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Model: dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-ar
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+library_name: transformers
+tags:
+  - interpretability
+  - natural-language-autoencoder
+  - activation-reconstructor
+  - qwen2.5
+---
+
+# Qwen2.5-1.5B-Instruct NLA L18 — AR (Activation Reconstructor)
+
+The AR half of a Natural Language Autoencoder trained on the layer-18 residual stream of
+`Qwen/Qwen2.5-1.5B-Instruct`. A copy of the base model truncated to layers
+0…17 (`num_hidden_layers: 18`), plus a `Linear(1536, 1536)` value head, trained with MSE to
+reconstruct the L2-normalised layer-18 activation from the AV's description text.
+
+This repo mirrors the layout of
+[kitft/Llama-3.3-70B-NLA-L53-ar](https://huggingface.co/kitft/Llama-3.3-70B-NLA-L53-ar):
+full merged truncated weights at the root (LoRA already folded in, standard `model.*` keys),
+`value_head.safetensors` at the root, and an `nla_meta.yaml` sidecar (schema v2) at the root.
+The AV half lives in the sibling repo
+[dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-av](https://huggingface.co/dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-av).
+The original LoRA-adapter release (both halves in one repo, `av/` + `ar/` subdirs) is
+[dormantx/Qwen2.5-1.5B-Instruct-NLA-L18](https://huggingface.co/dormantx/Qwen2.5-1.5B-Instruct-NLA-L18);
+weights here are numerically identical to that release
+(merged-vs-adapter reconstruction cosine 0.999999).
+
+Notes for loaders expecting the 70B layout:
+
+- `value_head.safetensors` holds two tensors, `weight` `[1536, 1536]` and `bias` `[1536]`,
+  both float32 (the 70B AR head is a bias-free bf16 `weight` only). The head was trained
+  with a bias — drop it and reconstructions shift.
+- There is no AR wrapper template: `prompt_templates.ar` is `"{explanation}"`. The AR
+  consumes the raw explanation text, no prefix/suffix, so `critic_suffix_ids: null`.
+- Pooling: the reconstruction input is `hidden_states[18]` at the **last non-pad token**, fed to
+  the value head in float32. Targets were L2-normalised to **unit** norm (`mse_scale: 1.0`), so
+  compare reconstructions by direction (cosine), not magnitude.
+- Qwen2.5-1.5B ties embeddings, so there is no `lm_head.weight` key; the checkpoint also keeps
+  `model.norm.weight` (unused by the pre-norm extraction path, harmless to load).
+
+## Usage
+
+```python
+import torch, torch.nn.functional as F
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file
+from transformers import AutoModel, AutoTokenizer
+
+repo = "dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-ar"
+tok = AutoTokenizer.from_pretrained(repo)
+ar = AutoModel.from_pretrained(repo, torch_dtype=torch.bfloat16).cuda().eval()
+head = load_file(hf_hub_download(repo, "value_head.safetensors"))
+W, b = head["weight"].cuda(), head["bias"].cuda()
+
+if tok.pad_token is None:
+    tok.pad_token = tok.eos_token
+enc = tok(["the outcome of a football game"], return_tensors="pt", padding=True).to("cuda")
+with torch.no_grad():
+    h = ar(**enc, output_hidden_states=True).hidden_states[18]      # [B, T, 1536]
+    last = enc["attention_mask"].sum(1) - 1
+    pooled = h[torch.arange(h.shape[0]), last].to(torch.float32)    # last non-pad token
+    pred = pooled @ W.T.float() + b.float()                         # reconstructed direction
+    pred = F.normalize(pred, dim=-1)
+```
+
+## Provenance
+
+Pipeline and honesty-check methodology build on
+[kitft/natural_language_autoencoders](https://github.com/kitft/natural_language_autoencoders),
+[kameshkanna/nla-train](https://github.com/kameshkanna/nla-train), and
+[SolshineCode/nla-gemma-4-e2b](https://github.com/SolshineCode/nla-gemma-4-e2b).

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.13.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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nla_meta.yaml

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+kind: nla_model
+schema_version: 2
+d_model: 1536
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+  injection_token_id: 151655
+  injection_left_neighbor_id: 220
+  injection_right_neighbor_id: 151645
+  critic_suffix_ids: null
+prompt_templates:
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+
+    You interpret neural-network activations. Given one activation vector, name in a single sentence the
+    concept, entity, topic, or syntactic role it encodes. Be concrete; do not hedge or add preamble.<|im_end|>
+
+    <|im_start|>user
+
+    Activation: {injection_char}<|im_end|>
+
+    <|im_start|>assistant
+
+    '
+  ar: '{explanation}'
+created_by: nla (dormantx/NLA_Qwen2.5_1.5B pipeline)
+role_aliases:
+  verbalizer: actor
+  recon: critic
+layer: 18
+extraction_layer_index: 18
+base_model: Qwen/Qwen2.5-1.5B-Instruct
+role: ar
+stage: sl
+extraction:
+  injection_scale: null
+  mse_scale: 1.0
+critic:
+  extraction_layer_index: 18
+training:
+  lr: 5.0e-05
+  loss_type: mse_unit_norm
+  global_batch_size: 32
+  num_layers: 18
+value_head:
+  file: value_head.safetensors
+  dtype: float32
+  weight_shape:
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+  - 1536
+  bias: true

tokenizer.json

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+oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
+size 11421892

tokenizer_config.json

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+  "backend": "tokenizers",
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+  "eos_token": "<|im_end|>",
+  "errors": "replace",
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+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

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