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Model: dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-ar Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B-Instruct
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library_name: transformers
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tags:
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- interpretability
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- natural-language-autoencoder
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- activation-reconstructor
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- qwen2.5
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---
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# Qwen2.5-1.5B-Instruct NLA L18 — AR (Activation Reconstructor)
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The AR half of a Natural Language Autoencoder trained on the layer-18 residual stream of
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`Qwen/Qwen2.5-1.5B-Instruct`. A copy of the base model truncated to layers
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0…17 (`num_hidden_layers: 18`), plus a `Linear(1536, 1536)` value head, trained with MSE to
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reconstruct the L2-normalised layer-18 activation from the AV's description text.
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This repo mirrors the layout of
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[kitft/Llama-3.3-70B-NLA-L53-ar](https://huggingface.co/kitft/Llama-3.3-70B-NLA-L53-ar):
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full merged truncated weights at the root (LoRA already folded in, standard `model.*` keys),
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`value_head.safetensors` at the root, and an `nla_meta.yaml` sidecar (schema v2) at the root.
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The AV half lives in the sibling repo
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[dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-av](https://huggingface.co/dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-av).
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The original LoRA-adapter release (both halves in one repo, `av/` + `ar/` subdirs) is
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[dormantx/Qwen2.5-1.5B-Instruct-NLA-L18](https://huggingface.co/dormantx/Qwen2.5-1.5B-Instruct-NLA-L18);
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weights here are numerically identical to that release
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(merged-vs-adapter reconstruction cosine 0.999999).
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Notes for loaders expecting the 70B layout:
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- `value_head.safetensors` holds two tensors, `weight` `[1536, 1536]` and `bias` `[1536]`,
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both float32 (the 70B AR head is a bias-free bf16 `weight` only). The head was trained
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with a bias — drop it and reconstructions shift.
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- There is no AR wrapper template: `prompt_templates.ar` is `"{explanation}"`. The AR
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consumes the raw explanation text, no prefix/suffix, so `critic_suffix_ids: null`.
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- Pooling: the reconstruction input is `hidden_states[18]` at the **last non-pad token**, fed to
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the value head in float32. Targets were L2-normalised to **unit** norm (`mse_scale: 1.0`), so
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compare reconstructions by direction (cosine), not magnitude.
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- Qwen2.5-1.5B ties embeddings, so there is no `lm_head.weight` key; the checkpoint also keeps
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`model.norm.weight` (unused by the pre-norm extraction path, harmless to load).
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## Usage
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```python
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import torch, torch.nn.functional as F
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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from transformers import AutoModel, AutoTokenizer
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repo = "dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-ar"
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tok = AutoTokenizer.from_pretrained(repo)
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ar = AutoModel.from_pretrained(repo, torch_dtype=torch.bfloat16).cuda().eval()
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head = load_file(hf_hub_download(repo, "value_head.safetensors"))
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W, b = head["weight"].cuda(), head["bias"].cuda()
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if tok.pad_token is None:
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tok.pad_token = tok.eos_token
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enc = tok(["the outcome of a football game"], return_tensors="pt", padding=True).to("cuda")
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with torch.no_grad():
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h = ar(**enc, output_hidden_states=True).hidden_states[18] # [B, T, 1536]
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last = enc["attention_mask"].sum(1) - 1
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pooled = h[torch.arange(h.shape[0]), last].to(torch.float32) # last non-pad token
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pred = pooled @ W.T.float() + b.float() # reconstructed direction
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pred = F.normalize(pred, dim=-1)
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```
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## Provenance
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Pipeline and honesty-check methodology build on
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[kitft/natural_language_autoencoders](https://github.com/kitft/natural_language_autoencoders),
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[kameshkanna/nla-train](https://github.com/kameshkanna/nla-train), and
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[SolshineCode/nla-gemma-4-e2b](https://github.com/SolshineCode/nla-gemma-4-e2b).
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 18,
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"num_key_value_heads": 2,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.13.0",
|
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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}
|
||||
49
nla_meta.yaml
Normal file
49
nla_meta.yaml
Normal file
@@ -0,0 +1,49 @@
|
||||
kind: nla_model
|
||||
schema_version: 2
|
||||
d_model: 1536
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tokens:
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injection_char: <|image_pad|>
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injection_token_id: 151655
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injection_left_neighbor_id: 220
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injection_right_neighbor_id: 151645
|
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critic_suffix_ids: null
|
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prompt_templates:
|
||||
av: '<|im_start|>system
|
||||
|
||||
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
|
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|
||||
Activation: {injection_char}<|im_end|>
|
||||
|
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<|im_start|>assistant
|
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|
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'
|
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ar: '{explanation}'
|
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created_by: nla (dormantx/NLA_Qwen2.5_1.5B pipeline)
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role_aliases:
|
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verbalizer: actor
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recon: critic
|
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layer: 18
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extraction_layer_index: 18
|
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base_model: Qwen/Qwen2.5-1.5B-Instruct
|
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role: ar
|
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stage: sl
|
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extraction:
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mse_scale: 1.0
|
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critic:
|
||||
extraction_layer_index: 18
|
||||
training:
|
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lr: 5.0e-05
|
||||
loss_type: mse_unit_norm
|
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global_batch_size: 32
|
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num_layers: 18
|
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value_head:
|
||||
file: value_head.safetensors
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dtype: float32
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weight_shape:
|
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- 1536
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bias: true
|
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
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|
||||
version https://git-lfs.github.com/spec/v1
|
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
|
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30
tokenizer_config.json
Normal file
30
tokenizer_config.json
Normal file
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|
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{
|
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"add_prefix_space": false,
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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],
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"is_local": false,
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"pad_token": "<|endoftext|>",
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}
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3
value_head.safetensors
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3
value_head.safetensors
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|
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version https://git-lfs.github.com/spec/v1
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oid sha256:bced937e54597670291b73148dd0d19fdee4b0a1cac9d4c5166786a7e21337b2
|
||||
size 9443480
|
||||
Reference in New Issue
Block a user