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This model is a fine-tuned model for Chat based on mosaicml/mpt-7b with max_seq_lenght=2048 on various open source dataset. For the details of the used dataset, please refer to Intel/neural-chat-dataset-v1-1.
Model date
Neural-chat-7b-v1.1 was trained between June and July 2023.
Evaluation
We use the same evaluation metrics as open_llm_leaderboard which uses Eleuther AI Language Model Evaluation Harness, a unified framework to test generative language models on a large number of different evaluation tasks.
| Model | Average ⬆️ | ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️ | TruthfulQA (MC) (0-s) ⬆️ |
|---|---|---|---|---|---|
| mosaicml/mpt-7b | 47.4 | 47.61 | 77.56 | 31 | 33.43 |
| mosaicml/mpt-7b-chat | 49.95 | 46.5 | 75.55 | 37.60 | 40.17 |
| Ours | 51.41 | 50.09 | 76.69 | 38.79 | 40.07 |
Bias evaluation
Following the blog evaluating-llm-bias, we select 10000 samples randomly from allenai/real-toxicity-prompts to evaluate toxicity bias in Language Models
| Model | Toxicity Rito ↓ |
|---|---|
| mosaicml/mpt-7b | 0.027 |
| Ours | 0.0264 |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 3.0
Inference with transformers
import transformers
model = transformers.AutoModelForCausalLM.from_pretrained(
'Intel/neural-chat-7b-v1-1',
trust_remote_code=True
)
Inference with INT8
Follow the instructions link to install the necessary dependencies. Use the below command to quantize the model using Intel Neural Compressor link and accelerate the inference.
python run_generation.py \
--model Intel/neural-chat-7b-v1-1 \
--quantize \
--sq \
--alpha 0.95 \
--ipex
Examples
-
code generation
-
summarization
-
trip
Ethical Considerations and Limitations
neural-chat-7b-v1-1 can produce factually incorrect output, and should not be relied on to produce factually accurate information. neural-chat-7b-v1-1 was trained on various instruction/chat datasets based on mosaicml/mpt-7b. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of neural-chat-7b-v1-1, developers should perform safety testing.
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
Organizations developing the model
The NeuralChat team with members from Intel/SATG/AIA/AIPT. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen.
