Instructions to use cturan/Lamba-750M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cturan/Lamba-750M-GGUF 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 cturan/Lamba-750M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cturan/Lamba-750M-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf cturan/Lamba-750M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cturan/Lamba-750M-GGUF: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 cturan/Lamba-750M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cturan/Lamba-750M-GGUF: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 cturan/Lamba-750M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cturan/Lamba-750M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/cturan/Lamba-750M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use cturan/Lamba-750M-GGUF with Ollama:
```
ollama run hf.co/cturan/Lamba-750M-GGUF:Q4_K_M
```

Unsloth Studio

How to use cturan/Lamba-750M-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cturan/Lamba-750M-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for cturan/Lamba-750M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cturan/Lamba-750M-GGUF to start chatting

How to use cturan/Lamba-750M-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cturan/Lamba-750M-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "cturan/Lamba-750M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use cturan/Lamba-750M-GGUF with Docker Model Runner:
```
docker model run hf.co/cturan/Lamba-750M-GGUF:Q4_K_M
```

Lemonade

How to use cturan/Lamba-750M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cturan/Lamba-750M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lamba-750M-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use cturan/Lamba-750M-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cturan/Lamba-750M-GGUF: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 cturan/Lamba-750M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use cturan/Lamba-750M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf cturan/Lamba-750M-GGUF: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 "cturan/Lamba-750M-GGUF: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"

Lamba-750M

Lamba-750M is a compact Turkish language model. It is based on Qwen3.5 and trained specifically for Turkish instruction following.

Intended Use

Designed for short Turkish tasks like classification, summarization, and text transformation. Not recommended for English prompts, long-form generation, math, or multi-step reasoning.

Training

Continual Pre-Training (CPT): 10B Turkish tokens to adapt to Turkish grammar and vocabulary.
Supervised Fine-Tuning (SFT): Trained on question-answer instructions. Best eval loss: 0.9285 at ~18k steps.
DPO: Direct Preference Optimization for better alignment with human preferences.

Capabilities

Small but effective for short Turkish tasks:

Summarization
Text classification (sentiment, topic)
Text transformation (informal to formal, antonyms)
Short, single-turn instructions

Limitations

Weak at math, logic, and multi-step reasoning. Do not use Chain-of-Thought, it increases hallucinations.
Can hallucinate facts on knowledge-heavy topics.
May repeat phrases in long generations.
Text-only, no vision support.

Downloads last month: 61

GGUF

Model size

0.8B params

Architecture

qwen35

Hardware compatibility

4-bit

8-bit

16-bit

Inference Providers NEW

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