初始化项目,由ModelHub XC社区提供模型

Model: distil-labs/Distil-PII-gemma-3-270m-it
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-07-08 01:51:10 +08:00
commit 24268c52b6
17 changed files with 52027 additions and 0 deletions

36
.gitattributes vendored Normal file
View File

@@ -0,0 +1,36 @@
*.7z filter=lfs diff=lfs merge=lfs -text
*.arrow filter=lfs diff=lfs merge=lfs -text
*.bin filter=lfs diff=lfs merge=lfs -text
*.bz2 filter=lfs diff=lfs merge=lfs -text
*.ckpt filter=lfs diff=lfs merge=lfs -text
*.ftz filter=lfs diff=lfs merge=lfs -text
*.gz filter=lfs diff=lfs merge=lfs -text
*.h5 filter=lfs diff=lfs merge=lfs -text
*.joblib filter=lfs diff=lfs merge=lfs -text
*.lfs.* filter=lfs diff=lfs merge=lfs -text
*.mlmodel filter=lfs diff=lfs merge=lfs -text
*.model filter=lfs diff=lfs merge=lfs -text
*.msgpack filter=lfs diff=lfs merge=lfs -text
*.npy filter=lfs diff=lfs merge=lfs -text
*.npz filter=lfs diff=lfs merge=lfs -text
*.onnx filter=lfs diff=lfs merge=lfs -text
*.ot filter=lfs diff=lfs merge=lfs -text
*.parquet filter=lfs diff=lfs merge=lfs -text
*.pb filter=lfs diff=lfs merge=lfs -text
*.pickle filter=lfs diff=lfs merge=lfs -text
*.pkl filter=lfs diff=lfs merge=lfs -text
*.pt filter=lfs diff=lfs merge=lfs -text
*.pth filter=lfs diff=lfs merge=lfs -text
*.rar filter=lfs diff=lfs merge=lfs -text
*.safetensors filter=lfs diff=lfs merge=lfs -text
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
*.tar.* filter=lfs diff=lfs merge=lfs -text
*.tar filter=lfs diff=lfs merge=lfs -text
*.tflite filter=lfs diff=lfs merge=lfs -text
*.tgz filter=lfs diff=lfs merge=lfs -text
*.wasm filter=lfs diff=lfs merge=lfs -text
*.xz filter=lfs diff=lfs merge=lfs -text
*.zip filter=lfs diff=lfs merge=lfs -text
*.zst filter=lfs diff=lfs merge=lfs -text
*tfevents* filter=lfs diff=lfs merge=lfs -text
tokenizer.json filter=lfs diff=lfs merge=lfs -text

31
LICENSE Normal file
View File

@@ -0,0 +1,31 @@
GENERAL TERMS AND CONDITIONS
Note that if you want to use the Commercial licence, please contact us at contact@distillabs.ai
- Model License Terms -
R&D License
1. SERVICES, PRICES AND PAYMENT
1.1 The Customer pays a one-time license fee, as indicated in the check-out process, for running of one (1) training process of the selected Base Model using Customer Data (“License Fee”).
1.2 The License Fee shall be due for payment in advance. The Customer shall only be permitted to set off against payment claims of Distil Labs if the Customers claims are undisputed or have become res judicata.
2. MODEL LICENSE: R&D LICENSE
2.1 Subject to Customers payment of the license fee, Distil Labs grants to Customer the Model License (as defined below). For clarification, Distil Labs retains any other rights in its software or know- how, in particular in the codebase needed for the fine-tuning of the Trained Model.
2.2 Subject to the requirements of the Base Model License (cf. Section 2.5 below), Distil Labs transfers to the Customer the perpetual, non-exclusive usage right to the Trained Model for non-commercial purposes of prototyping and research & development. The Parties agree, that commercial purposes include deployment in production externally (to be used by Customers customers paid or free of charge) or internally (as a tool for Customers employees). The territorial scope of the license is limited to the use within the United States of America and the European Economic Area including all member states of the European Union (“Model License”).
2.3 The Model License for non-commercial purposes of prototyping and research & development shall include (i) the non-exclusive right to permanent or temporary reproduction, in whole or in part, by any means and in any form (e.g. permanent and/or volatile storage on electrical, electromagnetic, optical storage media, such as any type of SDD, HDD, DVD, memory cards, USB sticks), (ii) the non-exclusive right to distribution in any form, media and by any means regardless of whether the distribution is in tangible or intangible form, in particular to transmit the Trained Model via wired and wireless networks (e.g. for download from internet or intranet by wire or wireless means including broadband, cable, fiberglass, WIFI, LTE, 5G, satellite internet, other data networks), and (iii) the non-exclusive right of making available to the public in such a way that members of the public can access it from places and at times of their choice (e.g. by web or mobile app, virtual or augmented reality, cloud storage, cloud hosting, decentralized hosting, non-fungible token, application service providing, software as a service, or cloud computing). The license shall also contain, to the extent necessary for prototyping and research & development, the right to adapt and modify the Trained Model subject to the limitation in Section 2.4 and 2.5 below, to further develop the Trained Model including changes to functions or appearance, adapt to other software versions, to exchange parts of the Trained Model or combine the Trained Model with other results of work and to use the results in the same way as the original Trained Model. Any derived models from the Trained Model shall retain this model license.
2.4 The Customer shall not, without the prior written consent of Distil Labs:
2.4.1 train, fine-tune, re-train, or otherwise modify the Trained Model, unless for purpose of research & development;
2.4.2 use the Trained Model or any part thereof to create derivative models or services that compete with those of Distil Labs;
2.4.3 circumvent any technical restrictions embedded in the Trained Model or Base Model that are designed to enforce usage limitations.
2.5 The Parties acknowledge and agree that the Trained Model is developed from Base Models which are supplied by a third party. Therefore, the Model License is subject to the restrictions resulting from the open-source or any other applicable license of the Base Model (“Base Model License”) and the Customer must use the Trained Model in compliance with the Base Model License. In particular, the Customer must oblige their clients to compliance with the Base Model License in any case of transferring or sublicensing the rights to or making available in any way the Trained Model. The applicable Base Model License is defined in the Training Configuration and will be provided for download. The Customer agrees to indemnify Distil Labs for any and all claims brought by the Base Model provider for violations of the Base Model License.

1
Modelfile Normal file
View File

@@ -0,0 +1 @@
FROM .

