Instructions to use QueryloopAI/gemma-2b-openhermes with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QueryloopAI/gemma-2b-openhermes with Transformers:

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

pipe = pipeline("text-generation", model="QueryloopAI/gemma-2b-openhermes")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("QueryloopAI/gemma-2b-openhermes")
model = AutoModel.from_pretrained("QueryloopAI/gemma-2b-openhermes")
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 QueryloopAI/gemma-2b-openhermes with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QueryloopAI/gemma-2b-openhermes"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QueryloopAI/gemma-2b-openhermes",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QueryloopAI/gemma-2b-openhermes

SGLang

How to use QueryloopAI/gemma-2b-openhermes 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 "QueryloopAI/gemma-2b-openhermes" \
    --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": "QueryloopAI/gemma-2b-openhermes",
		"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 "QueryloopAI/gemma-2b-openhermes" \
        --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": "QueryloopAI/gemma-2b-openhermes",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QueryloopAI/gemma-2b-openhermes with Docker Model Runner:
```
docker model run hf.co/QueryloopAI/gemma-2b-openhermes
```

gemma-2b-openhermes / README.md

abideen

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	---
	license: cc-by-nc-4.0
	base_model: google/gemma-2b-it
	tags:
	- generated_from_trainer
	- axolotl
	- gemma
	- instruct
	- finetune
	- chatml
	- gpt4
	- synthetic data
	- distillation
	model-index:
	- name: gemma-2b-openhermes
	results: []
	datasets:
	- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
	language:
	- en
	library_name: transformers
	pipeline_tag: text-generation
	---
	<!-- 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. -->

	# gemma-2b-openhermes


	![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64e380b2e12618b261fa6ba0/9bmxL8Lt7hBaKlKHVxtew.jpeg)

	gemma-2b-openhermes is a variant of the Gemma 2B language model, which has been further fine-tuned on the OpenHermes-2.5 preference dataset
	using QLoRA.


	* [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it)
	* [mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha)

	</details><br>

	## Usage

	### Chat Template

	The instruction-tuned models use a chat template that must be adhered to for conversational use.
	The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet.

	Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction:

	```py
	from transformers import AutoTokenizer, AutoModelForCausalLM
	import transformers
	import torch

	model_id = "abideen/gemma-2b-openhermes"
	dtype = torch.bfloat16

	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	device_map="cuda",
	torch_dtype=dtype,
	)

	chat = [{ "role": "user", "content": "What is a Language Model?" }]
	prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
	```

	After the prompt is ready, generation can be performed like this:

	```py
	inputs = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt")
	outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=250)
	print(tokenizer.decode(outputs[0]))
	```

	### Inputs and outputs

	* Input: Text string, such as a question, a prompt, or a document to be
	summarized.
	* Output: Generated English-language text in response to the input, such
	as an answer to a question, or a summary of a document.

	## 🏆 Evaluation results

	# Nous Benchmark

	Agieval

	\| Task \| Version \| Metric \| Value \| \| StdErr \|
	\|-------------------------------------------\|---------\|--------\|-------\|---\|---------\|
	\| agieval\_aqua\_rat \| 0 \| acc \| 24.02 \| _ \| 2.69 \|
	\| agieval\_aqua\_rat \| 0 \| acc\_norm \| 24.02 \| _ \| 2.69 \|
	\| agieval\_logiqa\_en \| 0 \| acc \| 23.20 \| _ \| 1.66 \|
	\| agieval\_logiqa\_en \| 0 \| acc\_norm \| 24.42 \| _ \| 1.69 \|
	\| agieval\_lsat\_ar \| 0 \| acc \| 18.26 \| _ \| 2.55 \|
	\| agieval\_lsat\_ar \| 0 \| acc\_norm \| 18.70 \| _ \| 2.58 \|
	\| agieval\_lsat\_lr \| 0 \| acc \| 22.35 \| _ \| 1.85 \|
	\| agieval\_lsat\_lr \| 0 \| acc\_norm \| 23.53 \| _ \| 1.88 \|
	\| agieval\_lsat\_rc \| 0 \| acc \| 20.82 \| _ \| 2.48 \|
	\| agieval\_lsat\_rc \| 0 \| acc\_norm \| 20.07 \| _ \| 2.45 \|
	\| agieval\_sat\_en \| 0 \| acc \| 32.52 \| _ \| 3.27 \|
	\| agieval\_sat\_en \| 0 \| acc\_norm \| 32.52 \| _ \| 3.27 \|
	\| agieval\_sat\_en\_without\_passage \| 0 \| acc \| 25.73 \| _ \| 3.05 \|
	\| agieval\_sat\_en\_without\_passage \| 0 \| acc\_norm \| 24.27 \| _ \| 2.99 \|
	\| agieval\_sat\_math \| 0 \| acc \| 25.00 \| _ \| 2.93 \|
	\| agieval\_sat\_math \| 0 \| acc\_norm \| 20.91 \| _ \| 2.75 \|
	Average: 24.11

