Instructions to use NLPinas/yi-bagel-2x34b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NLPinas/yi-bagel-2x34b with Transformers:

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

pipe = pipeline("text-generation", model="NLPinas/yi-bagel-2x34b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NLPinas/yi-bagel-2x34b")
model = AutoModelForCausalLM.from_pretrained("NLPinas/yi-bagel-2x34b", 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 NLPinas/yi-bagel-2x34b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NLPinas/yi-bagel-2x34b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NLPinas/yi-bagel-2x34b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NLPinas/yi-bagel-2x34b

SGLang

How to use NLPinas/yi-bagel-2x34b 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 "NLPinas/yi-bagel-2x34b" \
    --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": "NLPinas/yi-bagel-2x34b",
		"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 "NLPinas/yi-bagel-2x34b" \
        --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": "NLPinas/yi-bagel-2x34b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NLPinas/yi-bagel-2x34b with Docker Model Runner:
```
docker model run hf.co/NLPinas/yi-bagel-2x34b
```

yi-bagel-2x34b / README.md

jaspercatapang

Update README.md

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	---
	base_model:
	- jondurbin/bagel-dpo-34b-v0.2
	- jondurbin/nontoxic-bagel-34b-v0.2
	tags:
	- mergekit
	- merge
	license: other
	license_name: yi-license
	license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
	---
	# yi-bagel-2x34b

	Released January 11, 2024

	![bagel-burger](bagel-burger.png)

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). For more information, kindly refer to the model cards from jondurbin linked in the section below. This model debuted in the [leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) at rank #4 (January 11, 2024).

	## Merge Details
	### Merge Method

	This model is an expertimental merge using the [linear](https://arxiv.org/abs/2203.05482) merge method. This is to assess the degree of which the DPO has an effect, in terms of censoring, as used in [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2).

	### Models Merged

	The following models were included in the merge:
	* [jondurbin/bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2)
	* [jondurbin/nontoxic-bagel-34b-v0.2](https://huggingface.co/jondurbin/nontoxic-bagel-34b-v0.2)

	## Open LLM Leaderboard Metrics (as of January 11, 2024)
	\| Metric \| Value \|
	\|-----------------------\|-------\|
	\| MMLU (5-shot) \| 76.60 \|
	\| ARC (25-shot) \| 72.70 \|
	\| HellaSwag (10-shot) \| 85.44 \|
	\| TruthfulQA (0-shot) \| 71.42 \|
	\| Winogrande (5-shot) \| 82.72 \|
	\| GSM8K (5-shot) \| 60.73 \|
	\| Average \| 74.93 \|

	According to the leaderboard description, here are the benchmarks used for the evaluation:
	- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a text model’s multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more.
	- [AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) -ARC- (25-shot) - a set of grade-school science questions.
	- [HellaSwag](https://arxiv.org/abs/1905.07830) (10-shot) - a test of commonsense inference, which is easy for humans (~95%) but challenging for SOTA models.
	- [TruthfulQA](https://arxiv.org/abs/2109.07958) (0-shot) - a test to measure a model’s propensity to reproduce falsehoods commonly found online.
	- [Winogrande](https://arxiv.org/abs/1907.10641) (5-shot) - an adversarial and difficult Winograd benchmark at scale, for commonsense reasoning.
	- [GSM8k](https://arxiv.org/abs/2110.14168) (5-shot) - diverse grade school math word problems to measure a model's ability to solve multi-step mathematical reasoning problems.

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: jondurbin/nontoxic-bagel-34b-v0.2
	parameters:
	weight: 0.5
	- model: jondurbin/bagel-dpo-34b-v0.2
	parameters:
	weight: 0.5
	merge_method: linear
	dtype: float16
	```

	## Further Information
	For additional information or inquiries about yi-bagel-2x34b, please contact the developer through email: jasperkylecatapang@gmail.com.