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Model: ngxson/Vistral-7B-ChatML Source: Original Platform
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README.md
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---
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language:
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- vi
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library_name: transformers
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tags:
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- LLMs
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- NLP
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- Vietnamese
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license: mit
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---
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## Model Description
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This model is finetuned from [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat). The dataset is taken from [bkai-foundation-models/vi-self-chat-sharegpt-format](https://huggingface.co/datasets/bkai-foundation-models/vi-self-chat-sharegpt-format)
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This is a **low rank** finetune to add support for chatml template. While the template does not affect model performance, it would be nice to support chatml since most of models based on Mistral already using it.
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The format looks like this:
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```
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<|im_start|>system
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Provide some context and/or instructions to the model.
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<|im_end|>
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<|im_start|>user
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The user’s message goes here
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<|im_end|>
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<|im_start|>assistant
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```
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## How to use it
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The recommended way is to use the GGUF `vistral-7b-chatml-Q4_K_M.gguf` file included in this repository. Run it via llama.cpp (remember to pass `-cml` argument to use chatml template)
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```bash
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./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
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```
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Additionally, you can run the `python3 run.py` inside this repository to try the model using transformers library. This it not the recommended way since you may need to change some params inside in order to make it work.
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This is an example of a conversation using llama.cpp:
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- <|im_start|>system
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- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
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- <|im_start|>user
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- > xin chào
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- Tôi là một trợ lý AI được tạo ra để hỗ trợ con người. Bạn có câu hỏi gì về chủ đề nào đó?<|im_end|>
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- > trợ lý AI là gì? bạn giải thích được không?
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- Trợ lý AI là một chương trình máy tính sử dụng trí tuệ nhân tạo và học máy để hỗ trợ con người trong nhiều công việc khác nhau. Nó có thể hiểu và xử lý ngôn ngữ tự nhiên, nhận biết hình ảnh và âm thanh cũng như đưa ra các đề xuất hay câu trả lời phù hợp cho các yêu cầu của bạn.<|im_end|>
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- > ồ, cảm ơn, vậy bạn có thể làm gì?
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- Tôi được thiết kế để giúp đỡ con người trong nhiều công việc như giải đáp các câu hỏi, trả lời những vấn đề liên quan đến cuộc sống hàng ngày của họ. Tôi cũng hỗ trợ nghiên cứu và phân tích dữ liệu để đưa ra dự đoán hay tư vấn cho doanh nghiệp hoặc cá nhân.<|im_end|>
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- > tức là sao?
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- Ví dụ, nếu bạn đang thắc mắc về một chủ đề nào đó và không biết phải bắt đầu tìm kiếm ở đâu hay làm thế nào để hiểu rõ hơn về nó thì tôi có thể giúp bạn. Tôi cũng có khả năng đọc và phân tích dữ liệu từ nhiều nguồn khác nhau như các tài liệu nghiên cứu khoa học hoặc thông tin trên mạng xã hội, sau đó đưa ra những giải thích chi tiết cho vấn đề của bạn.<|im_end|>
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## Training
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You can also look at the training code in the `finetune.py` file.
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For tokenizer, I changed these things:
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- Token `[INST]` to `<|im_start|>`, make it become special token
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- Token `[/INST]` to `<|im_end|>`, make it become special token
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- Change to `eos_token` to `<|im_end|>`
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- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
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Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
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## More information
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Disclaimer: I'm not expert in machine learning, my background is from cybersecurity so the making of this model is a "hobby" to me. Training is done using a VPS on Google Cloud, I paid with my own money.
