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submit-system-update (#838)
Browse files- add login at submit and user info (0b31d4e1ffcd10bcdf9603d588d3b1d28c77fdc2)
- use gradio-leaderboard 0.0.11 (c8c479205c76ee4c961253c5909ffaa1eb6b03ed)
- update and revised submission info (8013f916ae1e9df83a51c72a1b736f51f10c8bc5)
- app.py +1 -0
- pyproject.toml +1 -1
- requirements.txt +1 -1
- src/display/about.py +26 -14
- src/submission/submit.py +22 -7
app.py
CHANGED
@@ -235,6 +235,7 @@ with main_block:
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with gr.Row():
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gr.Markdown("# βοΈβ¨ Submit your model here!", elem_classes="markdown-text")
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with gr.Row():
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with gr.Column():
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with gr.Row():
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gr.Markdown("# βοΈβ¨ Submit your model here!", elem_classes="markdown-text")
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login_button = gr.LoginButton(elem_id="oauth-button")
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with gr.Row():
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with gr.Column():
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pyproject.toml
CHANGED
@@ -45,7 +45,7 @@ tokenizers = ">=0.15.0"
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gradio-space-ci = {git = "https://huggingface.co/spaces/Wauplin/gradio-space-ci", rev = "0.2.3"}
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isort = "^5.13.2"
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ruff = "^0.3.5"
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gradio-leaderboard = "0.0.
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gradio = {extras = ["oauth"], version = "^4.36.1"}
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requests = "^2.31.0"
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requests-oauthlib = "^1.3.1"
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gradio-space-ci = {git = "https://huggingface.co/spaces/Wauplin/gradio-space-ci", rev = "0.2.3"}
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isort = "^5.13.2"
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ruff = "^0.3.5"
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+
gradio-leaderboard = "0.0.11"
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gradio = {extras = ["oauth"], version = "^4.36.1"}
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requests = "^2.31.0"
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requests-oauthlib = "^1.3.1"
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requirements.txt
CHANGED
@@ -17,7 +17,7 @@ isort
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ruff
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gradio==4.31.0
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gradio[oauth]
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gradio_leaderboard==0.0.
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requests==2.31.0
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requests-oauthlib== 1.3.1
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schedule == 1.2.2
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ruff
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gradio==4.31.0
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gradio[oauth]
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gradio_leaderboard==0.0.11
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requests==2.31.0
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requests-oauthlib== 1.3.1
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schedule == 1.2.2
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src/display/about.py
CHANGED
@@ -179,35 +179,47 @@ EVALUATION_QUEUE_TEXT = f"""
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Models added here will be automatically evaluated on the π€ cluster.
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-
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##
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### 1) Make sure you can load your model and tokenizer using AutoClasses:
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```python
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from transformers import AutoConfig, AutoModel, AutoTokenizer
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config = AutoConfig.from_pretrained("your model name", revision=revision)
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model = AutoModel.from_pretrained("your model name", revision=revision)
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tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
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```
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-
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-
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###
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It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
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### 3) Make sure your model has an open license!
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This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model π€
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### 4
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When we add extra information about models to the leaderboard, it will be automatically taken from the model card
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### 5
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<b>Note:</b> Please be advised that when submitting, git <b>branches</b> and <b>tags</b> will be strictly tied to the <b>specific commit</b> present at the time of submission. This ensures revision consistency.
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## Model types
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{icons}
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"""
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Models added here will be automatically evaluated on the π€ cluster.
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> **Important:** Don't forget to read the [FAQ](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/faq) and [documentation](https://huggingface.co/docs/leaderboards/open_llm_leaderboard/about) for more information! π
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## Submission Disclaimer
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**By submitting a model, you acknowledge that:**
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- We store information about who submitted each model in [Requests dataset](https://huggingface.co/datasets/open-llm-leaderboard/requests).
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- This practice helps maintain the integrity of our leaderboard, prevent spam, and ensure responsible submissions.
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- Your submission will be visible to the community and you may be contacted regarding your model.
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- Please submit carefully and responsibly π
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## First Steps Before Submitting a Model
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### 1. Ensure Your Model Loads with AutoClasses
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Verify that you can load your model and tokenizer using AutoClasses:
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```python
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from transformers import AutoConfig, AutoModel, AutoTokenizer
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config = AutoConfig.from_pretrained("your model name", revision=revision)
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model = AutoModel.from_pretrained("your model name", revision=revision)
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tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
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```
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Note:
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- If this step fails, debug your model before submitting.
