license: apache-2.0
Moxin Chat 7B
Home Page | Technical Report | Base Model | Chat Model
Model
You can download our base 7B model from this link and our chat 7B model from this link.
Inference
You can use the following code to run inference with the model. The model is saved under './model/' directory. Change the model directory accordingly or use the Huggingface link.
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
torch.backends.cuda.enable_mem_efficient_sdp(False)
torch.backends.cuda.enable_flash_sdp(False)
model_name = 'moxin-org/moxin-chat-7b'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer = tokenizer,
torch_dtype=torch.bfloat16,
device_map="auto"
)
prompt = "Can you explain the concept of regularization in machine learning?"
sequences = pipe(
prompt,
do_sample=True,
max_new_tokens=1000,
temperature=0.7,
top_k=50,
top_p=0.95,
num_return_sequences=1,
)
print(sequences[0]['generated_text'])
Chat template
The chat template is available via the apply_chat_template() method:
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
model = AutoModelForCausalLM.from_pretrained("moxin-org/moxin-chat-7b")
tokenizer = AutoTokenizer.from_pretrained("moxin-org/moxin-chat-7b")
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])
Evaluation
We test the performance of our model with lm-evaluation-harness. The evaluation results on common datasets are shown below. We test on AI2 Reasoning Challenge (25-shot), HellaSwag (10-shot), MMLU (5-shot), and Winogrande (5-shot).
| Models | ARC-C | Hellaswag | MMLU | WinoGrade | Ave |
|---|---|---|---|---|---|
| Mistral-7B | 57.59 | 83.25 | 62.42 | 78.77 | 70.51 |
| LLaMA 3.1-8B | 54.61 | 81.95 | 65.16 | 77.35 | 69.77 |
| LLaMA 3-8B | 55.46 | 82.09 | 65.29 | 77.82 | 70.17 |
| LLaMA 2-7B | 49.74 | 78.94 | 45.89 | 74.27 | 62.21 |
| Qwen 2-7B | 57.68 | 80.76 | 70.42 | 77.43 | 71.57 |
| gemma-7b | 56.48 | 82.31 | 63.02 | 78.3 | 70.03 |
| internlm2.5-7b | 54.78 | 79.7 | 68.17 | 80.9 | 70.89 |
| Baichuan2-7B | 47.87 | 73.89 | 54.13 | 70.8 | 61.67 |
| Yi-1.5-9B | 58.36 | 80.36 | 69.54 | 77.53 | 71.48 |
| Moxin-7B-original | 53.75 | 75.46 | 59.43 | 70.32 | 64.74 |
| Moxin-7B-finetuned | 59.47 | 83.08 | 60.97 | 78.69 | 70.55 |
We also test the zero shot performance on AI2 Reasoning Challenge (0-shot), AI2 Reasoning Easy (0-shot), HellaSwag (0-shot), PIQA (0-shot) and Winogrande (0-shot). The results are shown below.
| Models | HellaSwag | WinoGrade | PIQA | ARC-E | ARC-C | Ave |
|---|---|---|---|---|---|---|
| Mistral-7B | 80.39 | 73.4 | 82.15 | 78.28 | 52.22 | 73.29 |
| LLaMA 2-7B | 75.99 | 69.06 | 79.11 | 74.54 | 46.42 | 69.02 |
| LLaMA 2-13B | 79.37 | 72.22 | 80.52 | 77.4 | 49.06 | 71.71 |
| LLaMA 3.1-8B | 78.92 | 74.19 | 81.12 | 81.06 | 53.67 | 73.79 |
| gemma-7b | 80.45 | 73.72 | 80.9 | 79.97 | 54.1 | 73.83 |
| Qwen v2-7B | 78.9 | 72.38 | 79.98 | 74.71 | 50.09 | 71.21 |
| internlm2.5-7b | 79.14 | 77.9 | 80.52 | 76.16 | 51.37 | 73.02 |
| Baichuan2-7B | 72.25 | 67.17 | 77.26 | 72.98 | 42.15 | 66.36 |
| Yi-1.5-9B | 77.86 | 73.01 | 80.74 | 79.04 | 55.03 | 73.14 |
| deepseek-7b | 76.13 | 69.77 | 79.76 | 71.04 | 44.8 | 68.3 |
| Moxin-7B-original | 72.06 | 66.31 | 78.07 | 71.47 | 48.15 | 67.21 |
| Moxin-7B-finetune | 80.03 | 75.17 | 82.24 | 81.12 | 58.64 | 75.44 |