TigerResearch / tigerbot-7b-sft-v2

huggingface.co
Total runs: 2
24-hour runs: 0
7-day runs: 0
30-day runs: -20
Model's Last Updated: July 11 2023
text-generation

Introduction of tigerbot-7b-sft-v2

Model Details of tigerbot-7b-sft-v2

TigerBot

A cutting-edge foundation for your very own LLM.

🌐 TigerBot • 🤗 Hugging Face

Github

https://github.com/TigerResearch/TigerBot

Usage
from transformers import AutoTokenizer, AutoModelForCausalLM
from accelerate import infer_auto_device_map, dispatch_model
from accelerate.utils import get_balanced_memory

tokenizer = AutoTokenizer.from_pretrained("TigerResearch/tigerbot-7b-sft-v2")

model = AutoModelForCausalLM.from_pretrained("TigerResearch/tigerbot-7b-sft-v2")

max_memory = get_balanced_memory(model)
device_map = infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["BloomBlock"])
model = dispatch_model(model, device_map=device_map, offload_buffers=True)

device = torch.cuda.current_device()


tok_ins = "\n\n### Instruction:\n"
tok_res = "\n\n### Response:\n"
prompt_input = tok_ins + "{instruction}" + tok_res

input_text = "What is the next number after this list: [1, 2, 3, 5, 8, 13, 21]"
input_text = prompt_input.format_map({'instruction': input_text})

max_input_length = 512
max_generate_length = 1024
generation_kwargs = {
        "top_p": 0.95,
        "temperature": 0.8,
        "max_length": max_generate_length,
        "eos_token_id": tokenizer.eos_token_id,
        "pad_token_id": tokenizer.pad_token_id,
        "early_stopping": True,
        "no_repeat_ngram_size": 4,
    }

inputs = tokenizer(input_text, return_tensors='pt', truncation=True, max_length=max_input_length)
inputs = {k: v.to(device) for k, v in inputs.items()}
output = model.generate(**inputs, **generation_kwargs)
answer = ''
for tok_id in output[0][inputs['input_ids'].shape[1]:]:
    if tok_id != tokenizer.eos_token_id:
        answer += tokenizer.decode(tok_id)
print(answer)

Runs of TigerResearch tigerbot-7b-sft-v2 on huggingface.co

2
Total runs
0
24-hour runs
0
3-day runs
0
7-day runs
-20
30-day runs

More Information About tigerbot-7b-sft-v2 huggingface.co Model

More tigerbot-7b-sft-v2 license Visit here:

https://choosealicense.com/licenses/apache-2.0

tigerbot-7b-sft-v2 huggingface.co

tigerbot-7b-sft-v2 huggingface.co is an AI model on huggingface.co that provides tigerbot-7b-sft-v2's model effect (), which can be used instantly with this TigerResearch tigerbot-7b-sft-v2 model. huggingface.co supports a free trial of the tigerbot-7b-sft-v2 model, and also provides paid use of the tigerbot-7b-sft-v2. Support call tigerbot-7b-sft-v2 model through api, including Node.js, Python, http.

TigerResearch tigerbot-7b-sft-v2 online free

tigerbot-7b-sft-v2 huggingface.co is an online trial and call api platform, which integrates tigerbot-7b-sft-v2's modeling effects, including api services, and provides a free online trial of tigerbot-7b-sft-v2, you can try tigerbot-7b-sft-v2 online for free by clicking the link below.

TigerResearch tigerbot-7b-sft-v2 online free url in huggingface.co:

https://huggingface.co/TigerResearch/tigerbot-7b-sft-v2

tigerbot-7b-sft-v2 install

tigerbot-7b-sft-v2 is an open source model from GitHub that offers a free installation service, and any user can find tigerbot-7b-sft-v2 on GitHub to install. At the same time, huggingface.co provides the effect of tigerbot-7b-sft-v2 install, users can directly use tigerbot-7b-sft-v2 installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

tigerbot-7b-sft-v2 install url in huggingface.co:

https://huggingface.co/TigerResearch/tigerbot-7b-sft-v2

Url of tigerbot-7b-sft-v2

tigerbot-7b-sft-v2 huggingface.co Url

Provider of tigerbot-7b-sft-v2 huggingface.co

TigerResearch
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