Instructions to use LoneStriker/Newton-7B-5.0bpw-h6-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LoneStriker/Newton-7B-5.0bpw-h6-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="LoneStriker/Newton-7B-5.0bpw-h6-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("LoneStriker/Newton-7B-5.0bpw-h6-exl2")
model = AutoModelForMultimodalLM.from_pretrained("LoneStriker/Newton-7B-5.0bpw-h6-exl2")
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 LoneStriker/Newton-7B-5.0bpw-h6-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/Newton-7B-5.0bpw-h6-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/Newton-7B-5.0bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LoneStriker/Newton-7B-5.0bpw-h6-exl2

SGLang

How to use LoneStriker/Newton-7B-5.0bpw-h6-exl2 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 "LoneStriker/Newton-7B-5.0bpw-h6-exl2" \
    --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": "LoneStriker/Newton-7B-5.0bpw-h6-exl2",
		"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 "LoneStriker/Newton-7B-5.0bpw-h6-exl2" \
        --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": "LoneStriker/Newton-7B-5.0bpw-h6-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LoneStriker/Newton-7B-5.0bpw-h6-exl2 with Docker Model Runner:
```
docker model run hf.co/LoneStriker/Newton-7B-5.0bpw-h6-exl2
```

Newton-7B-5.0bpw-h6-exl2 / README.md

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	---
	license: other
	tags:
	- axolotl
	- finetune
	- qlora
	base_model: openchat/openchat-3.5-0106
	datasets:
	- hendrycks/competition_math
	- allenai/ai2_arc
	- camel-ai/physics
	- camel-ai/chemistry
	- camel-ai/biology
	- camel-ai/math
	- STEM-AI-mtl/Electrical-engineering
	- openbookqa
	- piqa
	- metaeval/reclor
	- mandyyyyii/scibench
	- derek-thomas/ScienceQA
	- sciq
	- TIGER-Lab/ScienceEval
	---
	![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/aimTTdmut59aZxOWQlkcC.jpeg)

	# 🔬👩‍🔬 Newton-7B

	This model is a fine-tuned version of [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) on datasets related to science.

	This model is fine-tuned using [QLoRa](https://arxiv.org/abs/2305.14314) and [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).

	This model's training was sponsored by [sablo.ai](https://sablo.ai).

	<details><summary>See axolotl config</summary>

	axolotl version: `0.3.0`
	```yaml
	base_model: openchat/openchat-3.5-0106
	model_type: MistralForCausalLM
	tokenizer_type: LlamaTokenizer
	is_mistral_derived_model: true

	load_in_8bit: false
	load_in_4bit: true
	strict: false


	datasets:
	- path: merged_all.json
	type:
	field_instruction: instruction
	field_output: output

	format: "GPT4 Correct User: {instruction}<\|end_of_turn\|>GPT4 Correct Assistant:"
	no_input_format: "GPT4 Correct User: {instruction}<\|end_of_turn\|>GPT4 Correct Assistant:"


	dataset_prepared_path: last_run_prepared
	val_set_size: 0.01 # not sure
	output_dir: ./newton

	adapter: qlora
	lora_model_dir:

	sequence_len: 8192
	sample_packing: true
	pad_to_sequence_len: true

	lora_r: 128
	lora_alpha: 64
	lora_dropout: 0.05
	lora_target_linear: true
	lora_fan_in_fan_out:
	lora_target_modules:
	- gate_proj
	- down_proj
	- up_proj
	- q_proj
	- v_proj
	- k_proj
	- o_proj
	lora_modules_to_save:
	- embed_tokens
	- lm_head

	wandb_project: huggingface
	wandb_entity:
	wandb_watch:
	wandb_name:
	wandb_log_model:

	hub_model_id: Weyaxi/newton-lora
	save_safetensors: true

	# change #
	gradient_accumulation_steps: 12
	micro_batch_size: 6
	num_epochs: 2
	optimizer: adamw_bnb_8bit
	lr_scheduler: cosine
	learning_rate: 0.0002
	# change #

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

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

	warmup_steps: 10 # not sure

	saves_per_epoch: 2

	evals_per_epoch: 4
	eval_table_size:
	eval_table_max_new_tokens: 128

	debug:
	deepspeed:
	weight_decay: 0.1 # not sure
	fsdp:
	fsdp_config:
	special_tokens:
	bos_token: "<s>"
	eos_token: "</s>"
	unk_token: "<unk>"
	tokens:
	- "<\|end_of_turn\|>"
	- "<\|pad_0\|>"
	```

