Instructions to use tomaszki/nous-gemma-five with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tomaszki/nous-gemma-five with Transformers:

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

pipe = pipeline("text-generation", model="tomaszki/nous-gemma-five")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("tomaszki/nous-gemma-five")
model = AutoModelForMultimodalLM.from_pretrained("tomaszki/nous-gemma-five")
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 tomaszki/nous-gemma-five with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tomaszki/nous-gemma-five

SGLang

How to use tomaszki/nous-gemma-five 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 "tomaszki/nous-gemma-five" \
    --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": "tomaszki/nous-gemma-five",
		"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 "tomaszki/nous-gemma-five" \
        --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": "tomaszki/nous-gemma-five",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tomaszki/nous-gemma-five with Docker Model Runner:
```
docker model run hf.co/tomaszki/nous-gemma-five
```

See axolotl config

axolotl version: 0.4.0

base_model: MesozoicMetallurgist/nous-Devonian
model_type: GemmaForCausalLM
hub_model_id: nous-gemma-five

load_in_8bit: false
load_in_4bit: false
strict: false


datasets:
  - path: tomaszki/gemma
  - path: tomaszki/gemma-1
  - path: tomaszki/gemma-2
  - path: tomaszki/gemma-3
  - path: tomaszki/gemma-4
  - path: tomaszki/gemma-5
val_set_size: 0.0
output_dir: out

sequence_len: 1024
sample_packing: false

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 15
micro_batch_size: 8
num_epochs: 2
optimizer: adamw_hf
lr_scheduler: cosine
learning_rate: 0.00001
cosine_min_lr_ratio: 0.5

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

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

warmup_steps: 0
saves_per_epoch: 1
debug:
deepspeed: #deepspeed_configs/zero2.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:

nous-gemma-five

This model is a fine-tuned version of MesozoicMetallurgist/nous-Devonian 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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 15
total_train_batch_size: 120
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
num_epochs: 2

Training results

Framework versions

Transformers 4.39.0.dev0
Pytorch 2.1.2+cu118
Datasets 2.17.1
Tokenizers 0.15.0

Downloads last month: 10

Safetensors

Model size

3B params

Tensor type

BF16