Alibaban Qwen: Kiinalainen AI-malli haastaa Silicon Valley

Kirjoittajat : Jennie Bai Suhtaudu Bai Jääkiekko Chu Zeyu Cui Kun vaara Xiaodong Deng Yksi fani Vinkkejä Johanna Han Fei Huang Binyuan HuiMuokkaa Luo Ji Mää Li Ylhäinen Lin Kääpiö Lin Päiväkoti Liu Liu Gao Chengqiang Lu Lähde Lu Jääkiekko Ma Rui Men Ylhäinen Ren Xuancheng Ren Huuhkaja Tan Syyrialainen Tan Tiina Tu Jäsen Wang Liisa Wang Ystävä Wang Shengguang Wu Benfeng Xu Yin Xu Yksi Yang Hao Yang Jian Yang Shusheng Yin Yang Yao Bowen Yu Hongyi Yuan Zheng Juani Jianwei Zhang Xingxuan Zhang Yichang Zhang Jyrki Zhang Chang Zhou Jingren Zhou Tietoa Xiaohuan Zhou Tianhang Zhu Qwen Team, Alibaba ryhmä Kirjoittajat : Jennie Bai Suhtaudu Bai Jääkiekko Chu Tyyppi Cui Kun vaara Huomioi Deng Yksi fani Vinkkejä Johanna Han Fei Huang Binyuan HuiMuokkaa Luo Ji Mää Li Ylhäinen Lin Kääpiö Lin Päiväkoti Liu Liu Gao Jääkiekko Lu Lähde Lu Jääkiekko Ma Rui Miehet Ylhäinen Ren Jyrki Ren Huuhkaja Tan Syyrialainen Tan Tiina Tu Jäsen Wang Liisa Wang Ystävä Wang Jääkiekko Wu Benfeng Xu Yin Xu Yksi Yang Hao Yang Jian Yang Shusheng Yin Yin Yao Jääkiekko Yu Hongyi Yuan Zheng Juani Pääosat Jianwei Zhang Xingxuan Zhang Pääosat Zhang Jyrki Zhang Chang ZhouMuokkaa Jingren Zhou Tietoa Xiaohuan Zhou Tianhang Zhu Qwen Team, Alibaba Group abstrakti Suuret kielimallit (LLM) ovat mullistanut keinotekoisen älykkyyden alan, mikä mahdollistaa luonnollisen kielen käsittelytehtävät, joiden uskottiin aiemmin olevan yksinomaan ihmisille. QWEN on kattava kielimallisarja, joka sisältää erillisiä malleja, joilla on vaihtelevat parametrit. Se sisältää QWEN: n, perusvalmistettujen kielimallien, ja QWEN-CHAT: n, chat-mallien, jotka on räätälöity ihmisen tasapainotustekniikoilla. Peruskielimallit osoittavat johdonmukaisesti erinomaista suorituskykyä monissa jäljellä olevissa tehtävissä, ja chat-mallit, erityisesti ne, jotka on koulutettu käyttämällä Reinforcement Learning from Human Feedback (RLHF), ovat erittäin kilpailukykyisiä. Chat-malleilla on edistykselliset työkalujen käyttö- ja suunnitteluominaisuudet agentti-sovellusten luomiseksi, mikä osoittaa vaikuttavaa suorituskykyä jopa verratt 1 1 Johdanto Suurten kielten mallit (LLM) ovat mullistaneet keinotekoisen älykkyyden (AI) alalla tarjoamalla tehokkaan perustan monimutkaiseen päättelyyn ja ongelmanratkaisutehtäviin. Näillä malleilla on kyky puristaa laaja tietämys hermoverkkoihin, mikä tekee niistä uskomattoman monipuolisia agentteja. Chat-liittymällä LLM: t voivat suorittaa tehtäviä, joita aiemmin pidettiin ihmisten yksinomaisena alueena, erityisesti luovuutta ja asiantuntemusta koskevia He voivat osallistua luonnonkielisiin keskusteluihin ihmisten kanssa, vastata kysymyksiin, antaa tietoa ja jopa luoda luovaa sisältöä, kuten tarinoita, runoja ja musiikkia. (Radford ja al., Vuonna 2018; Devlin ja al., Vuonna 2018; Raffaele et al. vuonna 2020; Brownin ja al, vuonna 2020; avoinna on, Vuonna 2023 Chowdhery ja muut. Vuonna 2022 Anniina ja al. Vuonna 2023 Tammisaaren et al. Vuonna 2022 Touvron ja al, Vuonna 2023 a; b) ja avoinna, Vuonna 2022 Oulussa et al. Vuonna 2022 Anil ja al, Vuonna 2023 Googlen kanssa, Vuonna 2023 ja antropologinen, Vuonna 2023 a; b). LLM: t eivät rajoitu vain kielen tehtäviin. ne voivat toimia myös yleisenä edustajana yhteistyössä ulkoisten järjestelmien, työkalujen ja mallien kanssa ihmisten asettamien tavoitteiden saavuttamiseksi. Suorita koodi Käytä työkaluja Tämä avaa koko uuden mahdollisuuksien maailman tekoälyn sovelluksille, autonomisista ajoneuvoista ja robotiikasta terveydenhuoltoon ja rahoitukseen.Koska nämä mallit kehittyvät ja parantuvat edelleen, voimme odottaa näkevänsä entistä innovatiivisempia ja jännittäviä sovelluksia tulevina vuosina.Onpa se auttamassa