Instructions to use LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2")
model = AutoModelForMultimodalLM.from_pretrained("LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2

SGLang

How to use LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2 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 "LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2" \
    --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": "LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2",
		"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 "LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2" \
        --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": "LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2
```

Gemmalpaca-2B

This is gemma-2b model supervised fine-tuned on the Open-Orca/SlimOrca-Dedup dataset. It's not as good as mlabonne/Gemmalpaca-2B.

🏆 Evaluation

Nous

Gemmalpaca-2B outperforms gemma-2b but underperforms gemma-2b-it on Nous' benchmark suite (evaluation performed using LLM AutoEval). See the entire leaderboard here.

Model	Average	AGIEval	GPT4All	TruthfulQA	Bigbench
mlabonne/Gemmalpaca-2B 📄	38.39	24.48	51.22	47.02	30.85
google/gemma-2b-it 📄	36.1	23.76	43.6	47.64	29.41
mlabonne/OrcaGemma-2B 📄	35.63	24.44	42.49	45.84	29.76
google/gemma-2b 📄	34.26	22.7	43.35	39.96	31.03

🧩 Configuration

It was trained using Axolotl with the following configuration.

base_model: google/gemma-2b
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: Open-Orca/SlimOrca-Dedup
    type: sharegpt

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: 4
micro_batch_size: 2
num_epochs: 2
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:

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:

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Model tree for LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2

Base model

google/gemma-2b

Finetuned

(297)

this model

LoneStriker
/

OrcaGemma-2B-6.0bpw-h6-exl2

Gemmalpaca-2B

🏆 Evaluation

Nous

🧩 Configuration

Model tree for LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2

Dataset used to train LoneStriker/OrcaGemma-2B-6.0bpw-h6-exl2