	</details><br>

	# 📊 Datasets

	You can find the dataset I used and the work I am doing with this datasets here:

	https://huggingface.co/datasets/Weyaxi/sci-datasets

	Following datasets were used in this model:

	- 📐 [MATH](https://huggingface.co/datasets/hendrycks/competition_math)

	- 🧠 [ARC](https://huggingface.co/datasets/allenai/ai2_arc) (Note: Only train part)

	- 🧲 [camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics)

	- ⚗️ [camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry)

	- 🦠 [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology)

	- 📊 [camel-ai/math](https://huggingface.co/datasets/camel-ai/math)

	- ⚡ [STEM-AI-mtl/Electrical-engineering](https://huggingface.co/datasets/STEM-AI-mtl/Electrical-engineering)

	- 📚 [openbookqa](https://huggingface.co/datasets/openbookqa)

	- 🧠 [piqa](https://huggingface.co/datasets/piqa)

	- 🎨 [reclor](https://huggingface.co/datasets/metaeval/reclor)

	- 🔬 [scibench](https://github.com/mandyyyyii/scibench)

	- 🧪 [ScienceQA](https://huggingface.co/datasets/derek-thomas/ScienceQA)

	- 🧬 [sciq](https://huggingface.co/datasets/sciq)

	- 📝 [ScienceEval](https://huggingface.co/datasets/TIGER-Lab/ScienceEval)

	## 🛠️ Multiple Choice Question & Answer Datasets Conversion Progress

	I used [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) to generate a reasonable and logical answer by providing it with the question and the answer key.

	I used the [Together AI](https://www.together.ai) API for this task.

	The following datasets are converted using this method:

	- 🧠 [ARC](https://huggingface.co/datasets/allenai/ai2_arc) (Note: Only train part)

	- 📚 [openbookqa](https://huggingface.co/datasets/openbookqa)

	- 🎨 [reclor](https://huggingface.co/datasets/metaeval/reclor)

	- 🧬 [sciq](https://huggingface.co/datasets/sciq)

	# 💬 Prompt Template

	You can use this prompt template while using the model:

	### GPT4 Correct [(Openchat)](https://huggingface.co/openchat/openchat-3.5-0106#conversation-templates)

	```
	GPT4 Correct User: {user}<\|end_of_turn\|>GPT4 Correct Assistant: {asistant}<\|end_of_turn\|>GPT4 Correct User: {user}<\|end_of_turn\|>GPT4 Correct Assistant:
	```

	You can also utilize the chat template method from the tokenizer config like here:

	```python
	messages = [
	{"role": "user", "content": "Hello"},
	{"role": "assistant", "content": "Hi"},
	{"role": "user", "content": "How are you today?"}
	]
	tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
	```

	# 🤝 Acknowledgments

	Thanks to [openchat](https://huggingface.co/openchat) team for fine-tuning an excellent model that I used as a base model.

	Thanks to [@jondurbin](https://huggingface.co/jondurbin) for reformatting codes for some datasets: [bagel/data_sources](https://github.com/jondurbin/bagel/tree/main/bagel/data_sources)

	Thanks to [Together AI](https://www.together.ai) for providing everyone with free credits, which I used to generate a dataset in multiple choice to explanations format.

	Thanks to [Tim Dettmers](https://huggingface.co/timdettmers) for his excellent [QLoRA](https://arxiv.org/abs/2305.14314) work.

	Thanks to all the dataset authors mentioned in the datasets section.

	Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository I used to make this model.

	Overall, thanks to all of the open soure AI community! 🚀

	[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

	If you would like to support me:

	[☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi)