nla-qwen2.5-7b-L20-av API & Inference Endpoint

Quick Start

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained(    "Qwen/Qwen2.5-7B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(base, "Kameshr/nla-qwen2.5-7b-L20-av")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")model.eval()

Extract a layer-20 activation

python

def extract_layer20(text: str, model, tokenizer) -> torch.Tensor:    captured = {}    def hook(mod, inp, out):        captured["h"] = (out[0] if isinstance(out, tuple) else out).detach().float().cpu()        raise StopIteration    enc = tokenizer(text, return_tensors="pt").to(model.device)    handle = model.model.layers[20].register_forward_hook(hook)    try:        with torch.no_grad(): model(**enc)    except StopIteration:        pass    finally:        handle.remove()    return captured["h"][0, -1]  # last token, shape (3584,)

Verbalize it

python

INJECTION_CHAR = "㈀"  # U+3200 — single token in Qwen tokenizer @torch.no_grad()def verbalize(activation: torch.Tensor, model, tokenizer, max_new_tokens=80) -> str:    prompt = (        "You are a meticulous AI researcher investigating activation vectors from a language model. "        "Describe the semantic content of the vector enclosed in <concept> tags.\n\n"        f"<concept>{INJECTION_CHAR}</concept>\n\nPlease provide an explanation."    )    enc = tokenizer(        tokenizer.apply_chat_template(            [{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True        ),        return_tensors="pt",    ).to(model.device)     embeds = model.get_input_embeddings()(enc["input_ids"]).clone()    inj_pos = (enc["input_ids"][0] == tokenizer.encode(INJECTION_CHAR, add_special_tokens=False)[0]).nonzero()[0, 0]    embeds[0, inj_pos] = activation.to(embeds.dtype).to(embeds.device)     out = model.generate(        inputs_embeds=embeds,        attention_mask=enc["attention_mask"],        max_new_tokens=max_new_tokens,        do_sample=False,        pad_token_id=tokenizer.eos_token_id,    )    return tokenizer.decode(out[0], skip_special_tokens=True)

Example

python

text = "Photo caption: A golden retriever puppy sitting in a field of sunflowers,"act = extract_layer20(text, base, tokenizer)print(verbalize(act, model, tokenizer))# → "A happy puppy sitting in a field of flowers, dog with colorful flowers around it"

The AV reads the comma and plans ahead — the description includes content not yet in the input.

Generalization

Trained on layer 20, evaluated across all 28 layers (2000 texts, 5 domains):

Table with columns: Layer range, Cosine Similarity, Recall@10
Layer range	Cosine Similarity	Recall@10
L10–L14	0.52–0.57	0.40–0.55
L15–L19	0.60–0.67	0.59–0.65
L20 (train)	0.692	0.682
L21–L25	0.56–0.67	0.42–0.67
L27	0.215	0.005

28/28 layers statistically significant vs random baseline (Wilcoxon, BH-corrected). Norm scaling has no effect — the AV learned directional, not magnitude-based, representations.

Full experiment: github.com/kameshkanna/nla-train/experiments

Training

Table with columns: Stage, Details
Stage	Details
AR SFT	Truncated Qwen2.5-7B (layers 0–20), LoRA r=64, MSE loss, 1h
AV SFT	Full Qwen2.5-7B, LoRA r=32, CE loss on kitft-labeled descriptions, 1.5h
RL GRPO	TRL GRPOTrainer, reward = −MSE(AR(description), activation), 1250 steps, 7h
Compute	1× H100 80GB, ~$25 total

Citation

bibtex

@misc{fraser-taliente2026nla,  title={Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations},  author={Fraser-Taliente and Kantamneni and Ong et al.},  year={2026},  url={https://transformer-circuits.pub/2026/nla/index.html}}

Quick Start

python

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained(    "Qwen/Qwen2.5-7B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(base, "Kameshr/nla-qwen2.5-7b-L20-av")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")model.eval()

import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = AutoModelForCausalLM.from_pretrained(    "Qwen/Qwen2.5-7B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(base, "Kameshr/nla-qwen2.5-7b-L20-av")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")model.eval()

Extract a layer-20 activation

python

def extract_layer20(text: str, model, tokenizer) -> torch.Tensor:    captured = {}    def hook(mod, inp, out):        captured["h"] = (out[0] if isinstance(out, tuple) else out).detach().float().cpu()        raise StopIteration    enc = tokenizer(text, return_tensors="pt").to(model.device)    handle = model.model.layers[20].register_forward_hook(hook)    try:        with torch.no_grad(): model(**enc)    except StopIteration:        pass    finally:        handle.remove()    return captured["h"][0, -1]  # last token, shape (3584,)

def extract_layer20(text: str, model, tokenizer) -> torch.Tensor:    captured = {}    def hook(mod, inp, out):        captured["h"] = (out[0] if isinstance(out, tuple) else out).detach().float().cpu()        raise StopIteration    enc = tokenizer(text, return_tensors="pt").to(model.device)    handle = model.model.layers[20].register_forward_hook(hook)    try:        with torch.no_grad(): model(**enc)    except StopIteration:        pass    finally:        handle.remove()    return captured["h"][0, -1]  # last token, shape (3584,)

