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")
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

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 new

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
```

trollek commited on Jul 8, 2024

Commit

c4d024d

verified ·

1 Parent(s): 27daf62

Update README.md

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README.md +75 -3

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@@ -1,3 +1,75 @@
----
-license: apache-2.0
----

+---
+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.
+## 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
+```