meitä ratkaisemaan monimutkaisia ongelmia, luomaan uusia viihteen muotoja tai muuttamaan tapaa, jolla elämme ja työskentelemme, LLM: llä on valmiina keskeinen rooli tekoälyn tulevaisuuden muokkaamisessa. (Rihanna ja muut) Vuonna 2022 Bai et al, Vuonna 2022A Wang et al., Vuonna 2023a Automaattivaihteisto, Vuonna 2023 Hong et al, Vuonna 2023 ja avoinna, Vuonna 2023 Bai et al, Vuonna 2023 Liisa ja al. Vuonna 2023a Sinä ja minä, Vuonna 2023 Jaa ja al. Vuonna 2023 Jäsenet et al. Vuonna 2023 b) (Chen et al., Vuonna 2021 Jussi ja al. Vuonna 2023 Li ja al, vuonna 2023 d), (Häppäri et al. Vuonna 2023 Yhtiö LangChain, Inc. Vuonna 2023 Automaattivaihteisto, Vuonna 2023 Huolimatta vaikuttavista ominaisuuksistaan, LLM-ohjelmia arvostellaan usein niiden toistettavuuden, hallittavuuden ja palveluntarjoajien saavutettavuuden puutteesta. Tässä työssä olemme iloisia voidessamme esitellä ja julkaista LLM-sarjan alkuperäisen version, QWEN. QWEN on nimike, joka johtuu kiinalaisesta lauseesta Qianwen, joka kääntää "tuhansia käskyjä" ja välittää käsitteen, joka kattaa laajan kyselyn. QWEN on kattava kielimallisarja, joka sisältää erillisiä malleja, joilla on erilaiset parametrit. Mallisarja sisältää perusvalmistetut kielimallit, chat-mallit, jotka on hienostettu ihmisen mukauttamistekniikoilla, ts. valvottu finetuning 1. Peruskielimallit, nimittäin QWEN, ovat käyneet laajaa koulutusta käyttämällä jopa 3 biljoonaa tokenia monipuolisia tekstejä ja koodeja, jotka kattavat monenlaisia alueita. 2. QWEN-CHAT-mallit on hienostettu huolellisesti tehtävien suorittamiseen, chattiin, työkalujen käyttöön, agenttiin, turvallisuuteen jne. Vertailuarviointi osoittaa, että SFT-mallit voivat saavuttaa erinomaisen suorituskyvyn. Lisäksi olemme kouluttaneet palkitsemismalleja jäljittelemään ihmisen mieltymyksiä ja soveltaneet niitä RLHF: ssä chat-malleihin, jotka voivat tuottaa ihmisten mieltymyksiä. Haastavan testin ihmisen arvioinnin kautta havaitsemme, että RLHF: n kanssa koulutettu QWEN-CHAT-malli on erittäin kilpailukykyinen, joka jää edelleen GPT-4: n taakse vertailuarvostamme. 3. Lisäksi esittelemme erikoistuneita malleja, joita kutsutaan nimellä CODE-QWEN, johon kuuluvat CODE-QWEN-7B ja CODE-QWEN-14B sekä niiden chat-mallit, CODE-QWEN-14B-CHAT ja CODE-QWEN-7B-CHAT. Erityisesti CODE-QWEN on koulutettu laajoihin koodin tietokokonaisuuksiin ja hienostunut käsittelemään koodin tuottamiseen, vianmääritykseen ja tulkintaan liittyviä keskusteluja. MBPP ja HumanEvalPack osoittaa CODE-QWENin korkean tason taito koodin ymmärtämisessä ja luomisessa. (Hän et al , Vuonna 2021 (Austin ja al., Vuonna 2021 (Mäntsälän et al., Vuonna 2023 Tuloksemme osoittavat, että sekä MATH-QWEN-7B-CHAT että MATH-QWEN-14B-CHAT ylittävät avoimen lähdekoodin mallit samassa koossa suurilla marginaaleilla ja lähestyvät GPT-3.5: tä matematiikkaan liittyvissä vertailuarvotietokannoissa, kuten GSM8K: ssä ja matematiikkaa Kobe ja al, vuonna 2021) (Häppäri ja muut) Vuonna 2021). 5. Lisäksi meillä on avoimen lähdekoodin QWEN-VL- ja QWEN-VL-CHAT-mallit, joilla on monipuolinen kyky ymmärtää visuaalisia ja kielellisiä ohjeita. Nämä mallit ylittävät nykyiset avoimen lähdekoodin visuaalisen kielen mallit eri arviointikriteereissä ja tukevat tekstin tunnistamista ja visuaalista maadoitusta sekä kiinalaisilla että englanninkielisillä kielillä. Bai ja al. 2023 mukaan. Nyt olemme virallisesti avoimen lähdekoodin 14B-parametri- ja 7B-parametri-pohjaisia valmiiksi koulutettuja malleja QWEN ja tasoitettuja chat-malleja QWEN-CHAT Tämän julkaisun tavoitteena on tarjota kattavampia ja tehokkaampia LLM-ohjelmia kehittäjä- tai sovellusystävällisissä mittakaavoissa. 