test
#30
by
levmckinney
- opened
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See the raw diff here.
- .dockerignore +0 -2
- .gitattributes +0 -1
- Dockerfile +0 -25
- README.md +4 -1
- __pycache__/app.cpython-310.pyc +0 -0
- app.py +19 -24
- lens/CarperAI/stable-vicuna-13b/config.json +0 -1
- lens/EleutherAI/gpt-neox-20b/config.json +0 -1
- lens/EleutherAI/pythia-1.4b-deduped-v0/config.json +0 -1
- lens/EleutherAI/pythia-1.4b-deduped/config.json +0 -1
- lens/EleutherAI/pythia-12b-deduped-v0/config.json +0 -1
- lens/EleutherAI/pythia-12b-deduped-v0/params.pt +0 -3
- lens/EleutherAI/pythia-12b-deduped/config.json +0 -1
- lens/EleutherAI/pythia-12b-deduped/params.pt +0 -3
- lens/EleutherAI/pythia-160m-deduped-v0/config.json +0 -1
- lens/EleutherAI/pythia-160m-deduped/config.json +0 -1
- lens/EleutherAI/pythia-1b-deduped-v0/config.json +0 -1
- lens/EleutherAI/pythia-1b-deduped-v0/params.pt +0 -3
- lens/EleutherAI/pythia-2.8b-deduped-v0/config.json +0 -1
- lens/EleutherAI/pythia-2.8b-deduped-v0/params.pt +0 -3
- lens/EleutherAI/pythia-2.8b-deduped/config.json +0 -1
- lens/EleutherAI/pythia-2.8b-deduped/params.pt +0 -3
- lens/EleutherAI/pythia-410m-deduped-v0/config.json +0 -1
- lens/EleutherAI/pythia-410m-deduped-v0/params.pt +0 -3
- lens/EleutherAI/pythia-410m-deduped/config.json +0 -1
- lens/EleutherAI/pythia-410m-deduped/params.pt +0 -3
- lens/EleutherAI/pythia-6.9b-deduped-v0/config.json +0 -1
- lens/EleutherAI/pythia-6.9b-deduped-v0/params.pt +0 -3
- lens/EleutherAI/pythia-6.9b-deduped/config.json +0 -1
- lens/EleutherAI/pythia-6.9b-deduped/params.pt +0 -3
- lens/EleutherAI/pythia-70m-deduped-v0/config.json +0 -1
- lens/EleutherAI/pythia-70m-deduped-v0/params.pt +0 -3
- lens/EleutherAI/pythia-70m-deduped/config.json +0 -1
- lens/EleutherAI/pythia-70m-deduped/params.pt +0 -3
- lens/facebook/llama-13b/config.json +0 -1
- lens/facebook/llama-13b/params.pt +0 -3
- lens/facebook/llama-30b/config.json +0 -1
- lens/facebook/llama-30b/params.pt +0 -3
- lens/facebook/llama-65b/config.json +0 -1
- lens/facebook/llama-65b/params.pt +0 -3
- lens/facebook/llama-7b/config.json +0 -1
- lens/facebook/llama-7b/params.pt +0 -3
- lens/facebook/opt-1.3b/config.json +0 -1
- lens/facebook/opt-1.3b/params.pt +0 -3
- lens/facebook/opt-125m/config.json +0 -1
- lens/facebook/opt-125m/params.pt +0 -3
- lens/facebook/opt-6.7b/config.json +0 -1
- lens/facebook/opt-6.7b/params.pt +0 -3
- lens/gpt-neox-20b/config.json +1 -0
- lens/{EleutherAI/gpt-neox-20b → gpt-neox-20b}/params.pt +2 -2
.dockerignore
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lens
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.git
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.gitattributes
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@@ -32,4 +32,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.pyc filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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Dockerfile
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FROM python:3.9
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WORKDIR /code
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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# Set up a new user named "user" with user ID 1000
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RUN useradd -m -u 1000 user
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# Switch to the "user" user
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USER user
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# Set home to the user's home directory
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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# Set the working directory to the user's home directory
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WORKDIR $HOME/app
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# Copy the current directory contents into the container at $HOME/app setting the owner to the user
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COPY --chown=user . $HOME/app
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CMD ["python", "app.py"]
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README.md
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@@ -3,7 +3,10 @@ title: Tuned Lens
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emoji: 🔎
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colorFrom: pink
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colorTo: blue
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sdk:
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pinned: false
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license: mit
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---
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emoji: 🔎
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colorFrom: pink
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colorTo: blue
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sdk: gradio
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python_version: 3.10.2
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sdk_version: 3.20.0
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app_file: app.py
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pinned: false
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license: mit
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---
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__pycache__/app.cpython-310.pyc
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Binary file (3.92 kB). View file
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app.py
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import torch
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from tuned_lens.nn.lenses import TunedLens, LogitLens
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from tuned_lens.plotting import
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import gradio as gr
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from plotly import graph_objects as go
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device = torch.device("cpu")
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print(f"Using device {device} for inference")
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model = AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped")
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped")
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tuned_lens = TunedLens.