186
README.md Normal file
View File

@@ -0,0 +1,186 @@
---
license: gemma
language: en
base_model: google/gemma-3-270m
pipeline_tag: text-generation
tags: [pii-redaction, privacy, slm, distil-labs]
---
<div align="center">
<img src="https://github.com/distil-labs/badges/blob/main/distillabs-logo.svg?raw=true" width="40%" alt="distil labs" />
</div>
---
<div align="center">
<table>
<tr>
<td align="center">
<a href="https://www.distillabs.ai/?utm_source=hugging-face&utm_medium=referral&utm_campaign=distil-PII">
<img src="https://github.com/distil-labs/badges/blob/main/badge-distillabs-home.svg?raw=true" alt="Homepage"/>
</a>
</td>
<td align="center">
<a href="https://github.com/distil-labs">
<img src="https://github.com/distil-labs/badges/blob/main/badge-github.svg?raw=true" alt="GitHub"/>
</a>
</td>
<td align="center">
<a href="https://huggingface.co/distil-labs">
<img src="https://github.com/distil-labs/badges/blob/main/badge-huggingface.svg?raw=true" alt="Hugging Face"/>
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://www.linkedin.com/company/distil-labs/">
<img src="https://github.com/distil-labs/badges/blob/main/badge-linkedin.svg?raw=true" alt="LinkedIn"/>
</a>
</td>
<td align="center">
<a href="https://distil-labs-community.slack.com/join/shared_invite/zt-36zqj87le-i3quWUn2bjErRq22xoE58g">
<img src="https://github.com/distil-labs/badges/blob/main/badge-slack.svg?raw=true" alt="Slack"/>
</a>
</td>
<td align="center">
<a href="https://x.com/distil_labs">
<img src="https://github.com/distil-labs/badges/blob/main/badge-twitter.svg?raw=true" alt="Twitter"/>
</a>
</td>
</tr>
</table>
</div>
---
# Distil-PII-Llama-3.2-3B-Instruct
A **small language model** (SLM) fine-tuned by Distil Labs for **policy-aware PII redaction** that outputs a single JSON object with `redacted_text` and `entities`. Optimized to run locally with strong accuracy and strict schema adherence.
## Model Details
* **Developed by:** Distil Labs GmbH
* **License:** Gemma3 License Agreement
* **Finetuned from:** `google/gemma-3-270m`
## Intended Use & Limitations
* **Use cases:** Redacting support chats, logs, tickets, transcripts—removing identity while preserving ops signals (IDs last-4, order numbers, etc.).
* **Out of scope:** Legal or compliance advice; languages beyond English (generalization not guaranteed); domain-specific IDs unseen in training.
## Input & Output
**Input:** A plain-text prompt with task instruction + context.
**Output (JSON only):**
```json
{
"redacted_text": "Text with in-place tokens",
"entities": [
{"value": "<original>", "replacement_token": "[TOKEN]", "reason": "<why>"}
]
}
```
**Tokens:** `[PERSON] [EMAIL] [PHONE] [ADDRESS] [SSN] [ID] [UUID] [CARD_LAST4:####] [IBAN_LAST4:####] [GENDER] [AGE] [RACE] [MARITAL_STATUS]`
## Training
Instruction-tuned on a compact policy spec + ~20 curated examples emphasizing **exact JSON schema**, **minimal in-place edits**, and **entity correctness**.
## Evaluation
Judged by a frontier LLM using a deterministic rubric: JSON-only, schema validity, **redacted_text exact match**, and **set-equality** of `(value, replacement_token)` pairs (reason/order ignored). Score: **0.73 +/- 0.07**.
## How to Use
Details of deployment can be found in [docs](https://docs.distillabs.ai/how-to/model-deployment). Deploy the model using vllm or ollama (-gguf version available in this collection) and use the following snippet to get results
```python
SYSTEM_PROMPT = """
You are a problem solving model working on task_description XML block:
<task_description>
Produce a redacted version of texts, removing sensitive personal data while preserving operational signals. The model must return a single json blob with:
* **redacted_text** is the input with minimal, in-place replacements of redacted entities.
* **entities** as an array of objects with exactly three fields {value: original_value, replacement_token: replacement, reason: reasoning}.
## What to redact (→ replacement token)
* **PERSON** — customer/patient/person names (first/last/full; identifying initials) → `[PERSON]`
* **EMAIL** — any email, including obfuscated `name(at)domain(dot)com` → `[EMAIL]`
* **PHONE** — any international/national format (separators/emoji bullets allowed) → `[PHONE]`
* **ADDRESS** — street + number; full postal lines; apartment/unit numbers → `[ADDRESS]`
* **SSN** — US Social Security numbers → `[SSN]`
* **ID** — national IDs (PESEL, NIN, Aadhaar, DNI, etc.) when personal → `[ID]`
* **UUID** — person-scoped system identifiers (e.g., MRN/NHS/patient IDs/customer UUIDs) → `[UUID]`
* **CREDIT_CARD** — 1319 digits (spaces/hyphens allowed) → `[CARD_LAST4:####]` (keep last-4 only)
* **IBAN** — IBAN/bank account numbers → `[IBAN_LAST4:####]` (keep last-4 only)
* **GENDER** — self-identification (male/female/non-binary/etc.) → `[GENDER]`
* **AGE** — stated ages (“Im 29”, “age: 47”, “29 y/o”) → `[AGE_YEARS:##]`
* **RACE** — race/ethnicity self-identification → `[RACE]`
* **MARITAL_STATUS** — married/single/divorced/widowed/partnered → `[MARITAL_STATUS]`
## Keep (do not redact)
* Card **last-4** when only last-4 is present (e.g., “ending 9021”, “•••• 9021”).
* Operational IDs: order/ticket/invoice numbers, shipment tracking, device serials, case IDs.
* Non-personal org info: company names, product names, team names.
* Cities/countries alone (redact full street+number, not plain city/country mentions).
## Output schema (exactly these fields)
* **redacted_text** The original text with all the sensitive information replaced with redacted tokens
* **entities** Array with all the replaced elements, each element represented by following fields
* **replacement_token**: one of `[PERSON] | [EMAIL] | [PHONE] | [ADDRESS] | [SSN] | [ID] | [UUID] | [CREDIT_CARD] | [IBAN] | [GENDER] | [AGE] | [RACE] | [MARITAL_STATUS]`
* **value**: original text that was redacted
* **reason**: brief string explaining the rule/rationale
for example
{
"redacted_text": "Hi, I'm [PERSON] and my email is [EMAIL].",
"entities": [
{ "type": "PERSON", "value": "John Smith", "reason": "person name"},
{ "type": "EMAIL", "value": "john.smith@example.com", "reason": "email"},
]
}
</task_description>
You will be given a single task with context in the context XML block and the task in the question XML block
Solve the task in question block based on the context in context block.
Generate only the answer, do not generate anything else
"""
PROMPT_TEMPLATE = """
Now for the real task, solve the task in question block based on the context in context block.
Generate only the solution, do not generate anything else
<context>
{context}
</context>
<question>Redact provided text according to the task description and return redacted elements.</question>
"""
from openai import OpenAI
PORT = "PORT GOES HERE" # 8000 for vllm, 11434 for ollama
MODEL_NAME = "NAME USED FOR SETTING UP THE CLIENT"
TEXT_TO_REDACT = "NI number AB123456C confirmed."
client = OpenAI(base_url=f"http://127.0.0.1:{PORT}/v1", api_key="EMPTY")
chat_response = client.chat.completions.create(
model=MODEL_NAME,
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": PROMPT_TEMPLATE.format(context=TEXT_TO_REDACT)},
],
temperature=0,
)
```
## Risks & Mitigations
* **False negatives/positives:** May miss novel formats or over-redact generic terms. Mitigate via guardrails + post-validation.
* **Policy drift:** Keep task preamble fixed; monitor with unit tests.
## Model Sources
* **Homepage:** [https://distillabs.ai](https://distillabs.ai)
* **Contact:** [contact@distillabs.ai](mailto:contact@distillabs.ai)

