Cohere Command-R Beats GPT 3.5. Did it Pass the Coding Test?
Summary
TLDR视频介绍了由C公司发布的大型语言模型Command R,它是一款具有高准确性、低延迟和高吞吐量的检索增强型生成模型。Command R支持10种主要语言,并且在Hugging Face上提供模型权重以供研究和评估。通过一系列编程和逻辑推理测试,展示了Command R在不同难度级别上的处理能力,尽管在专家级挑战中未能完全成功,但其性能仍然令人印象深刻。
Takeaways
- 🌟 介绍了名为Command R的大型语言模型,由C公司发布,具有检索增强和生成能力,适用于大规模生产环境。
- 🎯 Command R在Rag和Tool使用上表现出色,具有低延迟和高吞吐量,支持128,000的上下文长度。
- 💰 提到了Command R的价格正在降低,同时在10种关键语言上具有强大的能力。
- 📈 强调了Command R在性能上的优势,特别是在企业RAG使用案例中。
- 🔍 Command R的模型权重可以在Hugging Face上找到,用于研究和评估。
- 🏆 通过与嵌入和重排模型结合使用,Command R在端到端拖拽任务中的准确性得到了提升。
- 🧠 展示了Command R在多步推理和搜索工具方面的应用。
- 🌐 在多语言评估中,Command R在120,000上下文窗口的Hast堆栈测试中表现优异。
- 📊 通过编程测试和逻辑推理测试,展示了Command R在不同难度级别上的挑战和成果。
- 🛠️ 描述了Command R在处理专家级挑战时的局限性,尽管在硬挑战中表现良好,但未能完成非常困难的专家级挑战。
- 📈 总结了Command R作为一个35亿参数模型的主要特点,特别是在RAG优化和128,000上下文长度方面的优势。
Q & A
Command R是什么?
-Command R是一个由C语言开发的大规模检索增强型生成模型,具有高准确性、低延迟、高吞吐量的特点。
Command R在哪些方面表现出色?
-Command R在Rag和Tool使用、多语言能力、企业级应用案例等方面表现出色,特别是在长文本处理(128,000上下文)和性能方面。
Command R支持哪些关键语言?
-Command R支持10种关键语言,具有强大的多语言处理能力。
Command R的模型权重可以在哪些平台找到?
-Command R的模型权重可以在Hugging Face平台上找到,用于研究和评估。
Command R在端到端拖拽任务中的准确性如何?
-Command R在端到端拖拽任务中的准确性高于Llama 270b Mixl和Gbd 3.5 Turbo。
Command R在多步骤推理和搜索工具方面的表现如何?
-Command R在多步骤推理和搜索工具方面表现良好,能够快速生成响应并解决问题。
Command R在多语言评估中的表现如何?
-即使在多语言评估中,Command R也显示出了较好的性能。
Command R在处理硬挑战和专家级挑战时的表现如何?
-Command R能够完成硬挑战,但在专家级挑战中未能成功,表明它在处理更复杂问题时仍有提升空间。
Command R在逻辑和推理测试中的表现如何?
-Command R在逻辑和推理测试中表现不一,能够正确回答一些问题,但也存在错误的情况。
Command R的参数规模是多少?
-Command R是一个拥有350亿参数的模型,主要优化了Rag,支持128,000的文本长度。
Command R在实际编程测试中的表现如何?
-在实际编程测试中,Command R能够快速生成解决方案并通过测试,显示出其在编程任务中的有效性。
Outlines
🤖 介绍Command R大型语言模型
本段落介绍了Command R,这是一个由C公司发布的大型语言模型。Command R是一个检索增强型、适用于生产规模的语言模型,具有强大的准确性,低延迟和高吞吐量,支持长达128,000的上下文长度。模型在10种关键语言上具有强大的能力,并且模型权重可以在Hugging Face上用于研究和评估。Command R在企业使用案例中表现出色,尤其是在多步骤推理和搜索工具方面。此外,通过与嵌入和重排序模型一起使用,Command R在准确性方面表现更佳。
🧠 Command R模型的编程和逻辑测试
这部分内容展示了Command R模型在编程测试和逻辑推理测试中的表现。通过Coare的游乐场,模型被用于解决一系列编程挑战,包括简单的数学问题、寻找折扣函数、数字到模拟转换器、从DNS提供商获取域名以及身份矩阵的生成。尽管Command R在中等难度的挑战中表现良好,但在最高难度的挑战中未能完全成功。此外,还进行了逻辑和推理测试,例如计算Natalia在两个月内销售的Clips总数,以及计算W在50分钟内的保姆收入。尽管在某些测试中出现了错误,但Command R作为一个35亿参数的模型,总体上表现良好。
Mindmap
Keywords
💡command R
💡检索增强型生成模型
💡多语言支持
💡低延迟
💡高吞吐量
💡上下文长度
💡性能比较
💡逻辑推理测试
💡编程测试
💡多步推理
Highlights
介绍了一个名为Command R的大型语言模型,它是由C语言开发的。
Command R是一个检索增强型、生产规模的生成模型,具有高准确性。
Command R在Rag和工具使用方面的延迟低,吞吐量高。
Command R支持长达128,000个上下文,并且价格正在降低。
Command R在10种关键语言上具有强大的能力。
模型权重可以在Hugging Face上用于研究和评估。
Command R在企业RAG使用案例中具有高性能。
Command R在多步骤推理和搜索工具方面表现出色。
在多语言评估中,Command R的表现优于其他模型。
Command R在Hast stack测试中,对于120,000个上下文窗口的性能表现良好。
Command R在与嵌入和重排序模型一起使用时,准确性更高。
Command R在函数调用方面表现良好,能够通过测试。
Command R能够快速生成数字到模拟转换器函数。
Command R在处理较难的挑战时,如找到DNS提供者的域名,表现出了能力。
Command R在专家级挑战中,如生成ECG序列,未能完全成功。
Command R在逻辑和推理测试中,如解决数学问题,表现不一。
Command R是一个35亿参数模型,主要优化了RAG,并支持128,000个上下文长度。
视频制作者计划创建更多关于Command R和RAG应用的视频。
Transcripts
this is amazing now we have command R
this is a l language model from C
command R is a retrieval augmented
generation at production scale it has a
strong accuracy on Rag and Tool use low
latency and high throughput longer
128,000 context and lowering price
strong capabilities across 10 key
languages and also you can see model
weights available in hugging face for
research and evaluation it has a high
performance and retrieval augmented
generation human reference on Enterprise
rag use cases and the dark ping is
command R and the light ping is mial you
can see the comparison here you can also
see another performance here in regards
to end to end drag you can see the
accuracy for command R is higher
compared to llama 270b mixl and gbd 3.5
turbo when used together with embedding
and rerank model this is performing
higher so the embedding and reranking
model is the cair version this lar
language model is good in function
calling that is enabling access to tools
you can see the comparison here between
GPD 3.5 turbo mixture llama 270p and
command R and you can see the accuracy
for the command R is higher this is
multi-step reasoning with search tools
next if you see multilingual evaluation
even for that you can see command R
performing much better for needle in the
Hast stack test for 120,000 context