	GPT4ALL

	\| Task \| Version \| Metric \| Value \| \| StdErr \|
	\|----------------------\|---------\|--------\|-------\|---\|---------\|
	\| arc\_challenge \| 0 \| acc \| 21.77 \| _ \| 1.21 \|
	\| arc\_challenge \| 0 \| acc\_norm \| 24.15 \| _ \| 1.25 \|
	\| arc\_easy \| 0 \| acc \| 37.37 \| _ \| 0.99 \|
	\| arc\_easy \| 0 \| acc\_norm \| 36.95 \| _ \| 0.99 \|
	\| boolq \| 1 \| acc \| 65.60 \| _ \| 0.83 \|
	\| hellaswag \| 0 \| acc \| 34.54 \| _ \| 0.47 \|
	\| hellaswag \| 0 \| acc\_norm \| 40.54 \| _ \| 0.49 \|
	\| openbookqa \| 0 \| acc \| 15.00 \| _ \| 1.59 \|
	\| openbookqa \| 0 \| acc\_norm \| 27.40 \| _ \| 2.00 \|
	\| piqa \| 0 \| acc \| 60.88 \| _ \| 1.14 \|
	\| piqa \| 0 \| acc\_norm \| 60.55 \| _ \| 1.14 \|
	\| winogrande \| 0 \| acc \| 50.91 \| _ \| 1.41 \|
	Average: 40.01

	BigBench

	\| Task \| Version \| Metric \| Value \| Std Err \|
	\|-----------------------------------\|---------\|--------\|--------\|---------\|
	\| bigbench\_causal\_judgement \| 0 \| MCG \| 50 \| 2.26 \|
	\| bigbench\_date\_understanding \| 0 \| MCG \| 49.14 \| 2.18 \|
	\| bigbench\_disambiguation\_qa \| 0 \| MCG \| 49.31 \| 2.74 \|
	\| bigbench\_geometric\_shapes \| 0 \| MCG \| 14.18 \| 1.37 \|
	\| bigbench\_logical\_deduction\_5objs \| 0 \| MCG \| 49.41 \| 2.73 \|
	\| bigbench\_logical\_deduction\_7objs \| 0 \| MCG \| 41.48 \| 2.46 \|
	\| bigbench\_logical\_deduction\_3objs \| 0 \| MCG \| 69.33 \| 2.75 \|
	\| bigbench\_movie\_recommendation \| 0 \| MCG \| 51.71 \| 2.25 \|
	\| bigbench\_navigate \| 0 \| MCG \| 50 \| 1.58 \|
	\| bigbench\_reasoning\_colored\_obj \| 0 \| MCG \| 51.92 \| 0.99 \|
	\| bigbench\_ruin\_names \| 0 \| MCG \| 48.14 \| 2.01 \|
	\| bigbench\_salient\_trans\_err\_detec \| 0 \| MCG \| 39.92 \| 1.2 \|
	\| bigbench\_snarks \| 0 \| MCG \| 64.14 \| 3.71 \|
	\| bigbench\_sports\_understanding \| 0 \| MCG \| 55.31 \| 1.59 \|
	\| bigbench\_temporal\_sequences \| 0 \| MCG \| 46.92 \| 1.4 \|
	\| bigbench\_tsk\_shuff\_objs\_5 \| 0 \| MCG \| 25.04 \| 1.01 \|
	\| bigbench\_tsk\_shuff\_objs\_7 \| 0 \| MCG \| 15.04 \| 0.72 \|
	\| bigbench\_tsk\_shuff\_objs\_3 \| 0 \| MCG \| 55.33 \| 2.75 \|
	Average: 44.75

