Instructions to use Sayansantra/pytho25M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sayansantra/pytho25M with Transformers:

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

pipe = pipeline("text-generation", model="Sayansantra/pytho25M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sayansantra/pytho25M")
model = AutoModelForCausalLM.from_pretrained("Sayansantra/pytho25M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Sayansantra/pytho25M 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 Sayansantra/pytho25M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Sayansantra/pytho25M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sayansantra/pytho25M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Sayansantra/pytho25M: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 Sayansantra/pytho25M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Sayansantra/pytho25M: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 Sayansantra/pytho25M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sayansantra/pytho25M:Q4_K_M

Use Docker

docker model run hf.co/Sayansantra/pytho25M:Q4_K_M

LM Studio
Jan

vLLM

How to use Sayansantra/pytho25M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sayansantra/pytho25M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sayansantra/pytho25M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sayansantra/pytho25M:Q4_K_M

SGLang

How to use Sayansantra/pytho25M 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 "Sayansantra/pytho25M" \
    --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": "Sayansantra/pytho25M",
		"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 "Sayansantra/pytho25M" \
        --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": "Sayansantra/pytho25M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use Sayansantra/pytho25M with Ollama:
```
ollama run hf.co/Sayansantra/pytho25M:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Sayansantra/pytho25M with Docker Model Runner:
```
docker model run hf.co/Sayansantra/pytho25M:Q4_K_M
```

Lemonade

How to use Sayansantra/pytho25M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sayansantra/pytho25M:Q4_K_M

Run and chat with the model

lemonade run user.pytho25M-Q4_K_M

List all available models

lemonade list

Atomic Chat

pytho25M

File size: 717 Bytes

cdb3160

{
  "architectures": [
    "LlamaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 0,
  "dtype": "float32",
  "eos_token_id": 2,
  "head_dim": 64,
  "hidden_act": "silu",
  "hidden_size": 384,
  "initializer_range": 0.02,
  "intermediate_size": 1024,
  "max_position_embeddings": 512,
  "mlp_bias": false,
  "model_type": "llama",
  "num_attention_heads": 6,
  "num_hidden_layers": 14,
  "num_key_value_heads": 2,
  "pad_token_id": 1,
  "pretraining_tp": 1,
  "rms_norm_eps": 1e-06,
  "rope_parameters": {
    "rope_theta": 10000.0,
    "rope_type": "default"
  },
  "tie_word_embeddings": true,
  "transformers_version": "5.0.0",
  "use_cache": false,
  "vocab_size": 8000
}