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Model: Nondzu/Mistral-7B-codealpaca-lora Source: Original Platform
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README.md
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---
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license: apache-2.0
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tags:
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- code
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- mistral
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---
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# Mistral-7B-codealpaca
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I am thrilled to introduce my Mistral-7B-codealpaca model. This variant is optimized and demonstrates potential in assisting developers as a coding companion. I welcome contributions from testers and enthusiasts to help evaluate its performance.
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## Training Details
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I trained the model using 3xRTX 3090 for 118 hours.
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[](https://github.com/OpenAccess-AI-Collective/axolotl)
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## Quantised Model Links:
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1. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-GPTQ
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2. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-GGUF
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3. https://huggingface.co/TheBloke/Mistral-7B-codealpaca-lora-AWQ
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## Download by qBittorrent:
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#### Torrent file: https://github.com/Nondzu/LlamaTor/blob/torrents/torrents/Nondzu_Mistral-7B-codealpaca-lora.torrent
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## Dataset:
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- Dataset Name: theblackcat102/evol-codealpaca-v1
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- Dataset Link: [theblackcat102/evol-codealpaca-v1](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1)
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## Prompt template: Alpaca
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt}
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### Response:
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```
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## Performance (evalplus)
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Human eval plus: https://github.com/evalplus/evalplus
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Well, the results are better than I expected:
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- Base: `{'pass@1': 0.47560975609756095}`
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- Base + Extra: `{'pass@1': 0.4329268292682927}`
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For reference, I've provided the performance of the original Mistral model alongside my Mistral-7B-code-16k-qlora model.
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** [Nondzu/Mistral-7B-code-16k-qlora](https://huggingface.co/Nondzu/Mistral-7B-code-16k-qlora)**:
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- Base: `{'pass@1': 0.3353658536585366}`
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- Base + Extra: `{'pass@1': 0.2804878048780488}`
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** [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)**:
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- Base: `{'pass@1': 0.2926829268292683}`
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- Base + Extra: `{'pass@1': 0.24390243902439024}`
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## Model Configuration:
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Here are the configurations for my Mistral-7B-codealpaca-lora:
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```yaml
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base_model: mistralai/Mistral-7B-Instruct-v0.1
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base_model_config: mistralai/Mistral-7B-Instruct-v0.1
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model_type: MistralForCausalLM
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tokenizer_type: LlamaTokenizer
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is_mistral_derived_model: true
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load_in_8bit: true
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load_in_4bit: false
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strict: false
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datasets:
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- path: theblackcat102/evol-codealpaca-v1
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type: oasst
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dataset_prepared_path:
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val_set_size: 0.01
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output_dir: ./nondzu/Mistral-7B-codealpaca-test14
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adapter: lora
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sequence_len: 4096
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sample_packing: true
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pad_to_sequence_len: true
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lora_r: 32
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules:
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lora_target_linear: true
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```
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## Additional Projects:
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For other related projects, you can check out:
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- [LlamaTor on GitHub](https://github.com/Nondzu/LlamaTor)
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"vocab_size": 32000
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}
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|
||||
"model.norm.weight": "pytorch_model-00002-of-00002.bin"
|
||||
}
|
||||
}
|
||||
56
run-humaneval.py
Normal file
56
run-humaneval.py
Normal file
@@ -0,0 +1,56 @@
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
import torch.multiprocessing as mp
|
||||
from transformers import AutoTokenizer, LlamaForCausalLM
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from evalplus.data import get_human_eval_plus, write_jsonl
|
||||
import os
|
||||
from tqdm import tqdm # import tqdm
|
||||
|
||||
def setup(rank, world_size):
|
||||
os.environ['MASTER_ADDR'] = 'localhost'
|
||||
os.environ['MASTER_PORT'] = '12355'
|
||||
dist.init_process_group("gloo", rank=rank, world_size=world_size)
|
||||
|
||||
def cleanup():
|
||||
dist.destroy_process_group()
|
||||
|
||||
def generate_one_completion(ddp_model, tokenizer, prompt: str):
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=4096)
|
||||
|
||||
# Generate
|
||||
generate_ids = ddp_model.module.generate(inputs.input_ids.to("cuda"), max_new_tokens=384, do_sample=True, top_p=0.75, top_k=40, temperature=0.1, pad_token_id=tokenizer.eos_token_id)
|
||||
completion = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||||
completion = completion.replace(prompt, "").split("\n\n\n")[0]
|
||||
|
||||
print("-------------------")
|
||||
print(completion)
|
||||
return completion
|
||||
|
||||
def run(rank, world_size):
|
||||
setup(rank, world_size)
|
||||
|
||||
model_path = "Nondzu/Mistral-7B-codealpaca-lora"
|
||||
model = LlamaForCausalLM.from_pretrained(model_path,load_in_8bit=True)
|
||||
ddp_model = DDP(model, device_ids=[rank])
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
||||
|
||||
problems = get_human_eval_plus()
|
||||
num_samples_per_task = 1
|
||||
|
||||
samples = [
|
||||
dict(task_id=task_id, completion=generate_one_completion(ddp_model, tokenizer, problems[task_id]["prompt"]))
|
||||
for task_id in tqdm(problems) # add tqdm here
|
||||
for _ in range(num_samples_per_task)
|
||||
]
|
||||
write_jsonl(f"samples-Nondzu-Mistral-7B-codealpaca-lora-rank{rank}.jsonl", samples)
|
||||
|
||||
cleanup()
|
||||
|
||||
def main():
|
||||
world_size = 1
|
||||
mp.spawn(run, args=(world_size,), nprocs=world_size, join=True)
|
||||
|
||||
if __name__=="__main__":
|
||||
main()
|
||||
11
special_tokens_map.json
Normal file
11
special_tokens_map.json
Normal file
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<unk>",
|
||||
"<s>",
|
||||
"</s>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"eos_token": "</s>",
|
||||
"pad_token": "</s>",
|
||||
"unk_token": "<unk>"
|
||||
}
|
||||
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
50
tokenizer_config.json
Normal file
50
tokenizer_config.json
Normal file
@@ -0,0 +1,50 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<unk>",
|
||||
"<s>",
|
||||
"</s>"
|
||||
],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "</s>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "</s>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"tokenizer_file": "/home/kamil/.cache/huggingface/hub/models--mistralai--Mistral-7B-Instruct-v0.1/snapshots/7ad5799710574ba1c1d953eba3077af582f3a773/tokenizer.json",
|
||||
"trust_remote_code": false,
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": true,
|
||||
"use_fast": true
|
||||
}
|
||||
Reference in New Issue
Block a user