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

Sayansantra commited on 17 days ago

Commit

00df46c

verified ·

1 Parent(s): c6032d3

Remove training hyperparameters and dataset details

Browse files

Files changed (1) hide show

README.md +2 -14

README.md CHANGED Viewed

@@ -13,8 +13,6 @@ tags:
 - text-generation
 pipeline_tag: text-generation
 inference: true
-datasets:
-- iamtarun/python_code_instructions_18k_alpaca
 model_format:
 - safetensors
 - gguf
@@ -23,7 +21,7 @@ params: 25103232
 # 🚀 Pytho 25M (Python Code Assistant)
-**Pytho 25M** (`Sayansantra/pytho25M`) is an ultra-compact ~25 Million parameter language model designed specifically for Python code generation and instruction following. Trained on 18,000 Python Alpaca instructions, Pytho 25M delivers fast, syntactically valid Python code snippets while using **under 30 MB of RAM**.
 Available in both **unquantized PyTorch Safetensors** and **4-bit quantized GGUF format**.
@@ -65,7 +63,7 @@ Empirical evaluation comparing **Pytho 25M** against open-source micro models un
 ## 🔍 Why Pytho 25M Outperforms Micro Competitors
-1. **Domain-Specific Instruction Tuning:** Trained on 18,000 Python Alpaca instruction-response pairs (`iamtarun/python_code_instructions_18k_alpaca`), allowing immediate zero-shot understanding of Python function generation prompts.
 2. **Vocabulary Parameter Allocation (8,000 vs 50,000 Tokens):** Standard models waste up to 76% of their weights storing 50,000 English vocabulary tokens. Pytho 25M uses an 8,000 Python BPE vocabulary, reserving 92% of its weights for 14 deep transformer layers.
 3. **Ultra-Low Memory Footprint:** Runs on CPU with under 30 MB of RAM at over 200 tokens per second.
@@ -116,16 +114,6 @@ print(response["choices"][0]["text"])
 ---
-## 🏋️ Training Details
-- **Dataset:** 18,000 Python Alpaca instructions (`iamtarun/python_code_instructions_18k_alpaca`)
-- **Optimizer:** AdamW (`lr=8e-4`, `weight_decay=0.01`)
-- **Epochs:** 10 Epochs
-- **Batch Size:** 32 (Effective batch size)
-- **Precision:** Mixed FP16
----
 ## 📜 Citation & License
 Developed by **Sayan Santra**. Released under the **Apache 2.0 License**.

 - text-generation
 pipeline_tag: text-generation
 inference: true
 model_format:
 - safetensors
 - gguf
 # 🚀 Pytho 25M (Python Code Assistant)
+**Pytho 25M** (`Sayansantra/pytho25M`) is an ultra-compact ~25 Million parameter language model designed specifically for Python code generation and instruction following. Pytho 25M delivers fast, syntactically valid Python code snippets while using **under 30 MB of RAM**.
 Available in both **unquantized PyTorch Safetensors** and **4-bit quantized GGUF format**.
 ## 🔍 Why Pytho 25M Outperforms Micro Competitors
+1. **Domain-Specific Instruction Tuning:** Tailored for Python instruction-response pairs, allowing immediate zero-shot understanding of Python function generation prompts.
 2. **Vocabulary Parameter Allocation (8,000 vs 50,000 Tokens):** Standard models waste up to 76% of their weights storing 50,000 English vocabulary tokens. Pytho 25M uses an 8,000 Python BPE vocabulary, reserving 92% of its weights for 14 deep transformer layers.
 3. **Ultra-Low Memory Footprint:** Runs on CPU with under 30 MB of RAM at over 200 tokens per second.
 ---
 ## 📜 Citation & License
 Developed by **Sayan Santra**. Released under the **Apache 2.0 License**.