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")
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

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
```

Model Card for Model ID

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

Model Details

Model Description

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

Direct Use

Use this model to generate Python code.

Out-of-Scope Use

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.

# 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

Fine-tuning Data: flytech/python-codes-25k

Training Procedure

Training Hyperparameters

Training regime: fp16
learning_rate: 2e-4

Evaluation

Testing Data & Metrics

Testing Data

iamtarun/python_code_instructions_18k_alpaca

Metrics

chrf: 0.73
codebleu: 0.67
codebleu_ngram: 0.53

Results

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

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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

Downloads last month: 3

Safetensors

Model size

3B params

Tensor type

F32

Paper for mrSoul7766/gemma-2b-it-python-code-gen-adapter

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45