Instructions to use MaxChess/qwen2.5-7b-nla-affect-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaxChess/qwen2.5-7b-nla-affect-ar with Transformers:

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

pipe = pipeline("text-generation", model="MaxChess/qwen2.5-7b-nla-affect-ar")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaxChess/qwen2.5-7b-nla-affect-ar")
model = AutoModelForCausalLM.from_pretrained("MaxChess/qwen2.5-7b-nla-affect-ar", 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 MaxChess/qwen2.5-7b-nla-affect-ar with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaxChess/qwen2.5-7b-nla-affect-ar"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaxChess/qwen2.5-7b-nla-affect-ar",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaxChess/qwen2.5-7b-nla-affect-ar

SGLang

How to use MaxChess/qwen2.5-7b-nla-affect-ar 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 "MaxChess/qwen2.5-7b-nla-affect-ar" \
    --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": "MaxChess/qwen2.5-7b-nla-affect-ar",
		"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 "MaxChess/qwen2.5-7b-nla-affect-ar" \
        --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": "MaxChess/qwen2.5-7b-nla-affect-ar",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaxChess/qwen2.5-7b-nla-affect-ar with Docker Model Runner:
```
docker model run hf.co/MaxChess/qwen2.5-7b-nla-affect-ar
```

qwen2.5-7b-nla-affect-ar / README.md

MaxChess

Initialize private AR card

045ef73 verified about 1 month ago

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	---
	base_model: Qwen/Qwen2.5-7B-Instruct
	datasets:
	- MaxChess/nla-affect-10k
	library_name: transformers
	license: apache-2.0
	tags:
	- natural-language-autoencoder
	- activation-reconstructor
	- interpretability
	- affect
	---

	# Qwen2.5-7B NLA Affect AR

	Experimental activation reconstructor (AR): explanation text to a 3,584-dimensional
	Qwen2.5-7B-Instruct layer-20 activation direction. The backbone is truncated to 21
	transformer blocks and uses a separately saved `value_head.safetensors`.

	## Evaluation on 200 AR-held-out teacher explanations

	- Directional FVE: 0.34277.
	- Bootstrap 95% CI: [0.30129, 0.37560].
	- Mean cosine similarity: 0.76460.
	- Shuffled FVE: -0.77740.

	Targets and predictions are independently L2-normalized. FVE is
	`1 - mean(\|\|h-h_hat\|\|^2) / mean(\|\|h-mean(h)\|\|^2)` on normalized directions.

	## Training

	- 5,000 examples, one epoch, 209 optimizer steps.
	- Direction-only normalized MSE, scale `sqrt(3584)`.
	- Effective global batch: 24 on three V100-32GB GPUs.
	- PyTorch SDPA.

	This AR score measures reconstruction from teacher explanations. The current full
	AV -> AR cycle is substantially weaker (FVE 0.03765), indicating that AV is the main
	bottleneck. See `MaxChess/nla-affect-10k` for exact splits and source code.