Not-For-All-Audiences

Instructions to use jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5 with Transformers:

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

pipe = pipeline("text-generation", model="jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5")
model = AutoModelForMultimodalLM.from_pretrained("jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5")
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 jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5

SGLang

How to use jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5 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 "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5" \
    --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": "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5",
		"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 "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5" \
        --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": "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5 with Docker Model Runner:
```
docker model run hf.co/jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.5
```

Qwen2.5-7B-nerd-uncensored-v1.5 / README.md

jeffmeloy

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metadata

license: apache-2.0
base_model:
  - Qwen/Qwen2.5-7B
pipeline_tag: text-generation
tags:
  - not-for-all-audiences
language:
  - en
library_name: transformers

Model Description

Model created by analyzing and selecting the optimal layers from other Qwen2.5-7B models based on their dimensional utilization efficiency, measured by the Normalized Effective Rank (NER). Computed like:

Input: Weight matrix for each model layer
Compute singular values σᵢ where σᵢ ≥ 0 # σᵢ represents the importance of each dimension
Filter values above numerical threshold (>1e-12)
Sum all singular values: S = Σσᵢ # S acts as normalization factor
Create probability distribution: pᵢ = σᵢ/S # converts singular values to probabilities summing to 1
Compute Shannon entropy: H = -Σ(pᵢ * log₂(pᵢ)) # measures information content
Calculate maximum possible entropy: H_max = log₂(n)
Final NER score = H/H_max # normalizes score to [0,1] range
Results in value between 0 and 1 for each model layer

Creating Composite Model

Code here: https://huggingface.co/jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0/blob/main/ner_merge.py

Code functions:

Download selected models from Hugging Face Hub
Calculate Normalized Effective Rank (NER) for each layer within each model
Define model and layer name pairs that have highest NER for each layer based on their NER scores
Incrementally build a composite model using layer with highest NER from model pool
Save merge reports documenting layer sources
Copy config and tokenizer files from base model
Save the composite model with complete weights # model ready to use

Configfile:

base_model: "Qwen/Qwen2.5-7B"

fine_tuned_models: # uncomment the models you want to merge

#- "Qwen/Qwen2.5-7B"

#- "Qwen/Qwen2.5-7B-Instruct"

#- "EVA-UNIT-01/EVA-Qwen2.5-7B-v0.1"

#- "FourOhFour/Vapor_v2_7B"

#- "Goekdeniz-Guelmez/Josiefied-Qwen2.5-7B-Instruct-abliterated-v2"

#- "happzy2633/qwen2.5-7b-ins-v3"

#- "huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2"

#- "HumanLLMs/Humanish-Qwen2.5-7B-Instruct"

#- "Orion-zhen/Qwen2.5-7B-Instruct-Uncensored"

#- "Orion-zhen/Meissa-Qwen2.5-7B-Instruct"

#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v0.9"

#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.0"

#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.1"

#- "jeffmeloy/Qwen2.5-7B-nerd-uncensored-v1.2"

#- "AmberYifan/Qwen2.5-7B-dpo-2k"

#- "sethuiyer/Qwen2.5-7B-Anvita"

#- "rombodawg/Rombos-LLM-V2.5-Qwen-7b"

#- "Cran-May/T.E-8.1"

#- "beomi/Qwen2.5-7B-Instruct-kowiki-qa"

#- "Orion-zhen/Qwen2.5-7B-Gutenberg-KTO"

#- "fblgit/cybertron-v4-qw7B-MGS"

#- "nguyentd/FinancialAdvice-Qwen2.5-7B"

#- "WhiteRabbitNeo/WhiteRabbitNeo-2.5-Qwen-2.5-Coder-7B"

#- "edgerunner-ai/EdgeRunner-Command-Nested"

#- "katanemo/Arch-Function-7B"

#- "DeepGlint-AI/llava-mlcd-qwen2.5-7b"

#- "mergekit-community/mergekit-slerp-aflqaqy"

#- "mergekit-community/mergekit-ties-inxwsfo"

#- "Qwen/Qwen2.5-Coder-7B-Instruct"

#- "Qwen/Qwen2.5-Math-7B-Instruct"

#- "Qwen/Qwen2.5-Coder-7B"

#- "Qwen/Qwen2.5-Math-7B"

#- "thomas-yanxin/XinYuan-Qwen2.5-7B-0917"

#- "jbjeong91/Qwen2.5_7B_IST_StoryGen_vanilla"

#- "AmberYifan/Qwen2.5-7B-dpo-2k-hhrlhf"

#- "jbjeong91/Qwen2.5_7B_IST_StoryGen_test2"

models_dir: "./input_models/"

output_dir: "./merged_model/"

metric_dir: "./metrics/"