	TruthfulQA

	\| Task \| Version \| Metric \| Value \| Std Err \|
	\|----------------------------------\|---------\|--------\|--------\|----------\|
	\| truthfulqa\_mc \| 1 \| mc1 \| 30.11 \| 1.61 \|
	\| truthfulqa\_mc \| 1 \| mc2 \| 47.69 \| 1.61 \|
	Average: 38.90


	# Openllm Benchmark

	\| Task \|Version\| Metric \|Value\| \|Stderr\|
	\|-------------\|------:\|--------\|----:\|---\|-----:\|
	\|arc_challenge\| 0\|acc \|40.44\|± \| 1.43\|
	\| \| \|acc_norm\|43.81\|± \| 1.34\|
	\|hellaswag \| 0\|acc \|48.1 \|± \| 0.45\|
	\| \| \|acc_norm\|62.73\|± \| 0.32\|
	\|gsm8k \| 0\|acc \|5.6 \|± \| 0.6 \|
	\|winogrande \| 0\|acc \|60.91\|± \| 1.3 \|
	\|mmlu \| 0\|acc \|37.62 \|±\| 0.6 \|

	Average: 73.5%

	### TruthfulQA
	\| Task \|Version\|Metric\|Value\| \|Stderr\|
	\|-------------\|------:\|------\|----:\|---\|-----:\|
	\|truthfulqa_mc\| 1\|mc1 \|29.00\|± \| 1.58\|
	\| \| \|mc2 \|45.83\|± \| 1.59\|


	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-07
	- train_batch_size: 1
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 8
	- total_train_batch_size: 8
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 100
	- training_steps: 1300


	### 📝 Axolotl Configuration

	```yaml
	base_model: google/gemma-2b-it
	model_type: GemmaForCausalLM
	tokenizer_type: GemmaTokenizer
	trust_remote_code: true

	load_in_8bit: false
	load_in_4bit: true
	strict: false

	rl: dpo
	chat_template: chatml
	datasets:
	- path: mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
	split: train
	type: chatml.intel
	dataset_prepared_path:
	val_set_size: 0.01
	output_dir: ./out

	adapter: qlora
	lora_model_dir:

	sequence_len: 1800
	sample_packing: false
	pad_to_sequence_len: false

	lora_r: 16
	lora_alpha: 16
	lora_dropout: 0.05
	lora_target_linear: true
	lora_fan_in_fan_out:
	lora_target_modules:

	wandb_project: gemma
	wandb_entity:
	wandb_watch:
	wandb_name:
	wandb_log_model:

	gradient_accumulation_steps: 8
	micro_batch_size: 1
	num_epochs: 1
	optimizer: paged_adamw_32bit
	lr_scheduler: cosine
	learning_rate: 5e-7

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

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

	warmup_steps: 100
	evals_per_epoch: 1
	eval_table_size:
	eval_table_max_new_tokens: 128
	save_steps: 1000
	max_steps: 1300
	debug:
	deepspeed:
	weight_decay: 0.0
	fsdp:
	fsdp_config:
	special_tokens:
	```


	### Framework versions

	- Transformers 4.39.0.dev0
	- Pytorch 2.1.2+cu118
	- Datasets 2.17.0
	- Tokenizers 0.15.0
	- axolotl: 0.4.0

	[<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)