Instructions to use ArchiveAI/AlphaMonarch-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArchiveAI/AlphaMonarch-7B with Transformers:

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

pipe = pipeline("text-generation", model="ArchiveAI/AlphaMonarch-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArchiveAI/AlphaMonarch-7B")
model = AutoModelForCausalLM.from_pretrained("ArchiveAI/AlphaMonarch-7B", 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 ArchiveAI/AlphaMonarch-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ArchiveAI/AlphaMonarch-7B

SGLang

How to use ArchiveAI/AlphaMonarch-7B 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 "ArchiveAI/AlphaMonarch-7B" \
    --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": "ArchiveAI/AlphaMonarch-7B",
		"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 "ArchiveAI/AlphaMonarch-7B" \
        --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": "ArchiveAI/AlphaMonarch-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArchiveAI/AlphaMonarch-7B with Docker Model Runner:
```
docker model run hf.co/ArchiveAI/AlphaMonarch-7B
```

AlphaMonarch-7B / README.md

ArchiveAI

Duplicate from mlabonne/AlphaMonarch-7B

3381abb verified over 2 years ago

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	---
	license: cc-by-nc-4.0
	tags:
	- merge
	- lazymergekit
	- dpo
	- rlhf
	dataset:
	- mlabonne/truthy-dpo-v0.1
	- mlabonne/distilabel-intel-orca-dpo-pairs
	- mlabonne/chatml-OpenHermes2.5-dpo-binarized-alpha
	base_model:
	- mlabonne/NeuralMonarch-7B
	language:
	- en
	---

	![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/TI7C8F2gk43gmI9U2L0uk.jpeg)

	# 👑 AlphaMonarch-7B

	tl;dr: AlphaMonarch-7B is a new DPO merge that retains all the reasoning abilities of the very best merges and significantly improves its conversational abilities. Kind of the best of both worlds in a 7B model. 🎉

	AlphaMonarch-7B is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset.

	It is based on a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
	* [mlabonne/OmniTruthyBeagle-7B-v0](https://huggingface.co/mlabonne/OmniTruthyBeagle-7B-v0)
	* [mlabonne/NeuBeagle-7B](https://huggingface.co/mlabonne/NeuBeagle-7B)
	* [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B)

	Special thanks to [Jon Durbin](https://huggingface.co/jondurbin), [Intel](https://huggingface.co/Intel), [Argilla](https://huggingface.co/argilla), and [Teknium](https://huggingface.co/teknium) for the preference datasets.

	Try the demo: https://huggingface.co/spaces/mlabonne/AlphaMonarch-7B-GGUF-Chat

	## 🔍 Applications

	This model uses a context window of 8k. I recommend using it with the Mistral Instruct chat template (works perfectly with LM Studio).

	It is one of the very best 7B models in terms of instructing following and reasoning abilities and can be used for conversations, RP, and storytelling. Note that it tends to have a quite formal and sophisticated style, but it can be changed by modifying the prompt.

	## ⚡ Quantized models

	* GGUF: https://huggingface.co/mlabonne/AlphaMonarch-7B-GGUF
	* GPTQ: https://huggingface.co/LoneStriker/AlphaMonarch-7B-GPTQ
	* AWQ: https://huggingface.co/LoneStriker/AlphaMonarch-7B-AWQ
	* mlx: https://huggingface.co/mlx-community/AlphaMonarch-7B-mlx
	* EXL2:
	* https://huggingface.co/LoneStriker/AlphaMonarch-7B-3.0bpw-h6-exl2
	* https://huggingface.co/LoneStriker/AlphaMonarch-7B-4.0bpw-h6-exl2
	* https://huggingface.co/LoneStriker/AlphaMonarch-7B-5.0bpw-h6-exl2
	* https://huggingface.co/LoneStriker/AlphaMonarch-7B-6.0bpw-h6-exl2
	* https://huggingface.co/LoneStriker/AlphaMonarch-7B-8.0bpw-h6-exl2

	## 🏆 Evaluation

	### Nous

	AlphaMonarch-7B is the best-performing 7B model on Nous' benchmark suite (evaluation performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval)). See the entire leaderboard [here](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard).

