Instructions to use KBlueLeaf/TIPO-200M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KBlueLeaf/TIPO-200M with Transformers:

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

pipe = pipeline("text-generation", model="KBlueLeaf/TIPO-200M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KBlueLeaf/TIPO-200M")
model = AutoModelForCausalLM.from_pretrained("KBlueLeaf/TIPO-200M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KBlueLeaf/TIPO-200M 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 KBlueLeaf/TIPO-200M:F16
# Run inference directly in the terminal:
llama cli -hf KBlueLeaf/TIPO-200M:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KBlueLeaf/TIPO-200M:F16
# Run inference directly in the terminal:
llama cli -hf KBlueLeaf/TIPO-200M:F16

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 KBlueLeaf/TIPO-200M:F16
# Run inference directly in the terminal:
./llama-cli -hf KBlueLeaf/TIPO-200M:F16

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 KBlueLeaf/TIPO-200M:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KBlueLeaf/TIPO-200M:F16

Use Docker

docker model run hf.co/KBlueLeaf/TIPO-200M:F16

LM Studio
Jan

vLLM

How to use KBlueLeaf/TIPO-200M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KBlueLeaf/TIPO-200M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "KBlueLeaf/TIPO-200M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KBlueLeaf/TIPO-200M:F16

SGLang

How to use KBlueLeaf/TIPO-200M 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 "KBlueLeaf/TIPO-200M" \
    --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": "KBlueLeaf/TIPO-200M",
		"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 "KBlueLeaf/TIPO-200M" \
        --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": "KBlueLeaf/TIPO-200M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use KBlueLeaf/TIPO-200M with Ollama:
```
ollama run hf.co/KBlueLeaf/TIPO-200M:F16
```
Unsloth Desktop
Docker Model Runner
How to use KBlueLeaf/TIPO-200M with Docker Model Runner:
```
docker model run hf.co/KBlueLeaf/TIPO-200M:F16
```

Lemonade

How to use KBlueLeaf/TIPO-200M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KBlueLeaf/TIPO-200M:F16

Run and chat with the model

lemonade run user.TIPO-200M-F16

List all available models

lemonade list

Atomic Chat

KBlueLeaf commited on Nov 21, 2024

Commit

17ecbbd

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1 Parent(s): ac6a884

Update README.md

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@@ -1,5 +1,5 @@
 ---
-license: other
 license_name: kohaku-license-1.0
 datasets:
 - laion/conceptual-captions-12m-webdataset
@@ -80,8 +80,7 @@ This test examine the ability of prompt gen method on handling almostly complete
 | AI Corrupt ↑ | 0.6868 | 0.6712 | 0.6741 | 0.5925 | **0.7130** |
 ## LICENSE
-This model is released under [Kohaku License 1.0](https://kblueleaf.net/documents/kohaku-license/?[Your%20Organization/Name]=KohakuBlueLeaf&[Year]=2024)<br>
-You can check the above provided URL or check the LICENSE file in this repo.
 ### Citation
 ```bibtex

 ---
+license: apache-2.0
 license_name: kohaku-license-1.0
 datasets:
 - laion/conceptual-captions-12m-webdataset
 | AI Corrupt ↑ | 0.6868 | 0.6712 | 0.6741 | 0.5925 | **0.7130** |
 ## LICENSE
+For research purpose, this model is released under Apache-2.0 License.
 ### Citation
 ```bibtex