78
STUDENT_LICENSE Normal file
View File

@@ -0,0 +1,78 @@
Gemma Terms of Use
Copyright 2025 Google
Last modified: March 24, 2025
By using, reproducing, modifying, distributing, performing or displaying any portion or element of Gemma, Model Derivatives including via any Hosted Service, (each as defined below) (collectively, the "Gemma Services") or otherwise accepting the terms of this Agreement, you agree to be bound by this Agreement.
Section 1: DEFINITIONS
1.1 Definitions
(a) "Agreement" or "Gemma Terms of Use" means these terms and conditions that govern the use, reproduction, Distribution or modification of the Gemma Services and any terms and conditions incorporated by reference.
(b) "Distribution" or "Distribute" means any transmission, publication, or other sharing of Gemma or Model Derivatives to a third party, including by providing or making Gemma or its functionality available as a hosted service via API, web access, or any other electronic or remote means ("Hosted Service").
(c) "Gemma" means the set of machine learning language models, trained model weights and parameters identified in the Appendix, regardless of the source that you obtained it from.
(d) "Google" means Google LLC.
(e) "Model Derivatives" means all (i) modifications to Gemma, (ii) works based on Gemma, or (iii) any other machine learning model which is created by transfer of patterns of the weights, parameters, operations, or Output of Gemma, to that model in order to cause that model to perform similarly to Gemma, including distillation methods that use intermediate data representations or methods based on the generation of synthetic data Outputs by Gemma for training that model. For clarity, Outputs are not deemed Model Derivatives.
(f) "Output" means the information content output of Gemma or a Model Derivative that results from operating or otherwise using Gemma or the Model Derivative, including via a Hosted Service.
1.2
As used in this Agreement, "including" means "including without limitation".
Section 2: ELIGIBILITY AND USAGE
2.1 Eligibility
You represent and warrant that you have the legal capacity to enter into this Agreement (including being of sufficient age of consent). If you are accessing or using any of the Gemma Services for or on behalf of a legal entity, (a) you are entering into this Agreement on behalf of yourself and that legal entity, (b) you represent and warrant that you have the authority to act on behalf of and bind that entity to this Agreement and (c) references to "you" or "your" in the remainder of this Agreement refers to both you (as an individual) and that entity.
2.2 Use
You may use, reproduce, modify, Distribute, perform or display any of the Gemma Services only in accordance with the terms of this Agreement, and must not violate (or encourage or permit anyone else to violate) any term of this Agreement.
Section 3: DISTRIBUTION AND RESTRICTIONS
3.1 Distribution and Redistribution
You may reproduce or Distribute copies of Gemma or Model Derivatives if you meet all of the following conditions:
You must include the use restrictions referenced in Section 3.2 as an enforceable provision in any agreement (e.g., license agreement, terms of use, etc.) governing the use and/or distribution of Gemma or Model Derivatives and you must provide notice to subsequent users you Distribute to that Gemma or Model Derivatives are subject to the use restrictions in Section 3.2.
You must provide all third party recipients of Gemma or Model Derivatives a copy of this Agreement.
You must cause any modified files to carry prominent notices stating that you modified the files.
All Distributions (other than through a Hosted Service) must be accompanied by a "Notice" text file that contains the following notice: "Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms".
You may add your own intellectual property statement to your modifications and, except as set forth in this Section, may provide additional or different terms and conditions for use, reproduction, or Distribution of your modifications, or for any such Model Derivatives as a whole, provided your use, reproduction, modification, Distribution, performance, and display of Gemma otherwise complies with the terms and conditions of this Agreement. Any additional or different terms and conditions you impose must not conflict with the terms of this Agreement.
3.2 Use Restrictions
You must not use any of the Gemma Services:
for the restricted uses set forth in the Gemma Prohibited Use Policy at ai.google.dev/gemma/prohibited_use_policy ("Prohibited Use Policy"), which is hereby incorporated by reference into this Agreement; or
in violation of applicable laws and regulations.
To the maximum extent permitted by law, Google reserves the right to restrict (remotely or otherwise) usage of any of the Gemma Services that Google reasonably believes are in violation of this Agreement.
3.3 Generated Output
Google claims no rights in Outputs you generate using Gemma. You and your users are solely responsible for Outputs and their subsequent uses.
Section 4: ADDITIONAL PROVISIONS
4.1 Updates
Google may update Gemma from time to time.
4.2 Trademarks
Nothing in this Agreement grants you any rights to use Google's trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between you and Google. Google reserves any rights not expressly granted herein.
4.3 DISCLAIMER OF WARRANTY
UNLESS REQUIRED BY APPLICABLE LAW, THE GEMMA SERVICES, AND OUTPUTS, ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING ANY WARRANTIES OR CONDITIONS OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING, REPRODUCING, MODIFYING, PERFORMING, DISPLAYING OR DISTRIBUTING ANY OF THE GEMMA SERVICES OR OUTPUTS AND ASSUME ANY AND ALL RISKS ASSOCIATED WITH YOUR USE OR DISTRIBUTION OF ANY OF THE GEMMA SERVICES OR OUTPUTS AND YOUR EXERCISE OF RIGHTS AND PERMISSIONS UNDER THIS AGREEMENT.
4.4 LIMITATION OF LIABILITY
TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), PRODUCT LIABILITY, CONTRACT, OR OTHERWISE, UNLESS REQUIRED BY APPLICABLE LAW, SHALL GOOGLE OR ITS AFFILIATES BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT, INDIRECT, SPECIAL, INCIDENTAL, EXEMPLARY, CONSEQUENTIAL, OR PUNITIVE DAMAGES, OR LOST PROFITS OF ANY KIND ARISING FROM THIS AGREEMENT OR RELATED TO, ANY OF THE GEMMA SERVICES OR OUTPUTS EVEN IF GOOGLE OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
4.5 Term, Termination, and Survival
The term of this Agreement will commence upon your acceptance of this Agreement (including acceptance by your use, modification, or Distribution, reproduction, performance or display of any portion or element of the Gemma Services) and will continue in full force and effect until terminated in accordance with the terms of this Agreement. Google may terminate this Agreement if you are in breach of any term of this Agreement. Upon termination of this Agreement, you must delete and cease use and Distribution of all copies of Gemma and Model Derivatives in your possession or control. Sections 1, 2.1, 3.3, 4.2 to 4.9 shall survive the termination of this Agreement.
4.6 Governing Law and Jurisdiction
This Agreement will be governed by the laws of the State of California without regard to choice of law principles. The UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The state and federal courts of Santa Clara County, California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
4.7 Severability
If any provision of this Agreement is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
4.8 Entire Agreement
This Agreement states all the terms agreed between the parties and supersedes all other agreements between the parties as of the date of acceptance relating to its subject matter.
4.9 No Waiver
Google will not be treated as having waived any rights by not exercising (or delaying the exercise of) any rights under this Agreement.

