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 / nla_meta.yaml

MaxChess

Upload latest AR SFT checkpoint

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	kind: nla_model
	schema_version: 2
	role: ar
	stage: sft
	base_checkpoint: /home/sparrow/NLAmodel/models/qwen2.5-7b-instruct
	d_model: 3584
	extraction:
	injection_scale: 150.0
	mse_scale: 59.86651818838306
	tokens:
	injection_char: "\u320E"
	injection_token_id: 149705
	injection_left_neighbor_id: 29
	injection_right_neighbor_id: 522
	critic_suffix_ids:
	- 1318
	- 29
	- 366
	- 1708
	- 29
	prompt_templates:
	av: 'You are a meticulous AI researcher conducting an important investigation into
	activation vectors from a language model. Your overall task is to describe the
	semantic content of that activation vector.


	We will pass the vector enclosed in <concept> tags into your context. You must
	then produce an explanation for the vector, enclosed within <explanation> tags.
	The explanation consists of 2-3 text snippets describing that vector.


	Here is the vector:


	<concept>{injection_char}</concept>


	Please provide an explanation.'
	ar: 'Summary of the following text: <text>{explanation}</text> <summary>'
	critic:
	extraction_layer_index: 20
	trained_on:
	- /home/sparrow/NLAmodel/experiments/Exp16_affect_av_dataset/runs/production10000_clean_sources/dataset/records.jsonl
	parent_checkpoints:
	- /home/sparrow/NLAmodel/models/qwen2.5-7b-instruct
	created_by: Exp18_affect_ar_sft.train_ar_sft
	training:
	optimizer_steps: 209
	learning_rate: 6.12e-06
	global_batch_size: 24
	num_epoch: 1
	loss_type: direction_only_normalized_mse
	mse_scale: 59.86651818838306
	dataset: /home/sparrow/NLAmodel/experiments/Exp16_affect_av_dataset/runs/production10000_clean_sources/dataset/records.jsonl