Text Generation
Transformers
TensorBoard
Safetensors
gemma3_text
Generated from Trainer
conversational
text-generation-inference
Instructions to use Scale-or-Reason/gemma3-1B_0_split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Scale-or-Reason/gemma3-1B_0_split with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Scale-or-Reason/gemma3-1B_0_split") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Scale-or-Reason/gemma3-1B_0_split") model = AutoModelForCausalLM.from_pretrained("Scale-or-Reason/gemma3-1B_0_split") 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 Scale-or-Reason/gemma3-1B_0_split with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Scale-or-Reason/gemma3-1B_0_split" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Scale-or-Reason/gemma3-1B_0_split", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Scale-or-Reason/gemma3-1B_0_split
- SGLang
How to use Scale-or-Reason/gemma3-1B_0_split 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 "Scale-or-Reason/gemma3-1B_0_split" \ --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": "Scale-or-Reason/gemma3-1B_0_split", "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 "Scale-or-Reason/gemma3-1B_0_split" \ --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": "Scale-or-Reason/gemma3-1B_0_split", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Scale-or-Reason/gemma3-1B_0_split with Docker Model Runner:
docker model run hf.co/Scale-or-Reason/gemma3-1B_0_split
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library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: lustre/fswork/projects/rech/dgo/udv55np/ift/Nemotron-Super-49B-v1_5/gemma-3-1b/0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.12.2`
```yaml
base_model: /lustre/fswork/projects/rech/qwv/udv55np/Gemma/base/gemma-3-1b
datasets:
- path: /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking
ds_type: json
type: chat_template
field_messages: conversations
data_files:
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0007.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0009.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0005.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0006.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0014.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0010.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0012.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0008.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0001.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0002.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0013.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0015.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0004.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0011.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0000.jsonl
- /lustre/fswork/projects/rech/qwv/udv55np/dataset/ift/Nemotron-Super-49B-v1_5/no_thinking/0003.jsonl
dataset_prepared_path: /lustre/fswork/projects/rech/dgo/udv55np/dataset_gemma/Nemotron-Super-49B-v1_5/split_0
tokenizer_config: "/lustre/fswork/projects/rech/qwv/udv55np/Gemma/base/gemma-3-27b"
chat_template: gemma3
eot_tokens:
- "<end_of_turn>"
shuffle_merged_datasets: true
output_dir: /lustre/fswork/projects/rech/dgo/udv55np/ift/Nemotron-Super-49B-v1_5/gemma-3-1b/0
sequence_len: 16384
sample_packing: true
gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 0.6
auto_resume_from_checkpoints: true
optimizer: adamw_torch_fused
lr_scheduler: warmup_stable_decay
learning_rate: 2e-5
lr_scheduler_kwargs:
num_decay_steps: 200
min_lr_ratio: 0.1
warmup_steps: 100
bf16: true
tf32: false
gradient_checkpointing: true
logging_steps: 10
flash_attention: true
evals_per_epoch: 0
saves_per_epoch: 1
save_total_limit: 20
save_only_model: true
use_tensorboard: true
deepspeed: /lustre/fswork/projects/rech/qwv/udv55np/axolotl/zero3.json
```
</details><br>
# lustre/fswork/projects/rech/dgo/udv55np/ift/Nemotron-Super-49B-v1_5/gemma-3-1b/0
This model was trained from scratch 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: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: warmup_stable_decay
- lr_scheduler_warmup_steps: 100
- training_steps: 711
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.1
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