Instructions to use trollek/danube2-1.8b-MathInstruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use trollek/danube2-1.8b-MathInstruct with Transformers:

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

pipe = pipeline("text-generation", model="trollek/danube2-1.8b-MathInstruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("trollek/danube2-1.8b-MathInstruct")
model = AutoModelForCausalLM.from_pretrained("trollek/danube2-1.8b-MathInstruct", device_map="auto")
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 trollek/danube2-1.8b-MathInstruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "trollek/danube2-1.8b-MathInstruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "trollek/danube2-1.8b-MathInstruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/trollek/danube2-1.8b-MathInstruct

SGLang

How to use trollek/danube2-1.8b-MathInstruct 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 "trollek/danube2-1.8b-MathInstruct" \
    --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": "trollek/danube2-1.8b-MathInstruct",
		"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 "trollek/danube2-1.8b-MathInstruct" \
        --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": "trollek/danube2-1.8b-MathInstruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use trollek/danube2-1.8b-MathInstruct with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for trollek/danube2-1.8b-MathInstruct to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for trollek/danube2-1.8b-MathInstruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for trollek/danube2-1.8b-MathInstruct to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="trollek/danube2-1.8b-MathInstruct",
    max_seq_length=2048,
)

Docker Model Runner
How to use trollek/danube2-1.8b-MathInstruct with Docker Model Runner:
```
docker model run hf.co/trollek/danube2-1.8b-MathInstruct
```

danube2-1.8b-MathInstruct

File size: 3,029 Bytes

---
license: apache-2.0
base_model: h2oai/h2o-danube2-1.8b-base
datasets:
- TIGER-Lab/MathInstruct
language:
- en
library_name: transformers
tags:
- llama-factory
- unsloth
---
# h2o-danube2 with ChatML template

This model was first fine-tuned with [BAdam](https://arxiv.org/abs/2404.02827 "BAdam: A Memory Efficient Full Parameter Optimization Method for Large Language Models") on [TIGER-Lab/MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) using LLama-Factory.

## Quants

Mad props, [mradermacher](https://huggingface.co/mradermacher)!

- [mradermacher/danube2-1.8b-MathInstruct-GGUF](https://huggingface.co/mradermacher/danube2-1.8b-MathInstruct-GGUF)

## Template

```jinja
<|im_start|>system
You are a helpful assistant specialised in mathematics.<|im_end|>
<|im_start|>user
{{instruction}}<|im_end|>
<|im_start|>assistant
{{response}}<|im_end|>
```

## BAdam config

```yaml
### model
model_name_or_path: danube2-base-chatml

### method
stage: sft
do_train: true
finetuning_type: full
use_badam: true
badam_switch_mode: ascending
badam_switch_interval: 50
badam_verbose: 1
badam_start_block: 7
seed: 5772

### dataset
dataset: mathinstruct
template: ninja_chatml
cutoff_len: 8192
overwrite_cache: false
preprocessing_num_workers: 12

### output
output_dir: mathinstruct-chatml-badam
logging_steps: 5
save_steps: 1
save_strategy: epoch
plot_loss: true
overwrite_output_dir: false

### train
per_device_train_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 0.000005
num_train_epochs: 1
lr_scheduler_type: cosine
warmup_ratio: 0.01
pure_bf16: true
flash_attn: fa2

### eval
val_size: 0.01
per_device_eval_batch_size: 1
eval_strategy: steps
eval_steps: 1000
```

### BAdam training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.2748        | 0.0617 | 1000  | 0.2788          |
| 0.2786        | 0.1234 | 2000  | 0.2503          |
| 0.18          | 0.1850 | 3000  | 0.2144          |
| 0.2015        | 0.2467 | 4000  | 0.1926          |
| 0.2044        | 0.3084 | 5000  | 0.1777          |
| 0.142         | 0.3701 | 6000  | 0.1661          |
| 0.1813        | 0.4317 | 7000  | 0.1570          |
| 0.1413        | 0.4934 | 8000  | 0.1529          |
| 0.1805        | 0.5551 | 9000  | 0.1462          |
| 0.1431        | 0.6168 | 10000 | 0.1410          |
| 0.1693        | 0.6784 | 11000 | 0.1375          |
| 0.1291        | 0.7401 | 12000 | 0.1357          |
| 0.1501        | 0.8018 | 13000 | 0.1348          |
| 0.1521        | 0.8635 | 14000 | 0.1345          |
| 0.1279        | 0.9251 | 15000 | 0.1346          |
| 0.1351        | 0.9868 | 16000 | 0.1344          |

### GSM8K results

|Tasks|Version|     Filter     |n-shot|  Metric   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k|      3|strict-match    |     5|exact_match|0.2691|±  |0.0122|
|     |       |flexible-extract|     5|exact_match|0.2752|±  |0.0123|

It matches the chat trained model from h2o.