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If you want to discuss, feel free to contact me at `contact at ngxson dot com` - [ngxson.com](https://ngxson.com)
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added_tokens.json
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{
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"<</SYS>>": 38366,
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"<<SYS>>": 38365,
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"<|im_start|>": 38367,
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"<|im_end|>": 38368
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}
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checkpoint-300/README.md
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checkpoint-300/README.md
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---
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library_name: peft
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base_model: Viet-Mistral/Vistral-7B-Chat
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---
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||||
|
||||
# Model Card for Model ID
|
||||
|
||||
<!-- Provide a quick summary of what the model is/does. -->
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||||
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||||
|
||||
|
||||
## Model Details
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||||
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||||
### Model Description
|
||||
|
||||
<!-- Provide a longer summary of what this model is. -->
|
||||
|
||||
|
||||
|
||||
- **Developed by:** [More Information Needed]
|
||||
- **Funded by [optional]:** [More Information Needed]
|
||||
- **Shared by [optional]:** [More Information Needed]
|
||||
- **Model type:** [More Information Needed]
|
||||
- **Language(s) (NLP):** [More Information Needed]
|
||||
- **License:** [More Information Needed]
|
||||
- **Finetuned from model [optional]:** [More Information Needed]
|
||||
|
||||
### Model Sources [optional]
|
||||
|
||||
<!-- Provide the basic links for the model. -->
|
||||
|
||||
- **Repository:** [More Information Needed]
|
||||
- **Paper [optional]:** [More Information Needed]
|
||||
- **Demo [optional]:** [More Information Needed]
|
||||
|
||||
## Uses
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||||
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||||
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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||||
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### Direct Use
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||||
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||||
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Downstream Use [optional]
|
||||
|
||||
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Out-of-Scope Use
|
||||
|
||||
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Bias, Risks, and Limitations
|
||||
|
||||
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Recommendations
|
||||
|
||||
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
||||
|
||||
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
||||
|
||||
## How to Get Started with the Model
|
||||
|
||||
Use the code below to get started with the model.
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Training Details
|
||||
|
||||
### Training Data
|
||||
|
||||
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Training Procedure
|
||||
|
||||
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
||||
|
||||
#### Preprocessing [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
|
||||
#### Training Hyperparameters
|
||||
|
||||
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
||||
|
||||
#### Speeds, Sizes, Times [optional]
|
||||
|
||||
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Evaluation
|
||||
|
||||
<!-- This section describes the evaluation protocols and provides the results. -->
|
||||
|
||||
### Testing Data, Factors & Metrics
|
||||
|
||||
#### Testing Data
|
||||
|
||||
<!-- This should link to a Dataset Card if possible. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Factors
|
||||
|
||||
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Metrics
|
||||
|
||||
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Results
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Summary
|
||||
|
||||
|
||||
|
||||
## Model Examination [optional]
|
||||
|
||||
<!-- Relevant interpretability work for the model goes here -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Environmental Impact
|
||||
|
||||
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
||||
|
||||
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
||||
|
||||
- **Hardware Type:** [More Information Needed]
|
||||
- **Hours used:** [More Information Needed]
|
||||
- **Cloud Provider:** [More Information Needed]
|
||||
- **Compute Region:** [More Information Needed]
|
||||
- **Carbon Emitted:** [More Information Needed]
|
||||
|
||||
## Technical Specifications [optional]
|
||||
|
||||
### Model Architecture and Objective
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Compute Infrastructure
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Hardware
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Software
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Citation [optional]
|
||||
|
||||
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
||||
|
||||