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- Ensure your model is public.
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- We are working on adding support for models requiring `use_remote_code=True`.
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### 2. Convert Weights to Safetensors
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[Safetensors](https://huggingface.co/docs/safetensors/index) is a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
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### 3. Verify Your Model Open License
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This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model π€
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### 4. Complete Your Model Card
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When we add extra information about models to the leaderboard, it will be automatically taken from the model card
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### 5. Select Correct Precision
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Choose the right precision to avoid evaluation errors:
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- Not all models convert properly from float16 to bfloat16.
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- Incorrect precision can cause issues (e.g., loading a bf16 model in fp16 may generate NaNs).
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> **Important:** When submitting, git branches and tags will be strictly tied to the specific commit present at the time of submission to ensure revision consistency.
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## Model types
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{icons}
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"""
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src/submission/submit.py
CHANGED
@@ -1,5 +1,6 @@
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import json
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import os
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from datetime import datetime, timezone
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from dataclasses import dataclass
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weight_type: str,
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model_type: str,
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use_chat_template: bool,
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-
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global REQUESTED_MODELS
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global USERS_TO_SUBMISSION_DATES
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if not REQUESTED_MODELS:
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REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
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-
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model_path = model
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if "/" in model:
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-
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model_path = model.split("/")[1]
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precision = precision.split(" ")[0]
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if model_type is None or model_type == "":
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return styled_error("Please select a model type.")
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# Is the user rate limited?
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if
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user_can_submit, error_msg = user_submission_permission(
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)
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if not user_can_submit:
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return styled_error(error_msg)
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@@ -144,7 +158,6 @@ def add_new_eval(
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# Seems good, creating the eval
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print("Adding new eval")
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-
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eval_entry = {
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"model": model,
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"base_model": base_model,
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"job_id": -1,
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"job_start_time": None,
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"use_chat_template": use_chat_template,
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}
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print("Creating eval file")
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OUT_DIR = f"{EVAL_REQUESTS_PATH}/{
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os.makedirs(OUT_DIR, exist_ok=True)
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out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"
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f.write(json.dumps(eval_entry))
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print("Uploading eval file")
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API.upload_file(
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path_or_fileobj=out_path,
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path_in_repo=out_path.split("eval-queue/")[1],
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import json
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import os
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import gradio as gr
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from datetime import datetime, timezone
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from dataclasses import dataclass
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weight_type: str,
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model_type: str,
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use_chat_template: bool,
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profile: gr.OAuthProfile | None
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):
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# Login require
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if profile is None:
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return styled_error("Hub Login Required")
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# Name of the actual user who sent the request
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username = profile.username
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global REQUESTED_MODELS
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global USERS_TO_SUBMISSION_DATES
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if not REQUESTED_MODELS:
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REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
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org_or_user = ""
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model_path = model
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if "/" in model:
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org_or_user = model.split("/")[0]
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model_path = model.split("/")[1]
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precision = precision.split(" ")[0]
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if model_type is None or model_type == "":
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return styled_error("Please select a model type.")
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# Is user submitting own model?
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# Check that username in the org.
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# if org_or_user != profile.username:
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# Is the user rate limited?
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if org_or_user != "":
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user_can_submit, error_msg = user_submission_permission(
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org_or_user, USERS_TO_SUBMISSION_DATES, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA
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)
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if not user_can_submit:
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return styled_error(error_msg)
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# Seems good, creating the eval
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print("Adding new eval")
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eval_entry = {
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"model": model,
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"base_model": base_model,
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"job_id": -1,
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"job_start_time": None,
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"use_chat_template": use_chat_template,
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"sender": username
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}
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print("Creating eval file")
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OUT_DIR = f"{EVAL_REQUESTS_PATH}/{org_or_user}"
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os.makedirs(OUT_DIR, exist_ok=True)
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out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"
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f.write(json.dumps(eval_entry))
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print("Uploading eval file")
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print(eval_entry)
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API.upload_file(
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path_or_fileobj=out_path,
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path_in_repo=out_path.split("eval-queue/")[1],
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