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Model: dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-av Source: Original Platform
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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-verbalizer
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- qwen2.5
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---
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# Qwen2.5-1.5B-Instruct NLA L18 — AV (Activation Verbalizer)
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The AV half of a Natural Language Autoencoder trained on the layer-18 residual stream of
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`Qwen/Qwen2.5-1.5B-Instruct`. An activation is L2-normalised, scaled by
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`injection_scale`, and spliced into the embedding slot of the single injection token in a fixed
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prompt; the model then generates a free-text description of what the activation encodes.
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This repo mirrors the layout of
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[kitft/Llama-3.3-70B-NLA-L53-av](https://huggingface.co/kitft/Llama-3.3-70B-NLA-L53-av):
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full merged weights at the root (LoRA already folded in — load with plain
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`AutoModelForCausalLM`, no PEFT needed) and an `nla_meta.yaml` sidecar (schema v2) at the root.
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The AR half lives in the sibling repo
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[dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-ar](https://huggingface.co/dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-ar).
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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 greedy generations match exactly).
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Notes for loaders expecting the 70B layout:
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- Qwen2.5-1.5B ties embeddings (`tie_word_embeddings: true`), so there is no separate
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`lm_head.weight` key in the shards; `from_pretrained` re-ties automatically.
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- The AV prompt template in `nla_meta.yaml` uses the `{injection_char}` placeholder; the
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injection token is `<|image_pad|>` (id 151655), a registered special token that tokenizes
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atomically in context.
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- `mse_scale: 1.0` — AR targets in this pipeline are L2-normalised to unit norm, not
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`sqrt(d_model)`.
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## Usage
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```python
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import torch, torch.nn.functional as F, yaml
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from huggingface_hub import hf_hub_download
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "dormantx/Qwen2.5-1.5B-Instruct-NLA-L18-av"
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meta = yaml.safe_load(open(hf_hub_download(repo, "nla_meta.yaml")))
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tok = AutoTokenizer.from_pretrained(repo)
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av = AutoModelForCausalLM.from_pretrained(repo, torch_dtype=torch.bfloat16).cuda().eval()
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prompt = meta["prompt_templates"]["av"].format(injection_char=meta["tokens"]["injection_char"])
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ids = tok(prompt, add_special_tokens=False)["input_ids"]
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slot = ids.index(meta["tokens"]["injection_token_id"])
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emb = av.get_input_embeddings()(torch.tensor(ids).cuda()[None]).clone()
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act = ... # a raw layer-18 residual-stream activation, i.e. hidden_states[18], shape [1536]
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emb[:, slot] = F.normalize(act, dim=-1).to(emb.dtype) * meta["extraction"]["injection_scale"]
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out = av.generate(inputs_embeds=emb,
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attention_mask=torch.ones(emb.shape[:2], device=emb.device),
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max_new_tokens=32, do_sample=False)
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print(tok.decode(out[0], skip_special_tokens=True))
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```
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Activations must come from `hidden_states[18]` of the base model (output of block 18, before the
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final norm) and be passed raw — the injection step does the normalising and rescaling.
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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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"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": 28,
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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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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "5.13.0"
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}
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model-00001-of-00002.safetensors
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size 1991828104
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model.safetensors.index.json
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{
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|
||||||
|
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|
||||||
|
}
|
||||||
|
}
|
||||||
40
nla_meta.yaml
Normal file
40
nla_meta.yaml
Normal file
@@ -0,0 +1,40 @@
|
|||||||
|
kind: nla_model
|
||||||
|
schema_version: 2
|
||||||
|
d_model: 1536
|
||||||
|
tokens:
|
||||||
|
injection_char: <|image_pad|>
|
||||||
|
injection_token_id: 151655
|
||||||
|
injection_left_neighbor_id: 220
|
||||||
|
injection_right_neighbor_id: 151645
|
||||||
|
critic_suffix_ids: null
|
||||||
|
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
|
||||||
|
|
||||||
|
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: av
|
||||||
|
stage: sl
|
||||||
|
extraction:
|
||||||
|
injection_scale: 1.01256
|
||||||
|
mse_scale: 1.0
|
||||||
|
training:
|
||||||
|
lr: 1.0e-05
|
||||||
|
loss_type: sft_next_token
|
||||||
|
global_batch_size: 16
|
||||||
|
num_layers: 28
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
|
||||||
|
size 11421892
|
||||||
30
tokenizer_config.json
Normal file
30
tokenizer_config.json
Normal file
@@ -0,0 +1,30 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"is_local": false,
|
||||||
|
"local_files_only": false,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user