Description
mistralai/Mistral-7B-v0.1 model fine-tuned over 52k alpaca dataset
How to use it
# pip install transformers==4.35.2
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
model_id="MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
streamer = TextStreamer(tokenizer)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=1024,
temperature=0.1,
do_sample=True,
top_p=0.95,
repetition_penalty=1.15,
return_full_text=False,
streamer=streamer
)
prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
describe about pros and cons of docker system. Answer in bullet point
### Response:
"""
res = pipe(prompt)[0]['generated_text']
Results:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
describe about pros and cons of docker system. Answer in bullet point
### Response:
Pros of Docker System:
- Improved portability - Docker containers can be easily moved between different environments, making it easier to deploy applications across multiple platforms.
- Increased security - Containers are isolated from each other, which helps prevent malicious code from spreading throughout the system.
- Better resource utilization - Containers allow for better resource management by allowing users to run multiple applications on a single host without having to worry about conflicts or performance issues.
Cons of Docker System:
- Learning curve - It takes time to learn how to use Docker effectively, as there are many commands and concepts involved.
- Limited customization options - While Docker provides some basic configuration options, more advanced features such as network routing require additional tools.
- Performance overhead - Running multiple containers on a single host may result in slower performance due to increased memory usage.</s>
Eval
{
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"acc_norm": 0.6368691004374645,
"acc_norm_stderr": 0.03298401757997533,
"mc1": 0.29008567931456547,
"mc1_stderr": 0.01588623687420952,
"mc2": 0.41501661742948026,
"mc2_stderr": 0.014285902986671931
},
"harness|arc:challenge|25": {
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"acc_norm": 0.6092150170648464,
"acc_norm_stderr": 0.01425856388051378
},
"harness|hellaswag|10": {
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"acc_stderr": 0.0048384969668239025,
"acc_norm": 0.8212507468631747,
"acc_norm_stderr": 0.0038235918141330347
},
"harness|hendrycksTest-abstract_algebra|5": {
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},
"harness|hendrycksTest-anatomy|5": {
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"acc_norm": 0.6,
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},
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},
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},
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},
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},
"harness|hendrycksTest-conceptual_physics|5": {
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},
"harness|hendrycksTest-econometrics|5": {
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},
"harness|hendrycksTest-electrical_engineering|5": {
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},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.3915343915343915,
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"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.32,
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},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.7419354838709677,
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},
"harness|hendrycksTest-high_school_chemistry|5": {
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"acc_norm": 0.5024630541871922,
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},
"harness|hendrycksTest-high_school_computer_science|5": {
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"acc_norm": 0.67,
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},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm": 0.7575757575757576,
"acc_norm_stderr": 0.03346409881055953
},
"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_government_and_politics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"harness|hendrycksTest-high_school_physics|5": {
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"harness|hendrycksTest-high_school_psychology|5": {
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"harness|hendrycksTest-high_school_statistics|5": {
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"acc_norm": 0.49537037037037035,
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},
"harness|hendrycksTest-high_school_us_history|5": {
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},
"harness|hendrycksTest-high_school_world_history|5": {
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"harness|hendrycksTest-human_aging|5": {
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},
"harness|hendrycksTest-human_sexuality|5": {
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},
"harness|hendrycksTest-international_law|5": {
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"harness|hendrycksTest-jurisprudence|5": {
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"harness|hendrycksTest-logical_fallacies|5": {
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"harness|hendrycksTest-management|5": {
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"harness|hendrycksTest-miscellaneous|5": {
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"harness|hendrycksTest-public_relations|5": {
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-sociology|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"mc2": 0.41501661742948026,
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},
"harness|winogrande|5": {
"acc": 0.7734806629834254,
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},
"harness|gsm8k|5": {
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"acc_stderr": 0.013331774158491393
}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 60.46 |
| AI2 Reasoning Challenge (25-Shot) | 60.92 |
| HellaSwag (10-Shot) | 82.13 |
| MMLU (5-Shot) | 63.41 |
| TruthfulQA (0-shot) | 41.50 |
| Winogrande (5-shot) | 77.35 |
| GSM8k (5-shot) | 37.45 |
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Model tree for MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1
Dataset used to train MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard60.920
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard82.130
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard63.410
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard41.500
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard77.350
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard37.450