Instructions to use mlabonne/Gemmalpaca-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mlabonne/Gemmalpaca-7B with Transformers:

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

pipe = pipeline("text-generation", model="mlabonne/Gemmalpaca-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/Gemmalpaca-7B")
model = AutoModelForCausalLM.from_pretrained("mlabonne/Gemmalpaca-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mlabonne/Gemmalpaca-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mlabonne/Gemmalpaca-7B

SGLang

How to use mlabonne/Gemmalpaca-7B 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 "mlabonne/Gemmalpaca-7B" \
    --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": "mlabonne/Gemmalpaca-7B",
		"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 "mlabonne/Gemmalpaca-7B" \
        --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": "mlabonne/Gemmalpaca-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mlabonne/Gemmalpaca-7B with Docker Model Runner:
```
docker model run hf.co/mlabonne/Gemmalpaca-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Gemmalpaca-7B

This is gemma-7b model supervised fine-tuned on the vicgalle/alpaca-gpt4 dataset. It outperforms gemma-7b-it, Google's chat version, on Nous' benchmark suite.

It's mostly a test to see how fine-tuning works with Gemma models on a well-known dataset.

🔍 Applications

This model has a context length of 8k. I recommend using it with the Alpaca chat template and NOT the Gemma Instruct template (works perfectly with LM Studio). You also want to add </s> as a stop token.

🏆 Evaluation

Nous

Gemmalpaca-7B outperforms gemma-7b and gemma-7b-it on Nous' benchmark suite (evaluation performed using LLM AutoEval). See the entire leaderboard here.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/Gemmalpaca-7B 📄	34.45	21.6	40.87	44.85	30.49
google/gemma-2b 📄	34.26	22.7	43.35	39.96	31.03
google/gemma-7b 📄	33.56	20.64	38.49	46.61	28.51
google/gemma-7b-it 📄	33.53	21.33	40.84	41.7	30.25

🧩 Configuration

It was trained using Axolotl with the following configuration.

base_model: alpindale/gemma-7b
model_type: AutoModelForCausalLM
tokenizer_config: philschmid/gemma-tokenizer-chatml
tokenizer_type: AutoTokenizer
tokenizer_use_fast: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca

dataset_prepared_path:
val_set_size: 0.01
output_dir: ./out

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

adapter: qlora
lora_model_dir:
lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 10
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:

Downloads last month: 8

Safetensors

Model size

9B params

Tensor type

F16

Model tree for mlabonne/Gemmalpaca-7B

Base model

google/gemma-7b

Finetuned

(366)

this model

Quantizations

2 models

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Gemmalpaca-7B