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 / README.md

trollek

Update README.md

26624a9 verified about 2 years ago

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metadata

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 on TIGER-Lab/MathInstruct using LLama-Factory.

Quants

Mad props, mradermacher!

mradermacher/danube2-1.8b-MathInstruct-GGUF

Template

<|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

### 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.