonade

How to use thealper2/MiniCPM5-1B-Turkish with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull thealper2/MiniCPM5-1B-Turkish:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM5-1B-Turkish-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use thealper2/MiniCPM5-1B-Turkish with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thealper2/MiniCPM5-1B-Turkish:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default thealper2/MiniCPM5-1B-Turkish:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use thealper2/MiniCPM5-1B-Turkish with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf thealper2/MiniCPM5-1B-Turkish:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "thealper2/MiniCPM5-1B-Turkish:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

thealper2 commited on 8 days ago

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Update README.md

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@@ -75,8 +75,6 @@ print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
 > Sohbet şablonu taban modelin ChatML türevidir (`<|im_start|>role\n...<|im_end|>`). Her zaman `apply_chat_template` kullanın; prompt'u elle kurmayın.
-> **Önemli - çözümleme ayarı / decoding.** Bu modeli **örnekleme ile** çalıştırın (`do_sample=True`, `temperature≈0.7`, `top_p=0.95`), taban modelin `generation_config.json` dosyasındaki gibi. Greedy çözümlemede (`do_sample=False`) hem taban model hem bu model tekrar döngüsüne girebilir; bu ölçüldü ve `evaluation/comparison.md` ile `evaluation_sampled/comparison.md` karşılaştırmasında görülebilir. / Run this model **with sampling**; greedy decoding makes both the base and this model degenerate into repetition.
 Model, yanıtlarına boş bir düşünme bloğu (`<think>\n\n</think>`) ile başlayacak şekilde eğitildi; bu, taban modelin `enable_thinking=False` biçimiyle uyumludur. Uzun zincirleme akıl yürütme (CoT) verisiyle eğitilmedi.
 ### llama.cpp / GGUF
@@ -155,20 +153,6 @@ Değerlendirme, taban model ile ince ayarlı model üzerinde **aynı prompt'lar
 - `code_js_01` (code_generation): 0/1 vs 1/1 checks
 - `en_code_02` (english_coding): 0/2 vs 2/2 checks
-### Ölçülen sinyaller / measured signals (örneklemeli çözümleme)
-| sinyal / signal | taban / base | ince ayarlı / fine-tuned |
-|---|---|---|
-| biçim/içerik kontrol oranı | 0.467 | 0.633 |
-| ortalama kelime | 129.500 | 73.900 |
-| tekrar oranı (düşük iyi) | 0.033 | 0.031 |
-| kod bloğu içeren yanıt | 5 | 9 |
-| boş yanıt | 0 | 0 |
-Bu ölçümler mekanik sinyallerdir (biçim kontrolleri, yanıt dili, Python sözdizimi, tekrar oranı). Bir kalite skoru **değildir**; yayınlamadan önce `comparison.md` dosyasındaki yanıtları okumanız beklenir.
-**Açık sözlü değerlendirme / honest read.** Denetimli ince ayar biçim ve dil öğretir, **bilgi öğretmez**. Bu model taban modele göre belirgin biçimde daha düzgün Türkçe üretiyor, kod bloklarını ve JSON'u doğru biçimlendiriyor ve talimat biçimine daha iyi uyuyor; ancak **olgusal içerik doğruluğu hâlâ zayıf** ve yanıtlar taban modele göre belirgin biçimde daha kısa. 1B parametre ve yaklaşık yarım epoch'luk bir SFT bütçesiyle beklenen sonuç budur. Türkçe bilgi kalitesini yükseltmek sürekli ön-eğitim (continued pretraining) gerektirir; bu proje kapsamına bilinçli olarak dahil edilmedi.
 ## Sınırlar ve riskler / Limitations
 - 1B parametreli küçük bir modeldir; olgusal doğruluk sınırlıdır ve **halüsinasyon görülebilir**.

 > Sohbet şablonu taban modelin ChatML türevidir (`<|im_start|>role\n...<|im_end|>`). Her zaman `apply_chat_template` kullanın; prompt'u elle kurmayın.
 Model, yanıtlarına boş bir düşünme bloğu (`<think>\n\n</think>`) ile başlayacak şekilde eğitildi; bu, taban modelin `enable_thinking=False` biçimiyle uyumludur. Uzun zincirleme akıl yürütme (CoT) verisiyle eğitilmedi.
 ### llama.cpp / GGUF
 - `code_js_01` (code_generation): 0/1 vs 1/1 checks
 - `en_code_02` (english_coding): 0/2 vs 2/2 checks
 ## Sınırlar ve riskler / Limitations
 - 1B parametreli küçük bir modeldir; olgusal doğruluk sınırlıdır ve **halüsinasyon görülebilir**.