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

Libraries

How to use solidrust/Genstruct-7B-AWQ with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Genstruct-7B-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Genstruct-7B-AWQ", 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 solidrust/Genstruct-7B-AWQ with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/solidrust/Genstruct-7B-AWQ

SGLang

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

Docker Model Runner
How to use solidrust/Genstruct-7B-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/Genstruct-7B-AWQ
```

Genstruct-7B-AWQ / README.md

Suparious

Updated base_model tag in README.md

b1b0fe9 verified almost 2 years ago

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	---
	base_model: NousResearch/Genstruct-7B
	tags:
	- Mistral
	- instruct
	- finetune
	- synthetic
	- quantized
	- 4-bit
	- AWQ
	- text-generation
	- autotrain_compatible
	- endpoints_compatible
	- chatml
	license: apache-2.0
	language:
	- en
	library_name: transformers
	model_creator: NousResearch
	model_name: Genstruct-7B
	model_type: mistral
	pipeline_tag: text-generation
	inference: false
	prompt_template: '<\|im_start\|>system

	{system_message}<\|im_end\|>

	<\|im_start\|>user

	{prompt}<\|im_end\|>

	<\|im_start\|>assistant

	'
	quantized_by: Suparious
	---
	# NousResearch/Genstruct-7B AWQ

	- Model creator: [NousResearch](https://huggingface.co/NousResearch)
	- Original model: [Genstruct-7B](https://huggingface.co/NousResearch/Genstruct-7B)

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/64137e2150358a805203cbac/ZhntfiUrRzRtB16nQb_1e.png)

	## Model Summary

	Genstruct 7B is an instruction-generation model, designed to create valid instructions given a raw text corpus. This enables the creation of new, partially synthetic instruction finetuning datasets from any raw-text corpus.

	This work was inspired by [Ada-Instruct](https://arxiv.org/abs/2310.04484)

	Previous methods largely rely on in-context approaches to generate instructions, while Ada-Instruct trained a custom instruction-generation model.

	Inspired by this, we took this approach further by grounding the generations in user-provided context passages.

	Further, the model is trained to generate questions involving complex scenarios that require detailed reasoning, allowing for models trained on the generated data to reason step-by-step.

	## How to use

	### Install the necessary packages

	```bash
	pip install --upgrade autoawq autoawq-kernels
	```

	### Example Python code

	```python
	from awq import AutoAWQForCausalLM
	from transformers import AutoTokenizer, TextStreamer

	model_path = "solidrust/Genstruct-7B-AWQ"
	system_message = "You are Genstruct, incarnated as a powerful AI."

	# Load model
	model = AutoAWQForCausalLM.from_quantized(model_path,
	fuse_layers=True)
	tokenizer = AutoTokenizer.from_pretrained(model_path,
	trust_remote_code=True)
	streamer = TextStreamer(tokenizer,
	skip_prompt=True,
	skip_special_tokens=True)

	# Convert prompt to tokens
	prompt_template = """\
	<\|im_start\|>system
	{system_message}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant"""

	prompt = "You're standing on the surface of the Earth. "\
	"You walk one mile south, one mile west and one mile north. "\
	"You end up exactly where you started. Where are you?"

	tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
	return_tensors='pt').input_ids.cuda()

	# Generate output
	generation_output = model.generate(tokens,
	streamer=streamer,
	max_new_tokens=512)

	```

	### About AWQ

	AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.

	AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.

	It is supported by:

	- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
	- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
	- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
	- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
	- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code

	## Prompt template: ChatML

	```plaintext
	<\|im_start\|>system
	{system_message}<\|im_end\|>
	<\|im_start\|>user
	{prompt}<\|im_end\|>
	<\|im_start\|>assistant
	```

	## How to cite

	```bibtext
	@misc{Genstruct,
	url={[https://https://huggingface.co/NousResearch/Genstruct-7B](https://huggingface.co/NousResearch/https://huggingface.co/NousResearch/Genstruct-7B)},
	title={Genstruct},
	author={"euclaise"}
	}
	```