sascha-frank-ai-research/tsft-rag-qwen2.5-7b-instruct
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

初始化项目，由ModelHub XC社区提供模型

Browse Source 
Model: sascha-frank-ai-research/tsft-rag-qwen2.5-7b-instruct
Source: Original Platform
This commit is contained in:
ModelHub XC
2026-07-27 19:23:11 +08:00
commit 347372d0d9
10 changed files with 744 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

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

202
LICENSE Normal file
View File

@@ -0,0 +1,202 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
the copyright owner that is granting the License.
"Legal Entity" shall mean the union of the acting entity and all
other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
Object form, made available under the License, as indicated by a
copyright notice that is included in or attached to the work
(an example is provided in the Appendix below).
"Derivative Works" shall mean any work, whether in Source or Object
form, that is based on (or derived from) the Work and for which the
editorial revisions, annotations, elaborations, or other modifications
represent, as a whole, an original work of authorship. For the purposes
of this License, Derivative Works shall not include works that remain
separable from, or merely link (or bind by name) to the interfaces of,
the Work and Derivative Works thereof.
"Contribution" shall mean any work of authorship, including
the original version of the Work and any modifications or additions
to that Work or Derivative Works thereof, that is intentionally
submitted to Licensor for inclusion in the Work by the copyright owner
or by an individual or Legal Entity authorized to submit on behalf of
the copyright owner. For the purposes of this definition, "submitted"
means any form of electronic, verbal, or written communication sent
to the Licensor or its representatives, including but not limited to
communication on electronic mailing lists, source code control systems,
and issue tracking systems that are managed by, or on behalf of, the
Licensor for the purpose of discussing and improving the Work, but
excluding communication that is conspicuously marked or otherwise
designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
subsequently incorporated within the Work.
2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
copyright license to reproduce, prepare Derivative Works of,
publicly display, publicly perform, sublicense, and distribute the
Work and such Derivative Works in Source or Object form.
3. Grant of Patent License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
by such Contributor that are necessarily infringed by their
Contribution(s) alone or by combination of their Contribution(s)
with the Work to which such Contribution(s) was submitted. If You
institute patent litigation against any entity (including a
cross-claim or counterclaim in a lawsuit) alleging that the Work
or a Contribution incorporated within the Work constitutes direct
or contributory patent infringement, then any patent licenses
granted to You under this License for that Work shall terminate
as of the date such litigation is filed.
4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
include a readable copy of the attribution notices contained
within such NOTICE file, excluding those notices that do not
pertain to any part of the Derivative Works, in at least one
of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
documentation, if provided along with the Derivative Works; or,
within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
as modifying the License.
You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, 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 or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
and charge a fee for, acceptance of support, warranty, indemnity,
or other liability obligations and/or rights consistent with this
License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
defend, and hold each Contributor harmless for any liability
incurred by, or claims asserted against, such Contributor by reason
of your accepting any such warranty or additional liability.
END OF TERMS AND CONDITIONS
APPENDIX: How to apply the Apache License to your work.
To apply the Apache License to your work, attach the following
boilerplate notice, with the fields enclosed by brackets "[]"
replaced with your own identifying information. (Don't include
the brackets!) The text should be enclosed in the appropriate
comment syntax for the file format. We also recommend that a
file or class name and description of purpose be included on the
same "printed page" as the copyright notice for easier
identification within third-party archives.
Copyright 2024 Alibaba Cloud
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

