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Model: ngxson/Vistral-7B-ChatML
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---
language:
- vi
library_name: transformers
tags:
- LLMs
- NLP
- Vietnamese
license: mit
---
## Model Description
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)
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.
The format looks like this:
```
<|im_start|>system
Provide some context and/or instructions to the model.
<|im_end|>
<|im_start|>user
The users message goes here
<|im_end|>
<|im_start|>assistant
```
## How to use it
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)
```bash
./main -m vistral-7b-chatml-Q4_K_M.gguf -p "Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực." -cml
```
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.
This is an example of a conversation using llama.cpp:
- <|im_start|>system
- Bạn là một trợ lí Tiếng Việt nhiệt tình và trung thực.<|im_end|>
- <|im_start|>user
- > xin chào
- 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|>
- > trợ lý AI là gì? bạn giải thích được không?
- 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|>
- > ồ, cảm ơn, vậy bạn có thể làm gì?
- 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|>
- > tức là sao?
- 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|>
## Training
You can also look at the training code in the `finetune.py` file.
For tokenizer, I changed these things:
- Token `[INST]` to `<|im_start|>`, make it become special token
- Token `[/INST]` to `<|im_end|>`, make it become special token
- Change to `eos_token` to `<|im_end|>`
- Update `chat_template` to chatml, taken from [this example](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/blob/main/tokenizer_config.json#L52)
Additionally, there is a checkpoint file in my repository if you want to merge the LORA yourself.
## More information
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.
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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"<</SYS>>": 38366,
"<<SYS>>": 38365,
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"<|im_end|>": 38368
}

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---
library_name: peft
base_model: Viet-Mistral/Vistral-7B-Chat
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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### Recommendations
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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
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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### Testing Data, Factors & Metrics
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## Environmental Impact
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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]
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### Framework versions
- PEFT 0.7.2.dev0

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"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": "Viet-Mistral/Vistral-7B-Chat",
"bias": "none",
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
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"q_proj",
"k_proj",
"lm_head",
"down_proj",
"o_proj",
"v_proj",
"gate_proj"
],
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{
"_name_or_path": "Viet-Mistral/Vistral-7B-Chat",
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 2,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 32768,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-05,
"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
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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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)

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"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"transformers_version": "4.38.0.dev0",
"use_cache": false
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run.py Normal file
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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()

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special_tokens_map.json Normal file
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{
"bos_token": "<s>",
"eos_token": "<|im_end|>",
"pad_token": "</s>",
"unk_token": "<unk>"
}

108396
tokenizer.json Normal file

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tokenizer.model Normal file
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version https://git-lfs.github.com/spec/v1
oid sha256:e792a804bbfc19a96b61b87109b8f2b0b7c92830025f285b402ba27c0c309c6f
size 596883

80
tokenizer_config.json Normal file
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{
"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
}

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