初始化项目，由ModelHub XC社区提供模型
Model: Corianas/Neural-Mistral-7B Source: Original Platform
This commit is contained in:
ModelHub XC
225
README.md
Normal file
225
README.md
Normal file
@@ -0,0 +1,225 @@
|
||||
---
|
||||
library_name: transformers
|
||||
license: apache-2.0
|
||||
datasets:
|
||||
- Intel/orca_dpo_pairs
|
||||
language:
|
||||
- en
|
||||
---
|
||||
|
||||
# Model Card for Model ID
|
||||
|
||||
This is a DPO finetune of Mistral 7b-instruct0.2 following the article: https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac
|
||||
|
||||
|
||||
## Model Details
|
||||
|
||||
### Model Description
|
||||
|
||||
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
|
||||
|
||||
- **Developed by:** Corianas
|
||||
- **Model type:** [More Information Needed]
|
||||
- **License:** Apache 2.0
|
||||
- **Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2
|
||||
|
||||
|
||||
## Instruction format
|
||||
|
||||
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
|
||||
|
||||
E.g.
|
||||
```
|
||||
text = "<s>[INST] What is your favourite condiment? [/INST]"
|
||||
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
|
||||
"[INST] Do you have mayonnaise recipes? [/INST]"
|
||||
```
|
||||
|
||||
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
device = "cuda" # the device to load the model onto
|
||||
|
||||
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
|
||||
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
|
||||
|
||||
messages = [
|
||||
{"role": "user", "content": "What is your favourite condiment?"},
|
||||
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
|
||||
{"role": "user", "content": "Do you have mayonnaise recipes?"}
|
||||
]
|
||||
|
||||
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
|
||||
|
||||
model_inputs = encodeds.to(device)
|
||||
model.to(device)
|
||||
|
||||
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
|
||||
decoded = tokenizer.batch_decode(generated_ids)
|
||||
print(decoded[0])
|
||||
```
|
||||
|
||||
## Model Architecture
|
||||
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
|
||||
- Grouped-Query Attention
|
||||
- Sliding-Window Attention
|
||||
- Byte-fallback BPE tokenizer
|
||||
|
||||
## How to Get Started with the Model
|
||||
|
||||
Use the code below to get started with the model.
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Training Details
|
||||
|
||||
### Training Data
|
||||
|
||||
Intel/orca_dpo_pairs
|
||||
|
||||
### Training Procedure
|
||||
|
||||
https://medium.com/towards-data-science/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac
|
||||
|
||||
#### Preprocessing [optional]
|
||||
|
||||
def chatml_format(example):
|
||||
# Format system
|
||||
if len(example['system']) > 0:
|
||||
message = {"role": "user", "content": f"{example['system']}\n{example['question']}"}
|
||||
prompt = tokenizer.apply_chat_template([message], tokenize=False)
|
||||
else:
|
||||
# Format instruction
|
||||
message = {"role": "user", "content": example['question']}
|
||||
prompt = tokenizer.apply_chat_template([message], tokenize=False, add_generation_prompt=True)
|
||||
|
||||
# Format chosen answer
|
||||
chosen = example['chosen'] + tokenizer.eos_token
|
||||
|
||||
# Format rejected answer
|
||||
rejected = example['rejected'] + tokenizer.eos_token
|
||||
|
||||
return {
|
||||
"prompt": prompt,
|
||||
"chosen": chosen,
|
||||
"rejected": rejected,
|
||||
}
|
||||
|
||||
|
||||
#### Training Hyperparameters
|
||||
|
||||
training_args = TrainingArguments(
|
||||
per_device_train_batch_size=4,
|
||||
gradient_accumulation_steps=4,
|
||||
gradient_checkpointing=True,
|
||||
learning_rate=5e-5,
|
||||
lr_scheduler_type="cosine",
|
||||
max_steps=200,
|
||||
save_strategy="no",
|
||||
logging_steps=1,
|
||||
output_dir=new_model,
|
||||
optim="paged_adamw_32bit",
|
||||
warmup_steps=100,
|
||||
bf16=True,
|
||||
report_to="wandb",
|
||||
)
|
||||
|
||||
## 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]
|
||||
Reference in New Issue
Block a user