346
README.md Normal file
View File

@@ -0,0 +1,346 @@
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model: Qwen/Qwen2.5-7B-Instruct
base_model_relation: finetune
language:
- de
- en
tags:
- rag
- retrieval-augmented-generation
- task-specific-fine-tuning
- full-fine-tuning
- qwen2.5
- text-generation
- structured-output
- abstention
- research
- tsft-rag
- model-card-v1
---
# TSFT-RAG Qwen2.5-7B-Instruct
**Task-Specific Full Fine-Tuning for Retrieval-Augmented Generation**
TSFT-RAG Qwen2.5-7B-Instruct is a full-parameter fine-tuned derivative of
[Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
Part of the [TSFT-RAG model family](https://huggingface.co/sascha-frank-ai-research) for Retrieval-Augmented Generation across Gemma, Qwen and Llama architectures.
The objective is to investigate how supervised full fine-tuning changes the behaviour of modern language models when used as Retrieval-Augmented Generation (RAG) systems.
---
# TSFT-RAG Model Series
| Model | Parameters | Status |
|--------|-----------:|--------|
| [Qwen2.5-0.5B-Instruct](https://huggingface.co/sascha-frank-ai-research/tsft-rag-qwen2.5-0.5b-instruct) | 0.5B | Released |
| [Qwen2.5-1.5B-Instruct](https://huggingface.co/sascha-frank-ai-research/tsft-rag-qwen2.5-1.5b-instruct) | 1.5B | Released |
| **Qwen2.5-7B-Instruct** | **7B** | **Released** |
| [Qwen2.5-14B-Instruct](https://huggingface.co/sascha-frank-ai-research/tsft-rag-qwen2.5-14b-instruct) | 14B | Released |
---
The model was trained to improve several behaviours that are important in practical
RAG systems:
- answering from supplied context;
- abstaining when the context does not support an answer;
- following task-specific output requirements;
- producing structured JSON output;
- generating source-aware and citation-oriented responses.
The strongest observed gains are in hard-negative handling, structured-output validity,
and citation-related behaviour. Performance does not improve uniformly across all
metrics; grounded-QA and some analysis metrics show small declines in the reported
evaluation.
## Model Details
| Property | Value |
|---|---|
| Model family | TSFT-RAG |
| Base model | Qwen/Qwen2.5-7B-Instruct |
| Architecture | Qwen2ForCausalLM |
| Parameters | approximately 7B |
| Training method | full-parameter supervised fine-tuning |
| Primary language of the training corpus | German |
| Context length supported by the configuration | 32,768 tokens |
| Training sequence length | 1,024 tokens |
| Weight dtype | bfloat16 |
| Framework | Transformers |
| License | Apache-2.0 |
## Intended Use
This model is intended for research and experimental use in RAG-oriented workflows.
Suitable use cases include:
- context-grounded question answering;
- evaluation of abstention behaviour;
- generation of structured RAG responses;
- source-aware answer generation;
- comparison of base and fully fine-tuned language models;
- local or server-based RAG experiments.
The model should be used with retrieved evidence or other explicitly supplied context.
It is not intended to serve as a standalone factual knowledge source.
## Training Data
The model was trained on a fixed, task-specific German-language corpus designed for
RAG-oriented instruction following.
The training examples cover:
- answerable context-grounded questions;
- hard-negative and unanswerable questions;
- multiple assistant roles and response styles;
- analysis tasks for main topic, key message, and main arguments;
- JSON and short-answer output formats;
- citation-aware responses.
The complete raw training corpus is not distributed with this checkpoint. The
accompanying repository documents the data-processing, training, and evaluation
pipeline.
## Training Procedure
This checkpoint was produced using full-parameter supervised fine-tuning rather than
LoRA, QLoRA, or another adapter-based method.
| Training argument | Value |
|---|---:|
| Epochs | 3 |
| Learning rate | 1e-6 |
| Per-device train batch size | 4 |
| Per-device evaluation batch size | 4 |
| Gradient accumulation steps | 4 |
| Effective batch size per process | 16 |
| Optimizer | AdamW (PyTorch) |
| Scheduler | cosine |
| Warmup ratio | 0.08 |
| Weight decay | 0.01 |
| Maximum gradient norm | 1.0 |
| Precision | bfloat16 |
| FP16 | disabled |
| Maximum sequence length | 1,024 |
| Packing | disabled |
| Seed | 42 |
The recorded training runtime was approximately **5,656 seconds**
(about **1 h 34 min**), with a final reported training loss of
**0.8019**.
## Evaluation
The fine-tuned model and the unchanged base model were evaluated on the same held-out
test set of **1,940 instances**.
| Metric | Base model | TSFT-RAG | Difference |
|---|---:|---:|---:|
| Aggregate score | 0.4016 | 0.5617 | +0.1601 |
| Hard-negative handling | 0.0791 | 0.5523 | +0.4732 |
| Grounded QA | 0.5505 | 0.5256 | -0.0249 |
| Analysis: main topic | 0.6158 | 0.6241 | +0.0083 |
| Analysis: key message | 0.6122 | 0.5726 | -0.0396 |
| Analysis: main arguments | 0.5823 | 0.5584 | -0.0239 |
| JSON validity | 0.5739 | 0.9069 | +0.3330 |
| Citation precision | 0.0000 | 0.8300 | +0.8300 |
| Citation recall | 0.0000 | 0.8300 | +0.8300 |
The largest improvement is in hard-negative handling. The unchanged base model
correctly rejected **56 of 708** hard-negative cases, while the fine-tuned model
correctly rejected **391 of 708**.
The aggregate score, hard-negative handling, JSON validity, citation precision, and
citation recall improve substantially. Grounded QA, key-message analysis, and
main-argument analysis show modest declines, while main-topic analysis improves
slightly.
These results should be interpreted as task-specific benchmark outcomes rather than
general-purpose capability claims.
Further evaluation details, scripts, and analysis are available in the accompanying
repository and paper.
## Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "sascha-frank-ai-research/tsft-rag-qwen2.5-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{
"role": "system",
"content": (
"Du bist ein sachlicher RAG-Assistent. "
"Beantworte die Frage präzise nur auf Grundlage des Kontexts. "