9
TEACHER_LICENSE Normal file
View File

@@ -0,0 +1,9 @@
MIT License
Copyright (c) 2023 DeepSeek
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

3
added_tokens.json Normal file
View File

@@ -0,0 +1,3 @@
{
"<image_soft_token>": 262144
}

47
chat_template.jinja Normal file
View File

@@ -0,0 +1,47 @@
{{ bos_token }}
{%- if messages[0]['role'] == 'system' -%}
{%- if messages[0]['content'] is string -%}
{%- set first_user_prefix = messages[0]['content'] + '
' -%}
{%- else -%}
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
' -%}
{%- endif -%}
{%- set loop_messages = messages[1:] -%}
{%- else -%}
{%- set first_user_prefix = "" -%}
{%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
{%- endif -%}
{%- if (message['role'] == 'assistant') -%}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{{ '<start_of_turn>' + role + '
' + (first_user_prefix if loop.first else "") }}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is iterable -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'image' -%}
{{ '<start_of_image>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type") }}
{%- endif -%}
{{ '<end_of_turn>
' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{'<start_of_turn>model
'}}
{%- endif -%}

55
config.json Normal file
View File

@@ -0,0 +1,55 @@
{
"_sliding_window_pattern": 6,
"architectures": [
"Gemma3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": null,
"bos_token_id": 2,
"eos_token_id": 1,
"final_logit_softcapping": null,
"head_dim": 256,
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 640,
"initializer_range": 0.02,
"intermediate_size": 2048,
"layer_types": [
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"model_type": "gemma3_text",
"num_attention_heads": 4,
"num_hidden_layers": 18,
"num_key_value_heads": 1,
"pad_token": "<pad>",
"pad_token_id": 0,
"query_pre_attn_scalar": 256,
"rms_norm_eps": 1e-06,
"rope_local_base_freq": 10000.0,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": 512,
"torch_dtype": "bfloat16",
"transformers_version": "4.53.0",
"use_bidirectional_attention": false,
"use_cache": true,
"vocab_size": 262144
}

11
generation_config.json Normal file
View File

@@ -0,0 +1,11 @@
{
"cache_implementation": "hybrid",
"do_sample": true,
"eos_token_id": [
1,
106
],
"top_k": 64,
"top_p": 0.95,
"transformers_version": "4.53.0"
}

3
model.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:f00f1db800a3583afe82ab6f3704ecf3cf6dc3039dc1d21060f913ff3420e312
size 536223056

33
special_tokens_map.json Normal file
View File

@@ -0,0 +1,33 @@
{
"boi_token": "<start_of_image>",
"bos_token": {
"content": "<bos>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"eoi_token": "<end_of_image>",
"eos_token": {
"content": "<eos>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"image_token": "<image_soft_token>",
"pad_token": {
"content": "<pad>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"unk_token": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

3
tokenizer.json Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1945747f843b77ba1ce3c1ce4083d79b80e84f87bf4adeb5e247c9081f4343a3
size 33384827

3
tokenizer.model Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
size 4689074

