YeungNLP/firefly-mixtral-8x7b

This model is finetuend based on "mistralai/Mixtral-8x7B-v0.1" with Firefly and 48k data from ultrachat.

Evaluation

Though we finetune with only 48k data, our model can also achieve excellent performance.

Model	Open LLM Leaderboard
Qwen-72B	73.6
Mixtral-8x7B-Instruct-v0.1	72.62
Firefly-Mixtral-8x7B	70.34
Yi-34B	69.42
Mixtral-8x7B-v0.1	68.42
Llama2-65B-Chat	67.87
Qwen-14B	65.86
Vicuna-33B-v1.3	58.54

Run the model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name_or_path = 'YeungNLP/firefly-mixtral-8x7b'
max_new_tokens = 500
top_p = 0.9
temperature = 0.35
repetition_penalty = 1.0

model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    trust_remote_code=True,
    low_cpu_mem_usage=True,
    torch_dtype=torch.float16,
    device_map='auto'
)
model = model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)

text = "Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions."

inst_begin_tokens = tokenizer.encode('[INST]', add_special_tokens=False)
inst_end_tokens = tokenizer.encode('[/INST]', add_special_tokens=False)
human_tokens = tokenizer.encode(text, add_special_tokens=False)
input_ids = [tokenizer.bos_token_id] + inst_begin_tokens + human_tokens + inst_end_tokens

# input_ids = human_tokens
input_ids = torch.tensor([input_ids], dtype=torch.long).cuda()

with torch.no_grad():
    outputs = model.generate(
        input_ids=input_ids, max_new_tokens=max_new_tokens, do_sample=True,
        top_p=top_p, temperature=temperature, repetition_penalty=repetition_penalty,
        eos_token_id=tokenizer.eos_token_id
    )
outputs = outputs.tolist()[0][len(input_ids[0]):]
response = tokenizer.decode(outputs)
response = response.strip().replace(tokenizer.eos_token, "").strip()
print("Chatbot：{}".format(response))