Instructions to use shahdishank/gemma-2b-it-finetune-python-codes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shahdishank/gemma-2b-it-finetune-python-codes with Transformers:

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

pipe = pipeline("text-generation", model="shahdishank/gemma-2b-it-finetune-python-codes")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shahdishank/gemma-2b-it-finetune-python-codes")
model = AutoModelForCausalLM.from_pretrained("shahdishank/gemma-2b-it-finetune-python-codes", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shahdishank/gemma-2b-it-finetune-python-codes with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shahdishank/gemma-2b-it-finetune-python-codes"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahdishank/gemma-2b-it-finetune-python-codes",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shahdishank/gemma-2b-it-finetune-python-codes

SGLang

How to use shahdishank/gemma-2b-it-finetune-python-codes 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 "shahdishank/gemma-2b-it-finetune-python-codes" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahdishank/gemma-2b-it-finetune-python-codes",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "shahdishank/gemma-2b-it-finetune-python-codes" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shahdishank/gemma-2b-it-finetune-python-codes",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use shahdishank/gemma-2b-it-finetune-python-codes with Docker Model Runner:
```
docker model run hf.co/shahdishank/gemma-2b-it-finetune-python-codes
```

shahdishank commited on Apr 2, 2024

Commit

ca84cf7

verified ·

1 Parent(s): 2b75165

Updated README

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README.md +5 -13

README.md CHANGED Viewed

@@ -22,21 +22,13 @@ This model card corresponds to the 2B finetuned version of the Gemma-2b-it model
 **Author**: Dishank Shah
-## Model Information
-Summary description and brief definition of inputs and outputs.
 ### Description
-Gemma is a family of lightweight, state-of-the-art open models from Google,
-built from the same research and technology used to create the Gemini models.
-They are text-to-text, decoder-only large language models, available in English,
-with open weights, pre-trained variants, and instruction-tuned variants. Gemma
-models are well-suited for a variety of text generation tasks, including
-question answering, summarization, and reasoning. Their relatively small size
-makes it possible to deploy them in environments with limited resources such as
-a laptop, desktop or your own cloud infrastructure, democratizing access to
-state of the art AI models and helping foster innovation for everyone.
 ### Usage

 **Author**: Dishank Shah
 ### Description
+GifPC-2b (Gemma-2b-it-finetuned-python-codes) LLM is trained on a dataset containing Python code snippets.
+This specialized training aimed to enhance Gemma-2b-it's understanding of Python syntax, semantics, and common programming patterns.
+With this finetuning, Gemma-2b-it is now proficient in not only comprehending Python code but also capable of assisting in debugging tasks.
+Users can leverage its trained knowledge to seek guidance on Python-related issues, understand code logic, and identify potential errors within their programs.
+This specialized Gemma-2b-it variant serves as a valuable tool for programmers seeking assistance and guidance in Python programming and debugging tasks.
 ### Usage