Instructions to use mrSoul7766/gemma-2b-it-python-code-gen-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrSoul7766/gemma-2b-it-python-code-gen-adapter with Transformers:

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

pipe = pipeline("text-generation", model="mrSoul7766/gemma-2b-it-python-code-gen-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrSoul7766/gemma-2b-it-python-code-gen-adapter")
model = AutoModelForCausalLM.from_pretrained("mrSoul7766/gemma-2b-it-python-code-gen-adapter", 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 mrSoul7766/gemma-2b-it-python-code-gen-adapter with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrSoul7766/gemma-2b-it-python-code-gen-adapter"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrSoul7766/gemma-2b-it-python-code-gen-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrSoul7766/gemma-2b-it-python-code-gen-adapter

SGLang

How to use mrSoul7766/gemma-2b-it-python-code-gen-adapter 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 "mrSoul7766/gemma-2b-it-python-code-gen-adapter" \
    --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": "mrSoul7766/gemma-2b-it-python-code-gen-adapter",
		"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 "mrSoul7766/gemma-2b-it-python-code-gen-adapter" \
        --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": "mrSoul7766/gemma-2b-it-python-code-gen-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mrSoul7766/gemma-2b-it-python-code-gen-adapter with Docker Model Runner:
```
docker model run hf.co/mrSoul7766/gemma-2b-it-python-code-gen-adapter
```

gemma-2b-it-python-code-gen-adapter / README.md

mrSoul7766

Update README.md

55eb0a7 verified over 2 years ago

preview code

Raw

History Blame Contribute Delete

4.01 kB

	---
	library_name: transformers
	metrics:
	- bleu : 0.67
	- chrf : 0.73
	---

	# Model Card for Model ID

	This is the Gemma-2b-IT model fine-tuned for the Python code generation task.


	## Model Details

	### Model Description

	<!-- Provide a longer summary of what this model is. -->

	This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

	- Developed by: Mohammed Ashraf
	- Model type: google/gemma-2b
	- Finetuned from model [optional]: google/gemma-2b-it


	## Uses

	<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

	### Direct Use

	<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
	Use this model to generate Python code.



	### Out-of-Scope Use

	<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
	This model is trained on very basic Python code, so it might not be able to handle complex code.


	## How to Get Started with the Model

	Use the code below to get started with the model.

	```python
	# Load model directly
	from transformers import AutoTokenizer, AutoModelForCausalLM

	model_id = "mrSoul7766/gemma-2b-it-python-code-gen-adapter"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(model_id)

	text = """<start_of_turn>how to covert json to dataframe.<end_of_turn>
	<start_of_turn>model"""

	#device = "cuda:0"

	inputs = tokenizer(text, return_tensors="pt")


	outputs = model.generate(**inputs, max_new_tokens=200)
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))
	```


	## Training Details


	### Training Data

	<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

	Fine-tuning Data: [flytech/python-codes-25k](https://huggingface.co/datasets/flytech/python-codes-25k/viewer/default/train?p=2&row=294)


	### Training Procedure

	<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->


	#### Training Hyperparameters

	- Training regime: fp16 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
	- learning_rate: 2e-4

	## Evaluation
	<!-- This section describes the evaluation protocols and provides the results. -->


	### Testing Data & Metrics

	#### Testing Data

	<!-- This should link to a Dataset Card if possible. -->

	[iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca?row=44)


	#### Metrics

	<!-- These are the evaluation metrics being used, ideally with a description of why. -->

	- chrf: 0.73
	- codebleu: 0.67
	- codebleu_ngram: 0.53

	### Results

	```python
	import json
	import pandas as pd

	# Load the JSON data
	with open('data.json', 'r') as f:
	data = json.load(f)

	# Create the DataFrame
	df = pd.DataFrame(data)
	```

	#### Summary



	## Environmental Impact

	<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

	Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

	- Hardware Type: H100
	- Hours used: 30 minutes
	- Cloud Provider: Google-cloud


	## Technical Specifications [optional]

	### Model Architecture and Objective

	#### Hardware

	- Hardware Type: H100
	- Hours used: 30 minutes
	- Cloud Provider: Google-cloud

	#### Software

	- bitsandbytes==0.42.0
	- peft==0.8.2
	- trl==0.7.10
	- accelerate==0.27.1
	- datasets==2.17.0
	- transformers==4.38.0