Instructions to use BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored with Transformers:

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

pipe = pipeline("text-generation", model="BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored")
model = AutoModelForCausalLM.from_pretrained("BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored

SGLang

How to use BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored 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 "BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored" \
    --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": "BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored",
		"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 "BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored" \
        --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": "BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored with Docker Model Runner:
```
docker model run hf.co/BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🎯 Qwen2.5-Coder-7B-Instruct-Uncensored

An optimized version of Qwen2.5-Coder-7B-Instruct focused on removing refusal behaviors

📌 Overview

A streamlined implementation that removes refusal behaviors from Qwen2.5-Coder-7B-Instruct, based on directional intervention techniques.

🔍 Key Features

Implements directional intervention for behavior modification
Fully quantized for resource-efficient deployment

🛠️ Implementation

Based on research findings from "Refusal in LLMs is Mediated by a Single Direction"

💫 Quantization

Pre-quantized models available in GGFU format:

🤝 Contributors

Developed with ❤️ by BlossomAI

_{Star ⭐️ this repo if you find it valuable!}

Downloads last month: 902

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

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

Finetuned

(482)

this model

Quantizations

2 models

BlossomsAI
/

Qwen2.5-Coder-7B-Instruct-Uncensored

🎯 Qwen2.5-Coder-7B-Instruct-Uncensored

An optimized version of Qwen2.5-Coder-7B-Instruct focused on removing refusal behaviors

📌 Overview

🔍 Key Features

🛠️ Implementation

💫 Quantization

🤝 Contributors

Model tree for BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored

Space using BlossomsAI/Qwen2.5-Coder-7B-Instruct-Uncensored 1