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https://github.com/NethermindEth/Mpt-Instruct-DotNet-S
Training and Evaluation code for DotNet focused LLM (based on mosaicml/mpt-7b-instruct)
https://github.com/NethermindEth/Mpt-Instruct-DotNet-S
Last synced: about 1 month ago
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Training and Evaluation code for DotNet focused LLM (based on mosaicml/mpt-7b-instruct)
- Host: GitHub
- URL: https://github.com/NethermindEth/Mpt-Instruct-DotNet-S
- Owner: NethermindEth
- License: apache-2.0
- Created: 2023-08-20T23:45:15.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-25T14:49:48.000Z (over 1 year ago)
- Last Synced: 2024-11-13T00:21:50.874Z (about 1 month ago)
- Language: C#
- Size: 1.59 MB
- Stars: 17
- Watchers: 4
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-dotnet - Mpt-Instruct-DotNet-S - LLM that can generate and explain C# code (and its C# wrapper to run on consumer CPU with 5GB ram+, contains Console and Blazor sample projects) (Artificial Intelligence)
README
# Mpt-Instruct-DotNet-S
This repository hosts examples of [`Nethermind/Mpt-Instruct-DotNet-S`](https://huggingface.co/Nethermind/Mpt-Instruct-DotNet-S) usage and training procedures.
## Use on CPU in .Net
We created a GGML wrapper for MPT GGML codes and provided it in this repository.
It is built for:
- Windows-x64
- Linux-x64
- Mac-Arm (M1 and later, to run right-click on libmpt-library.dylib to open to allow unsigned binary)
Quantised weights can be automatically downloaded from [`Nethermind/Mpt-Instruct-DotNet-S`](https://huggingface.co/Nethermind/Mpt-Instruct-DotNet-S). We provide three flavours:
- f16 - for best results, requires > 14GB of free RAM, slow (in theory, in reality, just runs slower when there is not enough ram)
- q8 - for results with lower quality yet generated faster, requires > 7.5GB of free RAM (in theory, in reality, just runs slower when there is not enough ram)
- q5 - for results with even lower quality yet generated in the least amount of time, requires> 4.5GB of free RAM
Use:
```csharp
var downloader = new ModelDownloader();
var path = await downloader.DownloadModel("q8"); // you also can use f16 (eats 14 GB of RAM), q5 (eats 4 GB)
var mpt = new MptConsole(new mpt_params() {
model = path,
n_predict = 512,
n_ctx = 1024,
// n_threads = 16
});
var result = mpt.Process(@"You are an experienced .Net C# developer. Below is an instruction that describes a task. Write a response that completes the request providing detailed explanations with code examples.
### Instruction:
interface IRobot {
void Take(string what);
void Cut(int size);
void Give(string to);
}
You are a robot. Create a sample of _api usage performing what is asked in Example:
Rick asks a robot: slice and pass me the butter, please
Create example using _api continuing this code:
class Request {
IRobot _api;
void Do() {
### Response:
");
Console.WriteLine("--------------------------------");
Console.WriteLine(result.FilterString());
```
## Use on GPU in Python
It will requier > 8000MB of free GPU ram even with `load_in_8bit=True` In short:
```python
import torch
import transformers
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
tokenizer.pad_token = tokenizer.eos_token
device = torch.device("cuda")
model_name = "Nethermind/Mpt-Instruct-DotNet-S"
config = transformers.AutoConfig.from_pretrained(model_name, trust_remote_code=True)
config.init_device = device
config.max_seq_len = 1024
config.attn_config['attn_impl'] = 'torch'
config.use_cache = False
model = transformers.AutoModelForCausalLM.from_pretrained(
model_name,
config=config,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
ignore_mismatched_sizes=True,
# load_in_8bit=True # when low on GPU memory
)
model.eval()
INSTRUCTION_KEY = "### Instruction:"
RESPONSE_KEY = "### Response:"
PROMPT_FOR_GENERATION_FORMAT = """{system}
{instruction_key}
{instruction}
{response_key}
""".format(
system="{system}",
instruction_key=INSTRUCTION_KEY,
instruction="{instruction}",
response_key=RESPONSE_KEY
)
def give_answer(instruction="Create a loop over [0, 6, 7 , 77] that prints its contentrs", system="You are an experienced .Net C# developer. Below is an instruction that describes a task. Write a response that completes the request providing detailed explanations with code examples.", ):
question = PROMPT_FOR_GENERATION_FORMAT.format(system=system, instruction=instruction)
input_tokens = tokenizer.encode(question ,return_tensors='pt')
model.generate(input_tokens.to(device), max_new_tokens=min(512, 1024 - input_tokens.shape[1]), do_sample=False, top_k=1, top_p=0.95)
outputs = output_loop(tokenized_question)
answer = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(answer[0])
```
### GPU speedups
Set max_new_tokens to 256, 1024-prompt tokens length is its limit.