window you can see the performance here
this is nice that's exactly what we're
going to see today let's get
[Music]
started hi everyone I'm really excited
to show you about command r a large
language model released by coare in this
we are going to see about the
programming test and also logical and
reasoning test I'm going to take you
through step by step but before that I
regularly create videos in regards to
Artificial Intelligence on my YouTube
channel so do subscribe and click the
Bell icon to stay tuned make sure you
click the like button so this video can
be helpful for many others like you now
we're going to use coar playground and
the model we have chosen is command R
first we going to use Python very easy
challenge so return the sum of two
numbers just copying the instruction
here the solution is locked so we're
going to ask command R to give us a
result for this and I got the answer
here it was quick now I'm going to test
it here and
check and it is a pass next let's go to
the easy challenge find the discount
this will create a function to find the
discount so I'm going to ask the L
language model to create the function
and it is created now going to test the
generated function and click check and
it is a pause next going for the medium
challenge find digital to analog
converter function so requesting the L
language model to write a converter from
digital to analog so now requesting and
here is the answer I can see the
response is very very quick now testing
it here and it is a pause next going to
the hard challenge find domain name from
DNS provider so this should write a
function to find the domain name from
the DNS provider so going to ask this to
the log language model and clicking
enter here and I can see the function
got generated so going to copy the code
and going to test it here check and that
is a pass now going to the very hard
challenge identity Matrix so to write a
function that takes an integer and
Returns the identity Matrix so going to
request the Lost language model to
create a function and here is the answer
copying it let's test that here and
clicking
check I can see it got paed for four and
for the fifth test it got failed so I'm
going to copy this error code asking the
lar language model additionally I'm
going to use the test steps for a better
understanding so I'm just going to click
Summit seems like it's fixing all those
provider test numbers so going to redo
it again now I'm going to test it
check it's a fail now finally going to
the expert level challenge creating ECG
sequence copying the instruction so this
function should generate a ACG sequence
so asking the L language model to do the
same and got the answer here just
copying it and going to test it here
that is a fail so going to copy the
error code going to give a final try and
the code is getting generated and
testing it here so that is a fail so
overall this was able to complete up to
hard challenge but very hard an expert
level challenge it was not able to
complete but still it's a good starting
point now going to give some logical and
reasoning test using GSM 8K data set so
Natalia sold Clips to 48 of her friends
in April and then she sold half as many
Clips in May how many Clips did Natalia
sell all together in April and May
that's the question I'm going to ask and
here is the answer in the month of April
it's 48 in the month of May it's 24
totally 72 clips that is is correct so
here is another question W earns $1 12
an hour for babysitting yesterday she
just did 50 minutes of babysitting how
much did she earn going to ask the L
language model so 1 hour that is 60
Minutes $12 So for 50 minutes it should
be $10 but here the answer is $6 so that
is wrong so this is a fail but overall
this model is good it is a 35 billion
parameter model mainly it is rag
optimized with 128,000 cont text length
I'm really excited about this I'm going
to create more videos similar to this
such as function calling with command R
rag application with command so stay
tuned I hope you like this video do like
share and subscribe and thanks for
watching
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