Verbalize it

python

INJECTION_CHAR = "㈀"  # U+3200 — single token in Qwen tokenizer @torch.no_grad()def verbalize(activation: torch.Tensor, model, tokenizer, max_new_tokens=80) -> str:    prompt = (        "You are a meticulous AI researcher investigating activation vectors from a language model. "        "Describe the semantic content of the vector enclosed in <concept> tags.\n\n"        f"<concept>{INJECTION_CHAR}</concept>\n\nPlease provide an explanation."    )    enc = tokenizer(        tokenizer.apply_chat_template(            [{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True        ),        return_tensors="pt",    ).to(model.device)     embeds = model.get_input_embeddings()(enc["input_ids"]).clone()    inj_pos = (enc["input_ids"][0] == tokenizer.encode(INJECTION_CHAR, add_special_tokens=False)[0]).nonzero()[0, 0]    embeds[0, inj_pos] = activation.to(embeds.dtype).to(embeds.device)     out = model.generate(        inputs_embeds=embeds,        attention_mask=enc["attention_mask"],        max_new_tokens=max_new_tokens,        do_sample=False,        pad_token_id=tokenizer.eos_token_id,    )    return tokenizer.decode(out[0], skip_special_tokens=True)

INJECTION_CHAR = "㈀"  # U+3200 — single token in Qwen tokenizer @torch.no_grad()def verbalize(activation: torch.Tensor, model, tokenizer, max_new_tokens=80) -> str:    prompt = (        "You are a meticulous AI researcher investigating activation vectors from a language model. "        "Describe the semantic content of the vector enclosed in <concept> tags.\n\n"        f"<concept>{INJECTION_CHAR}</concept>\n\nPlease provide an explanation."    )    enc = tokenizer(        tokenizer.apply_chat_template(            [{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True        ),        return_tensors="pt",    ).to(model.device)     embeds = model.get_input_embeddings()(enc["input_ids"]).clone()    inj_pos = (enc["input_ids"][0] == tokenizer.encode(INJECTION_CHAR, add_special_tokens=False)[0]).nonzero()[0, 0]    embeds[0, inj_pos] = activation.to(embeds.dtype).to(embeds.device)     out = model.generate(        inputs_embeds=embeds,        attention_mask=enc["attention_mask"],        max_new_tokens=max_new_tokens,        do_sample=False,        pad_token_id=tokenizer.eos_token_id,    )    return tokenizer.decode(out[0], skip_special_tokens=True)

Example

python

text = "Photo caption: A golden retriever puppy sitting in a field of sunflowers,"act = extract_layer20(text, base, tokenizer)print(verbalize(act, model, tokenizer))# → "A happy puppy sitting in a field of flowers, dog with colorful flowers around it"

text = "Photo caption: A golden retriever puppy sitting in a field of sunflowers,"act = extract_layer20(text, base, tokenizer)print(verbalize(act, model, tokenizer))# → "A happy puppy sitting in a field of flowers, dog with colorful flowers around it"

The AV reads the comma and plans ahead — the description includes content not yet in the input.

Generalization

Trained on layer 20, evaluated across all 28 layers (2000 texts, 5 domains):

Table with columns: Layer range, Cosine Similarity, Recall@10
Layer range	Cosine Similarity	Recall@10
L10–L14	0.52–0.57	0.40–0.55
L15–L19	0.60–0.67	0.59–0.65
L20 (train)	0.692	0.682
L21–L25	0.56–0.67	0.42–0.67
L27	0.215	0.005

28/28 layers statistically significant vs random baseline (Wilcoxon, BH-corrected). Norm scaling has no effect — the AV learned directional, not magnitude-based, representations.

Full experiment: github.com/kameshkanna/nla-train/experiments

Training

Table with columns: Stage, Details
Stage	Details
AR SFT	Truncated Qwen2.5-7B (layers 0–20), LoRA r=64, MSE loss, 1h
AV SFT	Full Qwen2.5-7B, LoRA r=32, CE loss on kitft-labeled descriptions, 1.5h
RL GRPO	TRL GRPOTrainer, reward = −MSE(AR(description), activation), 1250 steps, 7h
Compute	1× H100 80GB, ~$25 total

Citation

bibtex

@misc{fraser-taliente2026nla,  title={Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations},  author={Fraser-Taliente and Kantamneni and Ong et al.},  year={2026},  url={https://transformer-circuits.pub/2026/nla/index.html}}

@misc{fraser-taliente2026nla,  title={Natural Language Autoencoders Produce Unsupervised Explanations of LLM Activations},  author={Fraser-Taliente and Kantamneni and Ong et al.},  year={2026},  url={https://transformer-circuits.pub/2026/nla/index.html}}

nla-qwen2.5-7b-L20-av

README

Quick Start

Extract a layer-20 activation

Verbalize it

Example

Generalization

Training

Citation

Explore FriendliAI today

README

Quick Start

Extract a layer-20 activation

Verbalize it

Example

Generalization

Training

Citation