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map_location=device,
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)
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logit_lens = LogitLens.from_model(model)
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lens_options_dict = {
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"Tuned Lens": tuned_lens,
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statistic_options_dict = {
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"Entropy": "entropy",
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"Cross Entropy": "
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"Forward KL": "forward_kl",
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}
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def make_plot(lens, text, statistic, token_cutoff):
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input_ids = tokenizer.encode(text)
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input_ids = [tokenizer.bos_token_id] + input_ids
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targets = input_ids[1:] + [tokenizer.eos_token_id]
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if len(input_ids) ==
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return go.Figure(layout=dict(title="Please enter some text."))
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if token_cutoff < 1:
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return go.Figure(layout=dict(title="Please provide valid token cut off."))
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-
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input_ids=input_ids,
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start_pos=start_pos,
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)
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return
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)
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preamble = """
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# The Tuned Lens 🔎
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demo.load(make_plot, [lens_options, text, statistic, token_cutoff], plot)
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if __name__ == "__main__":
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demo.launch(
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import torch
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from tuned_lens.nn.lenses import TunedLens, LogitLens
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from tuned_lens.plotting import plot_lens
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import gradio as gr
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from plotly import graph_objects as go
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device = torch.device("cpu")
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print(f"Using device {device} for inference")
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model = AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-410m-deduped-v0")
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model = model.to(device)
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/pythia-410m-deduped-v0")
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tuned_lens = TunedLens.load("pythia-410m-deduped-v0", map_location=device)
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logit_lens = LogitLens(model)
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lens_options_dict = {
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"Tuned Lens": tuned_lens,
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statistic_options_dict = {
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"Entropy": "entropy",
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"Cross Entropy": "ce",
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"Forward KL": "forward_kl",
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}
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def make_plot(lens, text, statistic, token_cutoff):
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input_ids = tokenizer.encode(text, return_tensors="pt")
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if len(input_ids[0]) == 0:
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return go.Figure(layout=dict(title="Please enter some text."))
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if token_cutoff < 1:
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return go.Figure(layout=dict(title="Please provide valid token cut off."))
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fig = plot_lens(
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model,
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tokenizer,
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lens_options_dict[lens],
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layer_stride=2,
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input_ids=input_ids,
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start_pos=max(len(input_ids[0]) - token_cutoff, 0),
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statistic=statistic_options_dict[statistic],
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)
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return fig
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preamble = """
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# The Tuned Lens 🔎
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demo.load(make_plot, [lens_options, text, statistic, token_cutoff], plot)
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if __name__ == "__main__":
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demo.launch()
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lens/CarperAI/stable-vicuna-13b/config.json
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{"base_model_name_or_path": "vicuna-13b", "d_model": 5120, "num_hidden_layers": 40, "bias": true, "base_model_revision": null, "unembed_hash": "270c781d8280754a30abaf6fc186b1d754cc5d6bb17173d572d5517d5c7c702c", "lens_type": "linear_tuned_lens"}
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lens/EleutherAI/gpt-neox-20b/config.json
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{"base_model_name_or_path": "EleutherAI/gpt-neox-20b", "d_model": 6144, "num_hidden_layers": 44, "bias": true, "base_model_revision": "4e49eadb5d14bd22f314ec3f45b69a87b88c7691", "lens_type": "linear_tuned_lens"}
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lens/EleutherAI/pythia-1.4b-deduped-v0/config.json
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{"base_model_name_or_path": "EleutherAI/pythia-1.4b-deduped-v0", "d_model": 2048, "num_hidden_layers": 24, "bias": true, "base_model_revision": "b541e01fddacd3038799915cf8ff5b52e835a6c4", "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-1.4b-deduped", "d_model": 2048, "num_hidden_layers": 24, "bias": true, "base_model_revision": null, "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-12b-deduped-v0", "d_model": 5120, "num_hidden_layers": 36, "bias": true, "base_model_revision": "b497662035bf3c80b4f6a1ddfe09bc27763e843a", "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-12b-deduped", "d_model": 5120, "num_hidden_layers": 36, "bias": true, "base_model_revision": null, "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-160m-deduped-v0", "d_model": 768, "num_hidden_layers": 12, "bias": true, "base_model_revision": "7e57cc978f5da949f028f36b5baf8f5d6c3281b1", "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-160m-deduped", "d_model": 768, "num_hidden_layers": 12, "bias": true, "base_model_revision": null, "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-1b-deduped-v0", "d_model": 2048, "num_hidden_layers": 16, "bias": true, "base_model_revision": "021f79f50ff000ae1c159e22402ffec62284664d", "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-2.8b-deduped-v0", "d_model": 2560, "num_hidden_layers": 32, "bias": true, "base_model_revision": null, "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-2.8b-deduped", "d_model": 2560, "num_hidden_layers": 32, "bias": true, "base_model_revision": null, "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-410m-deduped-v0", "d_model": 1024, "num_hidden_layers": 24, "bias": true, "base_model_revision": "3538d3569a7e313e445ad6401c92c6e16777a2da", "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-410m-deduped", "d_model": 1024, "num_hidden_layers": 24, "bias": true, "base_model_revision": null, "lens_type": "linear_tuned_lens"}
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{"base_model_name_or_path": "EleutherAI/pythia-6.9b-deduped-v0", "d_model": 4096, "num_hidden_layers": 32, "bias": true, "base_model_revision": "cbd53efc2e56056e3bd0235277b5d0b668a6dfbb", "lens_type": "linear_tuned_lens"}
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