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

tinybpe1m

File size: 715 Bytes

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{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 1,
  "dtype": "float32",
  "eos_token_id": 2,
  "head_dim": 64,
  "hidden_act": "silu",
  "hidden_size": 128,
  "initializer_range": 0.02,
  "intermediate_size": 352,
  "max_position_embeddings": 256,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 2,
  "num_hidden_layers": 4,
  "num_key_value_heads": 2,
  "pad_token_id": 2,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "rope_theta": 10000.0,
    "rope_type": "default"
  },
  "tie_word_embeddings": false,
  "transformers_version": "5.9.0",
  "use_cache": false,
  "vocab_size": 512
}