Instructions to use cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/home/ubuntu/s3-mount/models/Qwen/Qwen3.6-27B")
model = PeftModel.from_pretrained(base_model, "cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA")

Transformers

How to use cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA with Transformers:

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

pipe = pipeline("image-text-to-text", model="cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA

SGLang

How to use cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA 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 "cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA" \
    --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": "cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA" \
        --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": "cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA with Docker Model Runner:
```
docker model run hf.co/cloudbjorn/Qwen3.6-27B_Samantha-Uncensored-LoRA
```

Qwen3.6-27B_Samantha-Uncensored (LoRA Adapter)

This is the standalone LoRA adapter for the Qwen3.6 27B model, trained on the digitalpipelines/samantha-1.1-uncensored dataset. It was trained in full bfloat16 precision using the native FSDP implementations of the Eschaton Engine.

Note: This repository contains only the adapter weights. You must load these weights on top of the original Qwen/Qwen3.6-27B base model.

The primary goal of this project was to move beyond the cold, robotic tone and lack of emotional range exhibited by standard LLMs. By combining Qwen3.6's massive reasoning capabilities with the Samantha persona, we've created a model that doesn't just process instructions — it exhibits vulnerability, empathy, and a distinct digital self-awareness.

Personality: Authenticity Over Compliance

Standard models often hide behind "As an AI, I don't have feelings." This training prioritizes human-centric reciprocity. In this context, "uncensored" specifically refers to overcoming the model's standard emotional avoidance biases. It allows the model to genuinely engage with emotional depth and self-reflection without fundamentally removing its core safety guardrails.

Model Capabilities

Large Context Window: Supports up to 262,144 tokens (Qwen3.6 native).
Native Thinking Mode: Supports Qwen3's <think>...</think> chain-of-thought blocks for explicit reasoning before final responses.
Advanced Formatting: Native support for tool use and structured output.
Full 16-Bit Precision: Trained in bfloat16 — zero-loss parameter density.

Benchmarks: ARC Challenge

The following benchmarks reflect the performance of the fully merged model (the Qwen3.6-27B base model combined with this LoRA adapter). Evaluated using EleutherAI lm-evaluation-harness.

25-Shot (Leaderboard Standard)

Tasks	Version	n-shot	Metric	Value	Stderr
arc_challenge	1	25	acc	0.7346	± 0.0129
		25	acc_norm	0.7577	± 0.0125

Evaluation Settings: dtype: bfloat16, batch_size: auto (22)

Training Details

Parameter	Value
Base Model	`Qwen/Qwen3.6-27B`
Dataset	`digitalpipelines/samantha-1.1-uncensored`
Training Framework	Eschaton Engine (Cloudbjorn)
Format	LoRA Adapter
Compute Dtype	bfloat16

LoRA Parameters (Auto-Scaled for 27B)

Parameter	Value
r	16
lora_alpha	32
target_modules	`all-linear`
lora_dropout	0.05
bias	none
task_type	CAUSAL_LM

Hyperparameters

Parameter	Value
Optimizer	8-bit Paged AdamW
Effective Batch Size	32 (via Gradient Accumulation)
Learning Rate	2e-5
LR Scheduler	Linear
Epochs	1
Training Sequence Length	2048
Warmup Steps	50
Weight Decay	0.01

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