51345
tokenizer_config.json Normal file

File diff suppressed because it is too large Load Diff

92
training-logs.csv Normal file
View File

@@ -0,0 +1,92 @@
,eval_loss,eval_binary,eval_rouge,eval_llm_as_a_judge,eval_runtime,eval_samples_per_second,eval_steps_per_second,epoch,step,loss,grad_norm,learning_rate,train_runtime,train_samples_per_second,train_steps_per_second,total_flos,train_loss
0,2.046112060546875,0.0,0.5833271075254629,0.0,28.9848,0.828,0.207,0.0,0,,,,,,,,
1,,,,,,,,0.09858044164037855,250,0.5483,12.5625,1.2266009852216749e-05,,,,,
2,,,,,,,,0.1971608832807571,500,0.1428,6.03125,2.458128078817734e-05,,,,,
3,,,,,,,,0.29574132492113564,750,0.1048,4.5625,3.6896551724137934e-05,,,,,
4,,,,,,,,0.3943217665615142,1000,0.0944,3.8125,4.9211822660098524e-05,,,,,
5,,,,,,,,0.49290220820189273,1250,0.0833,2.296875,4.939293311887096e-05,,,,,
6,,,,,,,,0.5914826498422713,1500,0.0741,3.890625,4.874435739116899e-05,,,,,
7,,,,,,,,0.6900630914826499,1750,0.0674,2.3125,4.8095781663467026e-05,,,,,
8,,,,,,,,0.7886435331230284,2000,0.0653,2.484375,4.744720593576506e-05,,,,,
9,,,,,,,,0.887223974763407,2250,0.0605,2.3125,4.679863020806309e-05,,,,,
10,,,,,,,,0.9858044164037855,2500,0.056,1.4609375,4.6150054480361124e-05,,,,,
11,0.12178646773099899,0.16666666666666666,0.9454337879689271,0.25,39.1542,0.613,0.153,1.0,2536,,,,,,,,
12,,,,,,,,1.084384858044164,2750,0.0497,1.203125,4.550147875265916e-05,,,,,
13,,,,,,,,1.1829652996845426,3000,0.0479,2.46875,4.4852903024957196e-05,,,,,
14,,,,,,,,1.2815457413249212,3250,0.0467,1.671875,4.420432729725523e-05,,,,,
15,,,,,,,,1.3801261829652998,3500,0.0462,1.8515625,4.355575156955327e-05,,,,,
16,,,,,,,,1.4787066246056781,3750,0.0463,1.671875,4.29071758418513e-05,,,,,
17,,,,,,,,1.5772870662460567,4000,0.0427,1.1484375,4.2258600114149334e-05,,,,,
18,,,,,,,,1.6758675078864353,4250,0.0462,2.03125,4.1610024386447366e-05,,,,,
19,,,,,,,,1.774447949526814,4500,0.0411,1.0859375,4.09614486587454e-05,,,,,
20,,,,,,,,1.8730283911671926,4750,0.0422,1.5234375,4.031287293104343e-05,,,,,
21,,,,,,,,1.971608832807571,5000,0.0405,2.0,3.9664297203341464e-05,,,,,
22,0.12576398253440857,0.16666666666666666,0.9411790557730185,0.25,40.3742,0.594,0.149,2.0,5072,,,,,,,,
23,,,,,,,,2.0701892744479493,5250,0.036,1.1953125,3.90157214756395e-05,,,,,
24,,,,,,,,2.168769716088328,5500,0.0334,0.91796875,3.836714574793753e-05,,,,,
25,,,,,,,,2.2673501577287065,5750,0.035,1.3046875,3.771857002023557e-05,,,,,
26,,,,,,,,2.365930599369085,6000,0.0337,1.4609375,3.70699942925336e-05,,,,,
27,,,,,,,,2.4645110410094637,6250,0.0334,1.5390625,3.6421418564831635e-05,,,,,
28,,,,,,,,2.5630914826498423,6500,0.0322,1.546875,3.577284283712967e-05,,,,,
29,,,,,,,,2.661671924290221,6750,0.0344,2.109375,3.51242671094277e-05,,,,,
30,,,,,,,,2.7602523659305995,7000,0.0337,1.9609375,3.447569138172573e-05,,,,,
31,,,,,,,,2.8588328075709777,7250,0.0321,2.359375,3.3827115654023766e-05,,,,,
32,,,,,,,,2.9574132492113563,7500,0.0322,1.078125,3.31785399263218e-05,,,,,
33,0.13283216953277588,0.25,0.9450974688536018,0.25,37.2465,0.644,0.161,3.0,7608,,,,,,,,
34,,,,,,,,3.055993690851735,7750,0.0287,0.9765625,3.252996419861983e-05,,,,,
35,,,,,,,,3.1545741324921135,8000,0.0265,1.7734375,3.1881388470917864e-05,,,,,
36,,,,,,,,3.253154574132492,8250,0.0264,1.0078125,3.12328127432159e-05,,,,,
37,,,,,,,,3.3517350157728707,8500,0.028,1.2265625,3.0584237015513936e-05,,,,,
38,,,,,,,,3.4503154574132493,8750,0.0281,1.390625,2.993566128781197e-05,,,,,
39,,,,,,,,3.548895899053628,9000,0.0276,0.90625,2.928708556011e-05,,,,,
40,,,,,,,,3.6474763406940065,9250,0.0258,2.0625,2.8638509832408034e-05,,,,,
41,,,,,,,,3.746056782334385,9500,0.0261,0.921875,2.7989934104706067e-05,,,,,
42,,,,,,,,3.8446372239747633,9750,0.0263,0.61328125,2.73413583770041e-05,,,,,
43,,,,,,,,3.943217665615142,10000,0.027,1.125,2.6692782649302132e-05,,,,,
44,0.14927507936954498,0.25,0.9437395032119867,0.25,36.3204,0.661,0.165,4.0,10144,,,,,,,,
45,,,,,,,,4.041798107255521,10250,0.0244,1.5625,2.6044206921600168e-05,,,,,
46,,,,,,,,4.140378548895899,10500,0.0217,1.2578125,2.53956311938982e-05,,,,,
47,,,,,,,,4.238958990536277,10750,0.0226,1.9765625,2.4747055466196234e-05,,,,,
48,,,,,,,,4.337539432176656,11000,0.0215,2.015625,2.4098479738494266e-05,,,,,
49,,,,,,,,4.436119873817034,11250,0.023,2.015625,2.34499040107923e-05,,,,,
50,,,,,,,,4.534700315457413,11500,0.0225,2.34375,2.2801328283090335e-05,,,,,
51,,,,,,,,4.633280757097792,11750,0.021,1.5703125,2.2152752555388368e-05,,,,,
52,,,,,,,,4.73186119873817,12000,0.022,1.28125,2.15041768276864e-05,,,,,
53,,,,,,,,4.830441640378549,12250,0.021,0.91015625,2.0855601099984433e-05,,,,,
54,,,,,,,,4.929022082018927,12500,0.0212,1.84375,2.0207025372282466e-05,,,,,
55,0.16193042695522308,0.2916666666666667,0.9449687757852137,0.3333333333333333,35.8498,0.669,0.167,5.0,12680,,,,,,,,