**BibTeX:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
**APA:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Glossary [optional]
|
||||
|
||||
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## More Information [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Model Card Authors [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Model Card Contact
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
|
||||
### Framework versions
|
||||
|
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- PEFT 0.7.2.dev0
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checkpoint-300/adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "Viet-Mistral/Vistral-7B-Chat",
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"bias": "none",
|
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 8,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
|
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"up_proj",
|
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"q_proj",
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"k_proj",
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"lm_head",
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"down_proj",
|
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"o_proj",
|
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"v_proj",
|
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"gate_proj"
|
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],
|
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"task_type": "CAUSAL_LM",
|
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"use_rslora": false
|
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}
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checkpoint-300/adapter_model.safetensors
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checkpoint-300/adapter_model.safetensors
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size 713943216
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checkpoint-300/optimizer.pt
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checkpoint-300/optimizer.pt
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version https://git-lfs.github.com/spec/v1
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checkpoint-300/rng_state.pth
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checkpoint-300/rng_state.pth
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version https://git-lfs.github.com/spec/v1
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size 14244
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checkpoint-300/scheduler.pt
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checkpoint-300/scheduler.pt
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version https://git-lfs.github.com/spec/v1
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size 1064
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checkpoint-300/trainer_state.json
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checkpoint-300/trainer_state.json
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checkpoint-300/training_args.bin
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checkpoint-300/training_args.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:c3501b609227a6d61fef0435402a30ee750e1e3f7c04acadf47d4128368c928f
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size 4728
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config.json
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config.json
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{
|
||||
"_name_or_path": "Viet-Mistral/Vistral-7B-Chat",
|
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"architectures": [
|
||||
"MistralForCausalLM"
|
||||
],
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
|
||||
"sliding_window": 4096,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "float16",
|
||||
"transformers_version": "4.38.0.dev0",
|
||||
"use_cache": true,
|
||||
"vocab_size": 38369
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
133
finetune.py
Normal file
133
finetune.py
Normal file
@@ -0,0 +1,133 @@
|
||||
import torch
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging, TextStreamer
|
||||
from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model
|
||||
import os, torch, wandb, platform, warnings
|
||||
from datasets import load_dataset
|
||||
from trl import SFTTrainer
|
||||
|
||||
hf_token = ''
|
||||
wnb_token = ''
|
||||
wnb_name = 'vistral-chatml'
|
||||
MODEL = 'Viet-Mistral/Vistral-7B-Chat'
|
||||
resume_from_checkpoint = False
|
||||
output_dir = 'vistral-chatml'
|
||||
tokenizer_path = '.'
|
||||
|
||||
#######################################################
|
||||
## DATASET
|
||||
|
||||
|
||||
from datasets import load_dataset
|
||||
|
||||
|
||||
def generate_system_prompt(i):
|
||||
system_prompt = "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực. Hãy luôn trả lời một cách hữu ích nhất có thể, đồng thời giữ an toàn."
|
||||
if i % 2 == 0:
|
||||
system_prompt += "\nCâu trả lời của bạn không nên chứa bất kỳ nội dung gây hại, phân biệt chủng tộc, phân biệt giới tính, độc hại, nguy hiểm hoặc bất hợp pháp nào. Hãy đảm bảo rằng các câu trả lời của bạn không có thiên kiến xã hội và mang tính tích cực."
|
||||
if i % 5 == 0:
|
||||
system_prompt += "\nNếu một câu hỏi không có ý nghĩa hoặc không hợp lý về mặt thông tin, hãy giải thích tại sao thay vì trả lời một điều gì đó không chính xác. Nếu bạn không biết câu trả lời cho một câu hỏi, hãy trẳ lời là bạn không biết và vui lòng không chia sẻ thông tin sai lệch."
