cloudyu / Mixtral_7Bx5_MoE_30B

huggingface.co
Total runs: 89
24-hour runs: -2
7-day runs: -5
30-day runs: 31
Model's Last Updated: January 11 2024
text-generation

Introduction of Mixtral_7Bx5_MoE_30B

Model Details of Mixtral_7Bx5_MoE_30B

Mixtral MOE 5x7B

MoE of the following models :

gpu code example

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math

## v2 models
model_path = "cloudyu/Mixtral_7Bx5_MoE_30B"

tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float32, device_map='auto',local_files_only=False, load_in_4bit=True
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
  input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")

  generation_output = model.generate(
    input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
  )
  print(tokenizer.decode(generation_output[0]))
  prompt = input("please input prompt:")

CPU example

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import math

## v2 models
model_path = "cloudyu/Mixtral_7Bx5_MoE_30B"

tokenizer = AutoTokenizer.from_pretrained(model_path, use_default_system_prompt=False)
model = AutoModelForCausalLM.from_pretrained(
    model_path, torch_dtype=torch.float32, device_map='cpu',local_files_only=False
)
print(model)
prompt = input("please input prompt:")
while len(prompt) > 0:
  input_ids = tokenizer(prompt, return_tensors="pt").input_ids

  generation_output = model.generate(
    input_ids=input_ids, max_new_tokens=500,repetition_penalty=1.2
  )
  print(tokenizer.decode(generation_output[0]))
  prompt = input("please input prompt:")

Runs of cloudyu Mixtral_7Bx5_MoE_30B on huggingface.co

89
Total runs
-2
24-hour runs
-4
3-day runs
-5
7-day runs
31
30-day runs

More Information About Mixtral_7Bx5_MoE_30B huggingface.co Model

More Mixtral_7Bx5_MoE_30B license Visit here:

https://choosealicense.com/licenses/cc-by-nc-4.0

Mixtral_7Bx5_MoE_30B huggingface.co

Mixtral_7Bx5_MoE_30B huggingface.co is an AI model on huggingface.co that provides Mixtral_7Bx5_MoE_30B's model effect (), which can be used instantly with this cloudyu Mixtral_7Bx5_MoE_30B model. huggingface.co supports a free trial of the Mixtral_7Bx5_MoE_30B model, and also provides paid use of the Mixtral_7Bx5_MoE_30B. Support call Mixtral_7Bx5_MoE_30B model through api, including Node.js, Python, http.

Mixtral_7Bx5_MoE_30B huggingface.co Url

https://huggingface.co/cloudyu/Mixtral_7Bx5_MoE_30B

cloudyu Mixtral_7Bx5_MoE_30B online free

Mixtral_7Bx5_MoE_30B huggingface.co is an online trial and call api platform, which integrates Mixtral_7Bx5_MoE_30B's modeling effects, including api services, and provides a free online trial of Mixtral_7Bx5_MoE_30B, you can try Mixtral_7Bx5_MoE_30B online for free by clicking the link below.

cloudyu Mixtral_7Bx5_MoE_30B online free url in huggingface.co:

https://huggingface.co/cloudyu/Mixtral_7Bx5_MoE_30B

Mixtral_7Bx5_MoE_30B install

Mixtral_7Bx5_MoE_30B is an open source model from GitHub that offers a free installation service, and any user can find Mixtral_7Bx5_MoE_30B on GitHub to install. At the same time, huggingface.co provides the effect of Mixtral_7Bx5_MoE_30B install, users can directly use Mixtral_7Bx5_MoE_30B installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

Mixtral_7Bx5_MoE_30B install url in huggingface.co:

https://huggingface.co/cloudyu/Mixtral_7Bx5_MoE_30B

Url of Mixtral_7Bx5_MoE_30B

Mixtral_7Bx5_MoE_30B huggingface.co Url

Provider of Mixtral_7Bx5_MoE_30B huggingface.co

cloudyu
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