"Wenn der Kontext die Antwort nicht belegt, sage dies ausdrücklich."
),
},
{
"role": "user",
"content": (
"Kontext:\n"
"Ein Retrieval-Modul liefert relevante Dokumentpassagen an das Sprachmodell.\n\n"
"Frage:\n"
"Welche Komponente stellt dem Sprachmodell die Dokumentpassagen bereit?"
),
},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
with torch.inference_mode():
output = model.generate(
**inputs,
max_new_tokens=256,
do_sample=False,
)
generated_tokens = output[0, inputs["input_ids"].shape[1]:]
print(
tokenizer.decode(
generated_tokens,
skip_special_tokens=True,
)
)
```
## Prompting Notes
The model was trained with Qwen's chat template and should be used through
`tokenizer.apply_chat_template(...)`.
For RAG use, prompts should clearly separate:
1. the behavioural instruction;
2. the retrieved context;
3. the user question;
4. any required output format.
For deterministic evaluation, use greedy decoding or another fixed decoding setup.
## Limitations
- The model may produce incorrect, incomplete, or unsupported answers.
- Fine-tuning does not guarantee faithful use of retrieved evidence.
- Retrieval quality remains a major determinant of answer quality.
- The model may fail to abstain even when evidence is insufficient.
- Citation-like output does not guarantee that a cited passage truly supports a claim.
- Performance outside the evaluated RAG task formats has not been systematically established.
- The training corpus is primarily German, so behaviour may differ in other languages.
- The model inherits limitations and biases from the Qwen2.5 base model and from the
task-specific training data.
- The observed metric trade-offs should be considered when selecting the model for deployment.
The model should not be used for high-stakes medical, legal, financial, safety-critical,
or administrative decisions without independent validation and human oversight.
## Ethical Considerations
Retrieved documents may contain inaccurate, biased, confidential, or outdated
information. Such content can be reproduced or amplified by the model.
Users are responsible for:
- validating retrieval sources;
- protecting confidential material;
- testing model behaviour in the intended domain;
- monitoring unsupported or misleading output;
- complying with applicable law, policy, and licensing requirements.
## Relationship to the Research Project
This checkpoint is one model from a controlled cross-family study of task-specific
full fine-tuning for RAG. The study compares base and fine-tuned configurations from
the Gemma, Llama, and Qwen model families across parameter scales from approximately
0.5B to 14B.
Code, training scripts, evaluation scripts, and documentation:
https://github.com/frankmst/rag-task-specific-full-finetuning
## Base Model and License
This model is derived from
[Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct),
which is distributed under the Apache License 2.0.
This derivative checkpoint is also released under the Apache License 2.0. Users
should review the license file included in this repository and the licensing
information of the base model.
## Citation
A formal paper citation will be added after publication of the accompanying paper.
Until then, please cite the software repository:
```bibtex
@software{frank_tsft_rag_2026,
author = {Sascha Frank},
title = {Task-Specific Full Fine-Tuning for Retrieval-Augmented Generation},
year = {2026},
url = {https://github.com/frankmst/rag-task-specific-full-finetuning}
}
```
## Project
The complete TSFT-RAG research project, including training scripts,
evaluation pipeline, benchmark methodology and documentation, is available at:
**Project repository**
https://github.com/frankmst/rag-task-specific-full-finetuning
**TSFT-RAG model family**
https://huggingface.co/sascha-frank-ai-research
---
## Author
**Sascha Frank**
Independent AI Researcher
**ORCID**
https://orcid.org/0000-0002-0588-0081
**GitHub**
https://github.com/frankmst
**Hugging Face**
https://huggingface.co/sascha-frank-ai-research
## Links
- Project repository: https://github.com/frankmst/rag-task-specific-full-finetuning
- Base model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct
- Project website: https://sascha-frank.com
- Paper: forthcoming

54
chat_template.jinja Normal file
View File

@@ -0,0 +1,54 @@
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

62
config.json Normal file
View File

@@ -0,0 +1,62 @@
{
"architectures": [
"Qwen2ForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": null,
"dtype": "bfloat16",
"eos_token_id": 151645,
"hidden_act": "silu",
"hidden_size": 3584,
"initializer_range": 0.02,
"intermediate_size": 18944,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": 151654,
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000.0,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.12.1",
"unsloth_fixed": true,
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 152064
}

14
generation_config.json Normal file
View File

@@ -0,0 +1,14 @@
{
"do_sample": true,
"eos_token_id": [
151645,
151643
],
"max_length": 32768,
"pad_token_id": 151654,
"repetition_penalty": 1.05,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"transformers_version": "5.12.1"
}

3
model.safetensors Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:b1f5c572165efd1fcba6d7c34980108f61faf934b0b212bb45fb7ae9b43c1213
size 15231272152

3
tokenizer.json Normal file
View File

@@ -0,0 +1,3 @@
version https://git-lfs.github.com/spec/v1
oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
size 11421892

16
tokenizer_config.json Normal file
View File

@@ -0,0 +1,16 @@
{
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": null,
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"is_local": false,
"local_files_only": false,
"model_max_length": 32768,
"pad_token": "<|vision_pad|>",
"padding_side": "right",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

8
train_metrics.json Normal file
View File

@@ -0,0 +1,8 @@
{
"train_runtime": 5656.4446,
"train_samples_per_second": 8.233,
"train_steps_per_second": 0.515,
"total_flos": 5.3836610453744026e+17,
"train_loss": 0.8018893963112849,
"epoch": 3.0
}