|
||||
return system_prompt
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
|
||||
|
||||
def tokenize_chat(input, i):
|
||||
print(generate_system_prompt(i))
|
||||
conversation = [{'role': 'system', 'content': generate_system_prompt(i)}]
|
||||
for msg in input['conversations']:
|
||||
output = {'role': 'user', 'content': msg['value']}
|
||||
if msg['from'] == 'gpt':
|
||||
output['role'] = 'assistant'
|
||||
conversation.append(output)
|
||||
formatted = tokenizer.apply_chat_template(conversation, tokenize=False)
|
||||
return tokenizer(formatted)
|
||||
|
||||
sharegpt_dataset = load_dataset('bkai-foundation-models/vi-self-chat-sharegpt-format')
|
||||
train_data = sharegpt_dataset['train'].shuffle(seed=42)\
|
||||
.select(range(800))\
|
||||
.map(lambda x, i: tokenize_chat(x, i), remove_columns=["conversations"], with_indices=True)
|
||||
|
||||
|
||||
#######################################################
|
||||
## SETUP
|
||||
|
||||
wandb.login(key=wnb_token)
|
||||
wandb.init(name=wnb_name)
|
||||
# use custom tokenizer instead of one comes from the model
|
||||
#tokenizer = AutoTokenizer.from_pretrained(
|
||||
# MODEL,
|
||||
# add_eos_token=False,
|
||||
# add_bos_token=False,
|
||||
# token=hf_token,
|
||||
#)
|
||||
bnb_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
bnb_4bit_compute_dtype=torch.bfloat16,
|
||||
bnb_4bit_use_double_quant=True,
|
||||
)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
MODEL,
|
||||
device_map="auto",
|
||||
token=hf_token,
|
||||
quantization_config=bnb_config,
|
||||
trust_remote_code=True,
|
||||
)
|
||||
|
||||
|
||||
#######################################################
|
||||
## LORA CONFIG
|
||||
|
||||
model.gradient_checkpointing_enable()
|
||||
model = prepare_model_for_kbit_training(model)
|
||||
peft_config = LoraConfig(
|
||||
r=8,
|
||||
lora_alpha=16,
|
||||
target_modules=[
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
"o_proj",
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
"down_proj",
|
||||
"lm_head",
|
||||
],
|
||||
bias="none",
|
||||
lora_dropout=0.05, # Conventional
|
||||
task_type="CAUSAL_LM",
|
||||
)
|
||||
model = get_peft_model(model, peft_config)
|
||||
model.print_trainable_parameters()
|
||||
|
||||
from accelerate import Accelerator
|
||||
accelerator = Accelerator()
|
||||
model = accelerator.prepare_model(model)
|
||||
|
||||
|
||||
#######################################################
|
||||
## TRAIN
|
||||
|
||||
from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling
|
||||
trainer = Trainer(
|
||||
model=model,
|
||||
train_dataset=train_data,
|
||||
args=TrainingArguments(
|
||||
report_to='wandb',
|
||||
warmup_steps=1,
|
||||
per_device_train_batch_size=1,
|
||||
gradient_accumulation_steps=4,
|
||||
gradient_checkpointing=True,
|
||||
num_train_epochs=4,
|
||||
learning_rate=2.5e-5,
|
||||
logging_steps=1,
|
||||
optim="paged_adamw_8bit",
|
||||
save_strategy="steps",
|
||||
save_steps=10,
|
||||
save_total_limit=4,
|
||||
output_dir=output_dir
|
||||
),
|
||||
data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False)
|
||||
)
|
||||
model.config.use_cache = False
|
||||
|
||||
trainer.train(resume_from_checkpoint=resume_from_checkpoint)
|
||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 1,
|
||||
"eos_token_id": 2,
|
||||
"transformers_version": "4.38.0.dev0",
|
||||
"use_cache": false
|
||||
}
|
||||
3
model-00001-of-00003.safetensors
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3
model-00001-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4995337088
|
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3
model-00002-of-00003.safetensors
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3
model-00002-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4999819232
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3
model-00003-of-00003.safetensors
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3
model-00003-of-00003.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4592691112
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298
model.safetensors.index.json
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298
model.safetensors.index.json
Normal file
@@ -0,0 +1,298 @@
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{
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}
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}
|
||||
64
run.py
Normal file
64
run.py
Normal file
@@ -0,0 +1,64 @@
|
||||
import torch
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging, TextStreamer
|
||||
from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model
|
||||
import os, torch, wandb, platform, warnings
|
||||
from datasets import load_dataset
|
||||
from trl import SFTTrainer
|
||||
|
||||
hf_token = '..........'
