Instructions to use shibatch/tinybpe1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shibatch/tinybpe1m with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shibatch/tinybpe1m", dtype="auto")

llama-cpp-python

How to use shibatch/tinybpe1m with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shibatch/tinybpe1m",
	filename="tinybpe1m.BF16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use shibatch/tinybpe1m with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shibatch/tinybpe1m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf shibatch/tinybpe1m:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shibatch/tinybpe1m:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf shibatch/tinybpe1m: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 shibatch/tinybpe1m:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf shibatch/tinybpe1m: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 shibatch/tinybpe1m:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shibatch/tinybpe1m:Q4_K_M

Use Docker

docker model run hf.co/shibatch/tinybpe1m:Q4_K_M

LM Studio
Jan
Ollama
How to use shibatch/tinybpe1m with Ollama:
```
ollama run hf.co/shibatch/tinybpe1m:Q4_K_M
```

Unsloth Studio

How to use shibatch/tinybpe1m 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 shibatch/tinybpe1m 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 shibatch/tinybpe1m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shibatch/tinybpe1m to start chatting

Docker Model Runner
How to use shibatch/tinybpe1m with Docker Model Runner:
```
docker model run hf.co/shibatch/tinybpe1m:Q4_K_M
```

Lemonade

How to use shibatch/tinybpe1m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shibatch/tinybpe1m:Q4_K_M

Run and chat with the model

lemonade run user.tinybpe1m-Q4_K_M

List all available models

lemonade list

shibatch commited on about 19 hours ago

Commit

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@@ -48,8 +48,10 @@ A comprehensive validation suite converted for `llama.cpp` and compatible engine
 This directory contains the standard files required to load the model using the PyTorch `transformers` library:
 * **`hf/model.safetensors`**: The raw, unquantized model weights stored securely in Safetensors format.
 * **`hf/config.json`**: The architectural configuration file defining hyperparameters (layers, heads, dimensions).
-* **`hf/generation_config.json`**: Default parameters optimized for text generation (temperature, top_p, etc.).
-* **`hf/tokenizer.model`**: The custom 512-vocab SentencePiece tokenizer model file required for Python-side encoding/decoding.
 ---

 This directory contains the standard files required to load the model using the PyTorch `transformers` library:
 * **`hf/model.safetensors`**: The raw, unquantized model weights stored securely in Safetensors format.
 * **`hf/config.json`**: The architectural configuration file defining hyperparameters (layers, heads, dimensions).
+* **`hf/generation_config.json`**: Default parameters optimized for text generation.
+* **`hf/tokenizer_config.json`**: Tokenizer behavior layout enabling automatic BOS token injection and padding setup.
+* **`hf/special_tokens_map.json`**: Architectural mappings tying token strings to exact internal special token IDs.
+* **`hf/tokenizer.model`**: The custom 512-vocab SentencePiece tokenizer model file.
 ---