56,,,,,,,,5.027602523659306,12750,0.0208,1.390625,1.9558449644580502e-05,,,,,
57,,,,,,,,5.126182965299685,13000,0.0177,1.703125,1.8909873916878535e-05,,,,,
58,,,,,,,,5.224763406940063,13250,0.0172,1.6953125,1.8261298189176567e-05,,,,,
59,,,,,,,,5.323343848580442,13500,0.0185,1.4375,1.7612722461474604e-05,,,,,
60,,,,,,,,5.4219242902208205,13750,0.018,1.1796875,1.6964146733772636e-05,,,,,
61,,,,,,,,5.520504731861199,14000,0.0181,2.046875,1.631557100607067e-05,,,,,
62,,,,,,,,5.619085173501578,14250,0.0174,3.59375,1.5666995278368705e-05,,,,,
63,,,,,,,,5.717665615141955,14500,0.0189,2.96875,1.5018419550666738e-05,,,,,
64,,,,,,,,5.816246056782335,14750,0.0183,1.6484375,1.436984382296477e-05,,,,,
65,,,,,,,,5.914826498422713,15000,0.0177,1.421875,1.3721268095262805e-05,,,,,
66,0.18538308143615723,0.2916666666666667,0.9420310554561889,0.43478260869565216,36.3402,0.66,0.165,6.0,15216,,,,,,,,
67,,,,,,,,6.013406940063091,15250,0.0168,1.84375,1.3072692367560838e-05,,,,,
68,,,,,,,,6.11198738170347,15500,0.015,1.703125,1.242411663985887e-05,,,,,
69,,,,,,,,6.210567823343848,15750,0.0155,2.484375,1.1775540912156905e-05,,,,,
70,,,,,,,,6.309148264984227,16000,0.0154,1.3125,1.1126965184454937e-05,,,,,
71,,,,,,,,6.407728706624606,16250,0.0157,1.515625,1.0478389456752972e-05,,,,,
72,,,,,,,,6.506309148264984,16500,0.0149,2.71875,9.829813729051005e-06,,,,,
73,,,,,,,,6.604889589905363,16750,0.0147,2.328125,9.181238001349037e-06,,,,,
74,,,,,,,,6.703470031545741,17000,0.0157,1.8671875,8.532662273647072e-06,,,,,
75,,,,,,,,6.80205047318612,17250,0.0163,2.171875,7.884086545945104e-06,,,,,
76,,,,,,,,6.900630914826499,17500,0.016,2.421875,7.235510818243138e-06,,,,,
77,,,,,,,,6.999211356466877,17750,0.0146,2.375,6.5869350905411715e-06,,,,,
78,0.19330303370952606,0.2916666666666667,0.9420310554561889,0.4583333333333333,41.7416,0.575,0.144,7.0,17752,,,,,,,,
79,,,,,,,,7.097791798107256,18000,0.0145,2.421875,5.938359362839206e-06,,,,,
80,,,,,,,,7.196372239747634,18250,0.0146,1.5,5.289783635137239e-06,,,,,
81,,,,,,,,7.294952681388013,18500,0.0139,1.0703125,4.641207907435272e-06,,,,,
82,,,,,,,,7.393533123028391,18750,0.0135,1.890625,3.992632179733306e-06,,,,,
83,,,,,,,,7.492113564668769,19000,0.0143,1.9375,3.344056452031339e-06,,,,,
84,,,,,,,,7.590694006309148,19250,0.0142,1.7265625,2.695480724329373e-06,,,,,
85,,,,,,,,7.6892744479495265,19500,0.0146,1.59375,2.0469049966274064e-06,,,,,
86,,,,,,,,7.787854889589905,19750,0.0141,1.1875,1.39832926892544e-06,,,,,
87,,,,,,,,7.886435331230284,20000,0.0143,1.71875,7.497535412234734e-07,,,,,
88,,,,,,,,7.985015772870662,20250,0.0144,0.96484375,1.0117781352150678e-07,,,,,
89,0.19907398521900177,0.2916666666666667,0.9420310554561889,0.375,41.0489,0.585,0.146,8.0,20288,,,,,,,,
90,,,,,,,,8.0,20288,,,,6288.8277,12.903,3.226,6.467727988290509e+16,0.037849801997962625
1 eval_loss eval_binary eval_rouge eval_llm_as_a_judge eval_runtime eval_samples_per_second eval_steps_per_second epoch step loss grad_norm learning_rate train_runtime train_samples_per_second train_steps_per_second total_flos train_loss
2 0 2.046112060546875 0.0 0.5833271075254629 0.0 28.9848 0.828 0.207 0.0 0
3 1 0.09858044164037855 250 0.5483 12.5625 1.2266009852216749e-05
4 2 0.1971608832807571 500 0.1428 6.03125 2.458128078817734e-05
5 3 0.29574132492113564 750 0.1048 4.5625 3.6896551724137934e-05
6 4 0.3943217665615142 1000 0.0944 3.8125 4.9211822660098524e-05
7 5 0.49290220820189273 1250 0.0833 2.296875 4.939293311887096e-05
8 6 0.5914826498422713 1500 0.0741 3.890625 4.874435739116899e-05
9 7 0.6900630914826499 1750 0.0674 2.3125 4.8095781663467026e-05
10 8 0.7886435331230284 2000 0.0653 2.484375 4.744720593576506e-05
11 9 0.887223974763407 2250 0.0605 2.3125 4.679863020806309e-05
12 10 0.9858044164037855 2500 0.056 1.4609375 4.6150054480361124e-05
13 11 0.12178646773099899 0.16666666666666666 0.9454337879689271 0.25 39.1542 0.613 0.153 1.0 2536
14 12 1.084384858044164 2750 0.0497 1.203125 4.550147875265916e-05
15 13 1.1829652996845426 3000 0.0479 2.46875 4.4852903024957196e-05
16 14 1.2815457413249212 3250 0.0467 1.671875 4.420432729725523e-05
17 15 1.3801261829652998 3500 0.0462 1.8515625 4.355575156955327e-05
18 16 1.4787066246056781 3750 0.0463 1.671875 4.29071758418513e-05
19 17 1.5772870662460567 4000 0.0427 1.1484375 4.2258600114149334e-05
20 18 1.6758675078864353 4250 0.0462 2.03125 4.1610024386447366e-05
21 19 1.774447949526814 4500 0.0411 1.0859375 4.09614486587454e-05
22 20 1.8730283911671926 4750 0.0422 1.5234375 4.031287293104343e-05
23 21 1.971608832807571 5000 0.0405 2.0 3.9664297203341464e-05
24 22 0.12576398253440857 0.16666666666666666 0.9411790557730185 0.25 40.3742 0.594 0.149 2.0 5072
25 23 2.0701892744479493 5250 0.036 1.1953125 3.90157214756395e-05