|
||||
|
||||
tokenizer = AutoTokenizer.from_pretrained('./vistral-tokenizer')
|
||||
bnb_config = BitsAndBytesConfig(
|
||||
load_in_4bit=True,
|
||||
bnb_4bit_quant_type="nf4",
|
||||
bnb_4bit_compute_dtype=torch.bfloat16,
|
||||
bnb_4bit_use_double_quant=True,
|
||||
)
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
'Viet-Mistral/Vistral-7B-Chat',
|
||||
device_map="auto",
|
||||
token=hf_token,
|
||||
quantization_config=bnb_config,
|
||||
)
|
||||
ft_model = PeftModel.from_pretrained(model, CHECKPOINT_PATH)
|
||||
|
||||
#torch.backends.cuda.enable_mem_efficient_sdp(False)
|
||||
#torch.backends.cuda.enable_flash_sdp(False)
|
||||
|
||||
system_prompt = "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực. Hãy luôn trả lời một cách hữu ích nhất có thể, đồng thời giữ an toàn."
|
||||
|
||||
stop_tokens = [tokenizer.eos_token_id, tokenizer('<|im_end|>')['input_ids'].pop()]
|
||||
|
||||
def chat_test():
|
||||
conversation = [{"role": "system", "content": system_prompt }]
|
||||
while True:
|
||||
human = input("Human: ")
|
||||
if human.lower() == "reset":
|
||||
conversation = [{"role": "system", "content": system_prompt }]
|
||||
print("The chat history has been cleared!")
|
||||
continue
|
||||
|
||||
if human.lower() == "exit":
|
||||
break
|
||||
|
||||
conversation.append({"role": "user", "content": human })
|
||||
formatted = tokenizer.apply_chat_template(conversation, tokenize=False) + "<|im_start|>assistant"
|
||||
tok = tokenizer(formatted, return_tensors="pt").to(ft_model.device)
|
||||
input_ids = tok['input_ids']
|
||||
|
||||
out_ids = ft_model.generate(
|
||||
input_ids=input_ids,
|
||||
attention_mask=tok['attention_mask'],
|
||||
eos_token_id=stop_tokens,
|
||||
max_new_tokens=50,
|
||||
do_sample=True,
|
||||
top_p=0.95,
|
||||
top_k=40,
|
||||
temperature=0.1,
|
||||
repetition_penalty=1.05,
|
||||
)
|
||||
assistant = tokenizer.batch_decode(out_ids[:, input_ids.size(1): ], skip_special_tokens=True)[0].strip()
|
||||
print("Assistant: ", assistant)
|
||||
conversation.append({"role": "assistant", "content": assistant })
|
||||
|
||||
chat_test()
|
||||
6
special_tokens_map.json
Normal file
6
special_tokens_map.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"bos_token": "<s>",
|
||||
"eos_token": "<|im_end|>",
|
||||
"pad_token": "</s>",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
108396
tokenizer.json
Normal file
108396
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:e792a804bbfc19a96b61b87109b8f2b0b7c92830025f285b402ba27c0c309c6f
|
||||
size 596883
|
||||
80
tokenizer_config.json
Normal file
80
tokenizer_config.json
Normal file
@@ -0,0 +1,80 @@
|
||||
{
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"38365": {
|
||||
"content": "<<SYS>>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"38366": {
|
||||
"content": "<</SYS>>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"38367": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"38368": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<unk>",
|
||||
"<s>",
|
||||
"</s>",
|
||||
"<|im_start|>",
|
||||
"<|im_end|>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<unk>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false,
|
||||
"use_fast": true
|
||||
}
|
||||
3
vistral-7b-chatml-Q4_K_M.gguf
Normal file
3
vistral-7b-chatml-Q4_K_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c5b27fb9fc3b2118d24f5476e5881b4000045a640500c464319849d557746e14
|
||||
size 4404661312
|
||||
Reference in New Issue
Block a user