Instructions to use nbeerbower/Qwen3-14B-abliterated-TIES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nbeerbower/Qwen3-14B-abliterated-TIES with Transformers:

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

pipe = pipeline("text-generation", model="nbeerbower/Qwen3-14B-abliterated-TIES")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nbeerbower/Qwen3-14B-abliterated-TIES")
model = AutoModelForCausalLM.from_pretrained("nbeerbower/Qwen3-14B-abliterated-TIES", 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 nbeerbower/Qwen3-14B-abliterated-TIES with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/Qwen3-14B-abliterated-TIES"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nbeerbower/Qwen3-14B-abliterated-TIES",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nbeerbower/Qwen3-14B-abliterated-TIES

SGLang

How to use nbeerbower/Qwen3-14B-abliterated-TIES 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 "nbeerbower/Qwen3-14B-abliterated-TIES" \
    --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": "nbeerbower/Qwen3-14B-abliterated-TIES",
		"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 "nbeerbower/Qwen3-14B-abliterated-TIES" \
        --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": "nbeerbower/Qwen3-14B-abliterated-TIES",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nbeerbower/Qwen3-14B-abliterated-TIES with Docker Model Runner:
```
docker model run hf.co/nbeerbower/Qwen3-14B-abliterated-TIES
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-14B-abliterated-TIES

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the TIES merge method using Qwen/Qwen3-14B-Base as a base.

Models Merged

The following models were included in the merge:

huihui-ai/Qwen3-14B-abliterated

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: huihui-ai/Qwen3-14B-abliterated
    parameters:
      weight: 1
      density: 1
merge_method: ties
base_model: Qwen/Qwen3-14B-Base
parameters:
  weight: 1
  density: 1
  normalize: true
  int8_mask: true
dtype: bfloat16

Reasoning Fix

The abliteration and merge caused an issue where the <think> token would not always be properly selected. This was fixed by using the vector from Qwen/Qwen3-14B.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# paths
src = "Qwen/Qwen3-14B"
tgt = "TARGET_MODEL"
out = "OUTPUT_DIR"

tok_tag = "<think>"

# load
src_tok = AutoTokenizer.from_pretrained(src)
tgt_tok = AutoTokenizer.from_pretrained(tgt)
src_model = AutoModelForCausalLM.from_pretrained(src, torch_dtype="auto", device_map="cpu")
tgt_model = AutoModelForCausalLM.from_pretrained(tgt, torch_dtype="auto", device_map="cpu")

# ids (don’t hard-code, trust the tokenizer)
sid = src_tok.convert_tokens_to_ids(tok_tag)
tid = tgt_tok.convert_tokens_to_ids(tok_tag)

if tid == src_tok.unk_token_id:
    # tgt lost the token – add it back, resize, grab new id
    tgt_tok.add_tokens([tok_tag])
    tid = tgt_tok.convert_tokens_to_ids(tok_tag)
    tgt_model.resize_token_embeddings(len(tgt_tok))

# copy the vec
with torch.no_grad():
    tgt_model.get_input_embeddings().weight[tid].copy_(
        src_model.get_input_embeddings().weight[sid]
    )

# optional blend instead of overwrite
# tgt_vec = tgt_model.get_input_embeddings().weight[tid]
# tgt_model.get_input_embeddings().weight[tid].copy_(0.7*src_vec + 0.3*tgt_vec)

# save
tgt_model.save_pretrained(out)
tgt_tok.save_pretrained(out)