26 24 2.168769716088328 5500 0.0334 0.91796875 3.836714574793753e-05
27 25 2.2673501577287065 5750 0.035 1.3046875 3.771857002023557e-05
28 26 2.365930599369085 6000 0.0337 1.4609375 3.70699942925336e-05
29 27 2.4645110410094637 6250 0.0334 1.5390625 3.6421418564831635e-05
30 28 2.5630914826498423 6500 0.0322 1.546875 3.577284283712967e-05
31 29 2.661671924290221 6750 0.0344 2.109375 3.51242671094277e-05
32 30 2.7602523659305995 7000 0.0337 1.9609375 3.447569138172573e-05
33 31 2.8588328075709777 7250 0.0321 2.359375 3.3827115654023766e-05
34 32 2.9574132492113563 7500 0.0322 1.078125 3.31785399263218e-05
35 33 0.13283216953277588 0.25 0.9450974688536018 0.25 37.2465 0.644 0.161 3.0 7608
36 34 3.055993690851735 7750 0.0287 0.9765625 3.252996419861983e-05
37 35 3.1545741324921135 8000 0.0265 1.7734375 3.1881388470917864e-05
38 36 3.253154574132492 8250 0.0264 1.0078125 3.12328127432159e-05
39 37 3.3517350157728707 8500 0.028 1.2265625 3.0584237015513936e-05
40 38 3.4503154574132493 8750 0.0281 1.390625 2.993566128781197e-05
41 39 3.548895899053628 9000 0.0276 0.90625 2.928708556011e-05
42 40 3.6474763406940065 9250 0.0258 2.0625 2.8638509832408034e-05
43 41 3.746056782334385 9500 0.0261 0.921875 2.7989934104706067e-05
44 42 3.8446372239747633 9750 0.0263 0.61328125 2.73413583770041e-05
45 43 3.943217665615142 10000 0.027 1.125 2.6692782649302132e-05
46 44 0.14927507936954498 0.25 0.9437395032119867 0.25 36.3204 0.661 0.165 4.0 10144
47 45 4.041798107255521 10250 0.0244 1.5625 2.6044206921600168e-05
48 46 4.140378548895899 10500 0.0217 1.2578125 2.53956311938982e-05
49 47 4.238958990536277 10750 0.0226 1.9765625 2.4747055466196234e-05
50 48 4.337539432176656 11000 0.0215 2.015625 2.4098479738494266e-05
51 49 4.436119873817034 11250 0.023 2.015625 2.34499040107923e-05
52 50 4.534700315457413 11500 0.0225 2.34375 2.2801328283090335e-05
53 51 4.633280757097792 11750 0.021 1.5703125 2.2152752555388368e-05
54 52 4.73186119873817 12000 0.022 1.28125 2.15041768276864e-05
55 53 4.830441640378549 12250 0.021 0.91015625 2.0855601099984433e-05
56 54 4.929022082018927 12500 0.0212 1.84375 2.0207025372282466e-05
57 55 0.16193042695522308 0.2916666666666667 0.9449687757852137 0.3333333333333333 35.8498 0.669 0.167 5.0 12680
58 56 5.027602523659306 12750 0.0208 1.390625 1.9558449644580502e-05
59 57 5.126182965299685 13000 0.0177 1.703125 1.8909873916878535e-05
60 58 5.224763406940063 13250 0.0172 1.6953125 1.8261298189176567e-05
61 59 5.323343848580442 13500 0.0185 1.4375 1.7612722461474604e-05
62 60 5.4219242902208205 13750 0.018 1.1796875 1.6964146733772636e-05
63 61 5.520504731861199 14000 0.0181 2.046875 1.631557100607067e-05
64 62 5.619085173501578 14250 0.0174 3.59375 1.5666995278368705e-05
65 63 5.717665615141955 14500 0.0189 2.96875 1.5018419550666738e-05
66 64 5.816246056782335 14750 0.0183 1.6484375 1.436984382296477e-05
67 65 5.914826498422713 15000 0.0177 1.421875 1.3721268095262805e-05
68 66 0.18538308143615723 0.2916666666666667 0.9420310554561889 0.43478260869565216 36.3402 0.66 0.165 6.0 15216
69 67 6.013406940063091 15250 0.0168 1.84375 1.3072692367560838e-05
70 68 6.11198738170347 15500 0.015 1.703125 1.242411663985887e-05
71 69 6.210567823343848 15750 0.0155 2.484375 1.1775540912156905e-05
72 70 6.309148264984227 16000 0.0154 1.3125 1.1126965184454937e-05
73 71 6.407728706624606 16250 0.0157 1.515625 1.0478389456752972e-05
74 72 6.506309148264984 16500 0.0149 2.71875 9.829813729051005e-06
75 73 6.604889589905363 16750 0.0147 2.328125 9.181238001349037e-06
76 74 6.703470031545741 17000 0.0157 1.8671875 8.532662273647072e-06
77 75 6.80205047318612 17250 0.0163 2.171875 7.884086545945104e-06
78 76 6.900630914826499 17500 0.016 2.421875 7.235510818243138e-06
79 77 6.999211356466877 17750 0.0146 2.375 6.5869350905411715e-06
80 78 0.19330303370952606 0.2916666666666667 0.9420310554561889 0.4583333333333333 41.7416 0.575 0.144 7.0 17752
81 79 7.097791798107256 18000 0.0145 2.421875 5.938359362839206e-06
82 80 7.196372239747634 18250 0.0146 1.5 5.289783635137239e-06
83 81 7.294952681388013 18500 0.0139 1.0703125 4.641207907435272e-06
84 82 7.393533123028391 18750 0.0135 1.890625 3.992632179733306e-06
85 83 7.492113564668769 19000 0.0143 1.9375 3.344056452031339e-06
86 84 7.590694006309148 19250 0.0142 1.7265625 2.695480724329373e-06
87 85 7.6892744479495265 19500 0.0146 1.59375 2.0469049966274064e-06
88 86 7.787854889589905 19750 0.0141 1.1875 1.39832926892544e-06
89 87 7.886435331230284 20000 0.0143 1.71875 7.497535412234734e-07
90 88 7.985015772870662 20250 0.0144 0.96484375 1.0117781352150678e-07
91 89 0.19907398521900177 0.2916666666666667 0.9420310554561889 0.375 41.0489 0.585 0.146 8.0 20288
92 90 8.0 20288 6288.8277 12.903 3.226 6.467727988290509e+16 0.037849801997962625

