Instructions to use amitsaka76/gemma-7b-coding-assistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amitsaka76/gemma-7b-coding-assistant with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
model = PeftModel.from_pretrained(base_model, "amitsaka76/gemma-7b-coding-assistant")

Transformers

How to use amitsaka76/gemma-7b-coding-assistant with Transformers:

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

pipe = pipeline("text-generation", model="amitsaka76/gemma-7b-coding-assistant")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amitsaka76/gemma-7b-coding-assistant", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amitsaka76/gemma-7b-coding-assistant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amitsaka76/gemma-7b-coding-assistant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amitsaka76/gemma-7b-coding-assistant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/amitsaka76/gemma-7b-coding-assistant

SGLang

How to use amitsaka76/gemma-7b-coding-assistant 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 "amitsaka76/gemma-7b-coding-assistant" \
    --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": "amitsaka76/gemma-7b-coding-assistant",
		"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 "amitsaka76/gemma-7b-coding-assistant" \
        --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": "amitsaka76/gemma-7b-coding-assistant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use amitsaka76/gemma-7b-coding-assistant with Docker Model Runner:
```
docker model run hf.co/amitsaka76/gemma-7b-coding-assistant
```

gemma-7b-coding-assistant

This model is a fine-tuned version of google/gemma-7b on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2
training_steps: 200
mixed_precision_training: Native AMP

Training results

Framework versions

PEFT 0.17.1
Transformers 4.56.2
Pytorch 2.8.0+cu126
Datasets 4.1.1
Tokenizers 0.22.0

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google/gemma-7b

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