Instructions to use andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av with Transformers:

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

pipe = pipeline("text-generation", model="andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av")
model = AutoModelForCausalLM.from_pretrained("andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av", 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 andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av

SGLang

How to use andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av 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 "andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av" \
    --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": "andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av",
		"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 "andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av" \
        --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": "andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av with Docker Model Runner:
```
docker model run hf.co/andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av
```

Qwen2.5-1.5B-Instruct NLA L18 Activation Verbalizer

The activation verbalizer of a Natural Language Autoencoder trained on the layer-18 residual stream of Qwen/Qwen2.5-1.5B-Instruct. See https://transformer-circuits.pub/2026/nla/, and https://github.com/kitft/natural_language_autoencoders

Activation Reconstructor:dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-ar.

Usage

import torch, torch.nn.functional as F, yaml
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-av"
meta = yaml.safe_load(open(hf_hub_download(repo, "nla_meta.yaml")))
tok = AutoTokenizer.from_pretrained(repo)
av = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16).cuda().eval()

prompt = meta["prompt_templates"]["av"].format(injection_char=meta["tokens"]["injection_char"])
ids = tok(prompt, add_special_tokens=False)["input_ids"]
slot = ids.index(meta["tokens"]["injection_token_id"])
emb = av.get_input_embeddings()(torch.tensor(ids).cuda()[None]).clone()

act = ...  # a raw layer-18 residual-stream activation, i.e. hidden_states[18], shape [1536]
emb[:, slot] = F.normalize(act, dim=-1).to(emb.dtype) * meta["extraction"]["injection_scale"]
out = av.generate(inputs_embeds=emb,
                  attention_mask=torch.ones(emb.shape[:2], device=emb.device),
                  max_new_tokens=32, do_sample=False)
print(tok.decode(out[0], skip_special_tokens=True))

Activations must come from hidden_states[18] of the base model (output of block 18, before the final norm) and be passed raw — the injection step does the normalising and rescaling.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for andyx10/Qwen2.5-1.5B-Instruct-NLA-L18-av

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Finetuned

(1863)

this model