	\| Model \| Average \| AGIEval \| GPT4All \| TruthfulQA \| Bigbench \|
	\|---\|---:\|---:\|---:\|---:\|---:\|
	\| [AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B) [📄](https://gist.github.com/mlabonne/1d33c86824b3a11d2308e36db1ba41c1) \| 62.74 \| 45.37 \| 77.01 \| 78.39 \| 50.2 \|
	\| [NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B) [📄](https://gist.github.com/mlabonne/64050c96c6aa261a8f5b403190c8dee4) \| 62.73 \| 45.31 \| 76.99 \| 78.35 \| 50.28 \|
	\| [Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B) [📄](https://gist.github.com/mlabonne/0b8d057c5ece41e0290580a108c7a093) \| 62.68 \| 45.48 \| 77.07 \| 78.04 \| 50.14 \|
	\| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/88b21dd9698ffed75d6163ebdc2f6cc8) \| 52.42 \| 42.75 \| 72.99 \| 52.99 \| 40.94 \|
	\| [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B) [📄](https://gist.github.com/mlabonne/14687f1eb3425b166db511f31f8e66f6) \| 53.51 \| 43.67 \| 73.24 \| 55.37 \| 41.76 \|
	\| [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) [📄](https://gist.github.com/mlabonne/ad0c665bbe581c8420136c3b52b3c15c) \| 60.25 \| 46.06 \| 76.77 \| 70.32 \| 47.86 \|
	\| [mlabonne/NeuralOmniBeagle-7B](https://huggingface.co/mlabonne/NeuralOmniBeagle-7B) [📄](https://gist.github.com/mlabonne/0e49d591787185fa5ae92ca5d9d4a1fd) \| 62.3 \| 45.85 \| 77.26 \| 76.06 \| 50.03 \|
	\| [eren23/dpo-binarized-NeuralTrix-7B](https://huggingface.co/eren23/dpo-binarized-NeuralTrix-7B) [📄](https://gist.github.com/CultriX-Github/dbdde67ead233df0c7c56f1b091f728c) \| 62.5 \| 44.57 \| 76.34 \| 79.81 \| 49.27 \|
	\| [CultriX/NeuralTrix-7B-dpo](https://huggingface.co/CultriX/NeuralTrix-7B-dpo) [📄](https://gist.github.com/CultriX-Github/df0502599867d4043b45d9dafb5976e8) \| 62.5 \| 44.61 \| 76.33 \| 79.8 \| 49.24 \|

	### EQ-bench

	AlphaMonarch-7B is also outperforming 70B and 120B parameter models on [EQ-bench](https://eqbench.com/) by [Samuel J. Paech](https://twitter.com/sam_paech), who kindly ran the evaluations.

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dnCFxieqLiAC3Ll6CfdZW.png)

	### MT-Bench

	```
	########## First turn ##########
	score
	model turn
	gpt-4 1 8.95625
	OmniBeagle-7B 1 8.31250
	AlphaMonarch-7B 1 8.23750
	claude-v1 1 8.15000
	NeuralMonarch-7B 1 8.09375
	gpt-3.5-turbo 1 8.07500
	claude-instant-v1 1 7.80000

	########## Second turn ##########
	score
	model turn
	gpt-4 2 9.025000
	claude-instant-v1 2 8.012658
	OmniBeagle-7B 2 7.837500
	gpt-3.5-turbo 2 7.812500
	claude-v1 2 7.650000
	AlphaMonarch-7B 2 7.618750
	NeuralMonarch-7B 2 7.375000

	########## Average ##########
	score
	model
	gpt-4 8.990625
	OmniBeagle-7B 8.075000
	gpt-3.5-turbo 7.943750
	AlphaMonarch-7B 7.928125
	claude-instant-v1 7.905660
	claude-v1 7.900000
	NeuralMonarch-7B 7.734375
	NeuralBeagle14-7B 7.628125
	```

	### Open LLM Leaderboard

	AlphaMonarch-7B is one of the best-performing non-merge 7B models on the Open LLM Leaderboard:

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/njHxX_ERQaBssHqp17fMy.png)

	## 💻 Usage

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "mlabonne/AlphaMonarch-7B"
	messages = [{"role": "user", "content": "What is a large language model?"}]

	tokenizer = AutoTokenizer.from_pretrained(model)
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.float16,
	device_map="auto",
	)

	outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
	print(outputs[0]["generated_text"])
	```