Instructions to use diverWayne/mikky-64m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use diverWayne/mikky-64m with Transformers:

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

pipe = pipeline("text-generation", model="diverWayne/mikky-64m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("diverWayne/mikky-64m")
model = AutoModelForCausalLM.from_pretrained("diverWayne/mikky-64m", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use diverWayne/mikky-64m 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 diverWayne/mikky-64m:BF16
# Run inference directly in the terminal:
llama cli -hf diverWayne/mikky-64m:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf diverWayne/mikky-64m:BF16
# Run inference directly in the terminal:
llama cli -hf diverWayne/mikky-64m:BF16

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 diverWayne/mikky-64m:BF16
# Run inference directly in the terminal:
./llama-cli -hf diverWayne/mikky-64m:BF16

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 diverWayne/mikky-64m:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf diverWayne/mikky-64m:BF16

Use Docker

docker model run hf.co/diverWayne/mikky-64m:BF16

LM Studio
Jan

vLLM

How to use diverWayne/mikky-64m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "diverWayne/mikky-64m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "diverWayne/mikky-64m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/diverWayne/mikky-64m:BF16

SGLang

How to use diverWayne/mikky-64m 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 "diverWayne/mikky-64m" \
    --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": "diverWayne/mikky-64m",
		"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 "diverWayne/mikky-64m" \
        --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": "diverWayne/mikky-64m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use diverWayne/mikky-64m with Ollama:
```
ollama run hf.co/diverWayne/mikky-64m:BF16
```

Unsloth Studio

How to use diverWayne/mikky-64m 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 diverWayne/mikky-64m 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 diverWayne/mikky-64m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for diverWayne/mikky-64m to start chatting

Docker Model Runner
How to use diverWayne/mikky-64m with Docker Model Runner:
```
docker model run hf.co/diverWayne/mikky-64m:BF16
```

Lemonade

How to use diverWayne/mikky-64m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull diverWayne/mikky-64m:BF16

Run and chat with the model

lemonade run user.mikky-64m-BF16

List all available models

lemonade list

Atomic Chat

diverWayne commited on May 10

Commit

5ef9c3d

verified ·

1 Parent(s): 5b10fec

Update model card license and dataset citation

Browse files

Files changed (1) hide show

README.md +14 -4

README.md CHANGED Viewed

@@ -2,7 +2,7 @@
 language:
 - zh
 - en
-license: cc-by-nc-2.0
 pipeline_tag: text-generation
 library_name: transformers
 tags:
@@ -92,7 +92,17 @@ The GGUF structure and metadata were verified locally. Always verify generation
 - It is not suitable for high-stakes medical, legal, financial, or safety-critical use.
 - GRPO/PPO are not part of the final release checkpoint.
-## License And Data
-The upstream MiniMind dataset page includes both Apache-2.0 and CC-BY-NC-2.0 license metadata.
-This release is marked conservatively as **CC-BY-NC-2.0** until all upstream data-license constraints are independently verified.

 language:
 - zh
 - en
+license: apache-2.0
 pipeline_tag: text-generation
 library_name: transformers
 tags:
 - It is not suitable for high-stakes medical, legal, financial, or safety-critical use.
 - GRPO/PPO are not part of the final release checkpoint.
+## Dataset And License
+This model was trained with the MiniMind small-data recipe from
+[`jingyaogong/minimind_dataset`](https://huggingface.co/datasets/jingyaogong/minimind_dataset).
+For this release, the dataset reference follows the MiniMind small dataset license: **Apache-2.0**.
+Main data files used by this run:
+- `pretrain_t2t_mini.jsonl`: pretraining data.
+- `sft_t2t_mini.jsonl`: supervised fine-tuning data.
+- `dpo.jsonl`: preference data for DPO.
+- `lora_identity_mikky.jsonl`: project-authored identity/persona data for mikky.
+The model card, exported native checkpoint, Safetensors checkpoint, and GGUF artifact are released under **Apache-2.0**.