Instructions to use gghfez/WizardLM-2-22b-RP-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gghfez/WizardLM-2-22b-RP-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="gghfez/WizardLM-2-22b-RP-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gghfez/WizardLM-2-22b-RP-AWQ")
model = AutoModelForCausalLM.from_pretrained("gghfez/WizardLM-2-22b-RP-AWQ")
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 gghfez/WizardLM-2-22b-RP-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gghfez/WizardLM-2-22b-RP-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": "gghfez/WizardLM-2-22b-RP-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gghfez/WizardLM-2-22b-RP-AWQ

SGLang

How to use gghfez/WizardLM-2-22b-RP-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 "gghfez/WizardLM-2-22b-RP-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": "gghfez/WizardLM-2-22b-RP-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 "gghfez/WizardLM-2-22b-RP-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": "gghfez/WizardLM-2-22b-RP-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gghfez/WizardLM-2-22b-RP-AWQ with Docker Model Runner:
```
docker model run hf.co/gghfez/WizardLM-2-22b-RP-AWQ
```

WizardLM-2-22b-RP-AWQ / README.md

gghfez

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- gghfez/WizardLM-2-22b-RP
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- chat
	- creative
	- writing
	- roleplay
	---

	AWQ Quant of [gghfez/WizardLM-2-22b-RP](gghfez/WizardLM-2-22b-RP)

	# gghfez/WizardLM-2-22b-RP

	<img src="https://files.catbox.moe/acl4ld.png" width="400"/>

	⚠️ IMPORTANT: Experimental Model - Not recommended for Production Use
	- This is an experimental model created through bespoke, unorthodox merging techniques
	- The safety alignment and guardrails from the original WizardLM2 model may be compromised
	- This model is intended for creative writing and roleplay purposes ONLY
	- Use at your own risk and with appropriate content filtering in place


	This model is an experimental derivative of WizardLM2-8x22B, created by extracting the individual experts from the original mixture-of-experts (MoE) model, renaming the mlp modules to match the Mistral architecture, and merging them into a single dense model using linear merging via mergekit.

	The resulting model initially produced gibberish, but after fine-tuning on synthetic data generated by the original WizardLM2-8x22B, it regained the ability to generate relatively coherent text. However, the model exhibits confusion about world knowledge and mixes up the names of well known people.

	Despite efforts to train the model on factual data, the confusion persisted, so instead I trained it for creative tasks.

	As a result, this model is not recommended for use as a general assistant or for tasks that require accurate real-world knowledge (don't bother running MMLU-Pro on it).

	It actually retrieves details out of context very accurately, but I still can't recommend it for anything other than creative tasks.

	## Prompt format
	Mistral-v1 + the system tags from Mistral-V7 :
	```
	[SYSTEM_PROMPT] {system}[SYSTEM_PROMPT] [INST] {prompt}[/INST]
	```
	NOTE: This model is based on WizardLM2-8x22B, which is a finetune of Mixtral-8x22B - not to be confused with the more recent Mistral-Small-22B model.
	As such, it uses the same vocabulary and tokenizer as Mixtral-v0.1 and inherites the Apache2.0 license.
	I expanded the vocab to include the system prompt and instruction tags before training (including embedding heads).

	## Examples:

	### Strength: Information Extraction from Context
	[example 1]

	### Weakness: Basic Factual Knowledge
	[example 2]