91
training-logs.json Normal file
View File

@@ -0,0 +1,91 @@
{"eval_loss":2.0461120605,"eval_binary":0.0,"eval_rouge":0.5833271075,"eval_llm_as_a_judge":0.0,"eval_runtime":28.9848,"eval_samples_per_second":0.828,"eval_steps_per_second":0.207,"epoch":0.0,"step":0,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.0985804416,"step":250,"loss":0.5483,"grad_norm":12.5625,"learning_rate":0.000012266,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.1971608833,"step":500,"loss":0.1428,"grad_norm":6.03125,"learning_rate":0.0000245813,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.2957413249,"step":750,"loss":0.1048,"grad_norm":4.5625,"learning_rate":0.0000368966,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.3943217666,"step":1000,"loss":0.0944,"grad_norm":3.8125,"learning_rate":0.0000492118,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.4929022082,"step":1250,"loss":0.0833,"grad_norm":2.296875,"learning_rate":0.0000493929,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.5914826498,"step":1500,"loss":0.0741,"grad_norm":3.890625,"learning_rate":0.0000487444,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.6900630915,"step":1750,"loss":0.0674,"grad_norm":2.3125,"learning_rate":0.0000480958,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.7886435331,"step":2000,"loss":0.0653,"grad_norm":2.484375,"learning_rate":0.0000474472,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.8872239748,"step":2250,"loss":0.0605,"grad_norm":2.3125,"learning_rate":0.0000467986,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":0.9858044164,"step":2500,"loss":0.056,"grad_norm":1.4609375,"learning_rate":0.0000461501,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":0.1217864677,"eval_binary":0.1666666667,"eval_rouge":0.945433788,"eval_llm_as_a_judge":0.25,"eval_runtime":39.1542,"eval_samples_per_second":0.613,"eval_steps_per_second":0.153,"epoch":1.0,"step":2536,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.084384858,"step":2750,"loss":0.0497,"grad_norm":1.203125,"learning_rate":0.0000455015,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.1829652997,"step":3000,"loss":0.0479,"grad_norm":2.46875,"learning_rate":0.0000448529,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.2815457413,"step":3250,"loss":0.0467,"grad_norm":1.671875,"learning_rate":0.0000442043,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.380126183,"step":3500,"loss":0.0462,"grad_norm":1.8515625,"learning_rate":0.0000435558,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.4787066246,"step":3750,"loss":0.0463,"grad_norm":1.671875,"learning_rate":0.0000429072,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.5772870662,"step":4000,"loss":0.0427,"grad_norm":1.1484375,"learning_rate":0.0000422586,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.6758675079,"step":4250,"loss":0.0462,"grad_norm":2.03125,"learning_rate":0.00004161,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.7744479495,"step":4500,"loss":0.0411,"grad_norm":1.0859375,"learning_rate":0.0000409614,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.8730283912,"step":4750,"loss":0.0422,"grad_norm":1.5234375,"learning_rate":0.0000403129,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":1.9716088328,"step":5000,"loss":0.0405,"grad_norm":2.0,"learning_rate":0.0000396643,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":0.1257639825,"eval_binary":0.1666666667,"eval_rouge":0.9411790558,"eval_llm_as_a_judge":0.25,"eval_runtime":40.3742,"eval_samples_per_second":0.594,"eval_steps_per_second":0.149,"epoch":2.0,"step":5072,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.0701892744,"step":5250,"loss":0.036,"grad_norm":1.1953125,"learning_rate":0.0000390157,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.1687697161,"step":5500,"loss":0.0334,"grad_norm":0.91796875,"learning_rate":0.0000383671,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.2673501577,"step":5750,"loss":0.035,"grad_norm":1.3046875,"learning_rate":0.0000377186,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.3659305994,"step":6000,"loss":0.0337,"grad_norm":1.4609375,"learning_rate":0.00003707,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.464511041,"step":6250,"loss":0.0334,"grad_norm":1.5390625,"learning_rate":0.0000364214,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.5630914826,"step":6500,"loss":0.0322,"grad_norm":1.546875,"learning_rate":0.0000357728,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.6616719243,"step":6750,"loss":0.0344,"grad_norm":2.109375,"learning_rate":0.0000351243,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.7602523659,"step":7000,"loss":0.0337,"grad_norm":1.9609375,"learning_rate":0.0000344757,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.8588328076,"step":7250,"loss":0.0321,"grad_norm":2.359375,"learning_rate":0.0000338271,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":2.9574132492,"step":7500,"loss":0.0322,"grad_norm":1.078125,"learning_rate":0.0000331785,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":0.1328321695,"eval_binary":0.25,"eval_rouge":0.9450974689,"eval_llm_as_a_judge":0.25,"eval_runtime":37.2465,"eval_samples_per_second":0.644,"eval_steps_per_second":0.161,"epoch":3.0,"step":7608,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.0559936909,"step":7750,"loss":0.0287,"grad_norm":0.9765625,"learning_rate":0.00003253,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.1545741325,"step":8000,"loss":0.0265,"grad_norm":1.7734375,"learning_rate":0.0000318814,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.2531545741,"step":8250,"loss":0.0264,"grad_norm":1.0078125,"learning_rate":0.0000312328,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.3517350158,"step":8500,"loss":0.028,"grad_norm":1.2265625,"learning_rate":0.0000305842,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.4503154574,"step":8750,"loss":0.0281,"grad_norm":1.390625,"learning_rate":0.0000299357,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.5488958991,"step":9000,"loss":0.0276,"grad_norm":0.90625,"learning_rate":0.0000292871,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.6474763407,"step":9250,"loss":0.0258,"grad_norm":2.0625,"learning_rate":0.0000286385,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.7460567823,"step":9500,"loss":0.0261,"grad_norm":0.921875,"learning_rate":0.0000279899,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.844637224,"step":9750,"loss":0.0263,"grad_norm":0.61328125,"learning_rate":0.0000273414,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":3.9432176656,"step":10000,"loss":0.027,"grad_norm":1.125,"learning_rate":0.0000266928,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":0.1492750794,"eval_binary":0.25,"eval_rouge":0.9437395032,"eval_llm_as_a_judge":0.25,"eval_runtime":36.3204,"eval_samples_per_second":0.661,"eval_steps_per_second":0.165,"epoch":4.0,"step":10144,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.0417981073,"step":10250,"loss":0.0244,"grad_norm":1.5625,"learning_rate":0.0000260442,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.1403785489,"step":10500,"loss":0.0217,"grad_norm":1.2578125,"learning_rate":0.0000253956,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.2389589905,"step":10750,"loss":0.0226,"grad_norm":1.9765625,"learning_rate":0.0000247471,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.3375394322,"step":11000,"loss":0.0215,"grad_norm":2.015625,"learning_rate":0.0000240985,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.4361198738,"step":11250,"loss":0.023,"grad_norm":2.015625,"learning_rate":0.0000234499,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.5347003155,"step":11500,"loss":0.0225,"grad_norm":2.34375,"learning_rate":0.0000228013,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.6332807571,"step":11750,"loss":0.021,"grad_norm":1.5703125,"learning_rate":0.0000221528,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.7318611987,"step":12000,"loss":0.022,"grad_norm":1.28125,"learning_rate":0.0000215042,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.8304416404,"step":12250,"loss":0.021,"grad_norm":0.91015625,"learning_rate":0.0000208556,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":4.929022082,"step":12500,"loss":0.0212,"grad_norm":1.84375,"learning_rate":0.000020207,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":0.161930427,"eval_binary":0.2916666667,"eval_rouge":0.9449687758,"eval_llm_as_a_judge":0.3333333333,"eval_runtime":35.8498,"eval_samples_per_second":0.669,"eval_steps_per_second":0.167,"epoch":5.0,"step":12680,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.0276025237,"step":12750,"loss":0.0208,"grad_norm":1.390625,"learning_rate":0.0000195584,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.1261829653,"step":13000,"loss":0.0177,"grad_norm":1.703125,"learning_rate":0.0000189099,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.2247634069,"step":13250,"loss":0.0172,"grad_norm":1.6953125,"learning_rate":0.0000182613,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.3233438486,"step":13500,"loss":0.0185,"grad_norm":1.4375,"learning_rate":0.0000176127,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.4219242902,"step":13750,"loss":0.018,"grad_norm":1.1796875,"learning_rate":0.0000169641,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.5205047319,"step":14000,"loss":0.0181,"grad_norm":2.046875,"learning_rate":0.0000163156,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.6190851735,"step":14250,"loss":0.0174,"grad_norm":3.59375,"learning_rate":0.000015667,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.7176656151,"step":14500,"loss":0.0189,"grad_norm":2.96875,"learning_rate":0.0000150184,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.8162460568,"step":14750,"loss":0.0183,"grad_norm":1.6484375,"learning_rate":0.0000143698,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":5.9148264984,"step":15000,"loss":0.0177,"grad_norm":1.421875,"learning_rate":0.0000137213,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":0.1853830814,"eval_binary":0.2916666667,"eval_rouge":0.9420310555,"eval_llm_as_a_judge":0.4347826087,"eval_runtime":36.3402,"eval_samples_per_second":0.66,"eval_steps_per_second":0.165,"epoch":6.0,"step":15216,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.0134069401,"step":15250,"loss":0.0168,"grad_norm":1.84375,"learning_rate":0.0000130727,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.1119873817,"step":15500,"loss":0.015,"grad_norm":1.703125,"learning_rate":0.0000124241,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.2105678233,"step":15750,"loss":0.0155,"grad_norm":2.484375,"learning_rate":0.0000117755,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.309148265,"step":16000,"loss":0.0154,"grad_norm":1.3125,"learning_rate":0.000011127,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.4077287066,"step":16250,"loss":0.0157,"grad_norm":1.515625,"learning_rate":0.0000104784,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.5063091483,"step":16500,"loss":0.0149,"grad_norm":2.71875,"learning_rate":0.0000098298,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.6048895899,"step":16750,"loss":0.0147,"grad_norm":2.328125,"learning_rate":0.0000091812,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.7034700315,"step":17000,"loss":0.0157,"grad_norm":1.8671875,"learning_rate":0.0000085327,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.8020504732,"step":17250,"loss":0.0163,"grad_norm":2.171875,"learning_rate":0.0000078841,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.9006309148,"step":17500,"loss":0.016,"grad_norm":2.421875,"learning_rate":0.0000072355,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":6.9992113565,"step":17750,"loss":0.0146,"grad_norm":2.375,"learning_rate":0.0000065869,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":0.1933030337,"eval_binary":0.2916666667,"eval_rouge":0.9420310555,"eval_llm_as_a_judge":0.4583333333,"eval_runtime":41.7416,"eval_samples_per_second":0.575,"eval_steps_per_second":0.144,"epoch":7.0,"step":17752,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.0977917981,"step":18000,"loss":0.0145,"grad_norm":2.421875,"learning_rate":0.0000059384,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.1963722397,"step":18250,"loss":0.0146,"grad_norm":1.5,"learning_rate":0.0000052898,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.2949526814,"step":18500,"loss":0.0139,"grad_norm":1.0703125,"learning_rate":0.0000046412,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.393533123,"step":18750,"loss":0.0135,"grad_norm":1.890625,"learning_rate":0.0000039926,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.4921135647,"step":19000,"loss":0.0143,"grad_norm":1.9375,"learning_rate":0.0000033441,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.5906940063,"step":19250,"loss":0.0142,"grad_norm":1.7265625,"learning_rate":0.0000026955,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.6892744479,"step":19500,"loss":0.0146,"grad_norm":1.59375,"learning_rate":0.0000020469,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.7878548896,"step":19750,"loss":0.0141,"grad_norm":1.1875,"learning_rate":0.0000013983,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.8864353312,"step":20000,"loss":0.0143,"grad_norm":1.71875,"learning_rate":0.0000007498,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":7.9850157729,"step":20250,"loss":0.0144,"grad_norm":0.96484375,"learning_rate":0.0000001012,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":0.1990739852,"eval_binary":0.2916666667,"eval_rouge":0.9420310555,"eval_llm_as_a_judge":0.375,"eval_runtime":41.0489,"eval_samples_per_second":0.585,"eval_steps_per_second":0.146,"epoch":8.0,"step":20288,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":null,"train_samples_per_second":null,"train_steps_per_second":null,"total_flos":null,"train_loss":null}
{"eval_loss":null,"eval_binary":null,"eval_rouge":null,"eval_llm_as_a_judge":null,"eval_runtime":null,"eval_samples_per_second":null,"eval_steps_per_second":null,"epoch":8.0,"step":20288,"loss":null,"grad_norm":null,"learning_rate":null,"train_runtime":6288.8277,"train_samples_per_second":12.903,"train_steps_per_second":3.226,"total_flos":6.467727988e+16,"train_loss":0.037849802}