2 Kertomuksen rakenne on seuraava: Osa kuvailee lähestymistapaamme QWEN: n esikoulutukseen ja tuloksiin. kattaa yhdenmukaistamismenetelmämme ja raportoi sekä automaattisen arvioinnin että inhimillisen arvioinnin tuloksista.Lisäksi tässä osassa kuvataan yksityiskohtaisesti ponnisteluistamme työkalujen, koodin tulkin ja agentin käyttämiseen kykenevien chat-mallien rakentamisessa. ja tutustumme erikoistuneisiin malleihin koodauksesta ja matematiikasta sekä niiden suorituskykyyn. antaa yleiskatsauksen asiaankuuluvasta työstä, ja Se päättää tämän artikkelin ja kertoo tulevasta työstämme. 2 3 4 5 , 6 7 2 Esikoulutusta The pretraining stage involves learning vast amount of data to acquire a comprehensive understanding of the world and its various complexities. This includes not only basic language capabilities but also advanced skills such as arithmetic, coding, and logical reasoning. In this section, we introduce the data, the model design and scaling, as well as the comprehensive evaluation results on benchmark datasets. 2.1 Tiedot Tietojen koko on osoittautunut ratkaisevaksi tekijäksi vahvan suuren kielen mallin kehittämisessä, kuten edellisessä tutkimuksessa korostettiin. Tehokkaan esikoulutustietokokonaisuuden luomiseksi on välttämätöntä varmistaa, että tiedot ovat monipuolisia ja kattavat laajan valikoiman tyyppejä, toimialoja ja tehtäviä. Tietokokonaisuutemme on suunniteltu vastaamaan näihin vaatimuksiin ja sisältää julkisia web-asiakirjoja, tietosanakirjoja, kirjoja, koodeja jne. Lisäksi tietokokonaisuutemme on monikielinen, ja merkittävä osa tiedoista on englanniksi ja kiinaksi. (Häppäri et al. Vuonna 2022 Jääkiekko et al. Vuonna 2023 b. Ennakkotietojemme laadun varmistamiseksi olemme kehittäneet kattavan tietojen esikäsittelymenettelyn. Julkisen web-tietojen osalta poimimme tekstiä HTML: stä ja käytämme kielen tunnistustyökaluja kielen määrittämiseksi. Tietojemme monimuotoisuuden lisäämiseksi käytämme deduplointitekniikoita, mukaan lukien täsmällinen deduplointi normalisoinnin jälkeen ja hämärä deduplointi käyttäen MinHash- ja LSH-algoritmeja. Vähälaatuisten tietojen suodattamiseksi käytämme sääntöihin perustuvien ja koneoppimiseen perustuvien menetelmien yhdistelmää. Erityisesti käytämme useita malleja sisällön pisteytykseen, mukaan lukien kielimallit, tekstin laadun pisteytysmallit ja mallit mahdollisesti lo on osoitettu, että kielimallien esikoulutus, jossa on usean tehtävän ohjeet, voi parantaa niiden nollatappio- ja vähätappio-suorituskykyä. Mallimme suorituskyvyn parantamiseksi olemme sisällyttäneet laadukkaita opetustietoja esikoulutusprosessiin. ja huolellisesti poistanut kaikki ohjeet näytteet, jotka osoittavat 13 gramman päällekkäisyydet kaikkiin tietoihin testisarjoissa käytetty arvioinnissamme. (Hän et al. Vuonna 2022 Jäsenet et al., Vuonna 2021 Raffaele et al. vuonna 2020), Brown ja al. Vuoden 2020 Kun otetaan huomioon suuri määrä jäljellä olevia tehtäviä, ei ole mahdollista toistaa tätä suodatusprosessia kaikille tehtäville. Sen sijaan olemme varmistaneet, että ilmoitettujen tehtävien ohjeet ovat läpäisseet suodatusprosessimme varmistaaksemme niiden tarkkuuden ja luotettavuuden. 2.2 Tokenisointi Sanaston suunnittelu vaikuttaa merkittävästi koulutuksen tehokkuuteen ja jäljellä olevien tehtävien suorituskykyyn. Tässä tutkimuksessa käytämme byteparikoodausta (BPE) tokenisaatiomenetelmäämme GPT-3.5: n ja GPT-4: n jälkeen. Aloitamme avoimen lähdekoodin nopean BPE-tokenisaattorin, tiktoken ja valitse sanastopohja cl100k lähtökohtana.Valmistaaksemme mallimme suorituskykyä monikielisissä jatkotehtävissä, erityisesti kiinassa, lisäämme sanastoa yleisesti käytetyillä kiinalaisilla kirjaimilla ja sanoilla sekä muilla kielillä. Meillä on numeroita, jotka on jaettu yksittäisiin numeroihin. Lopullinen sanaston koko on noin 152K. Jaakko Niinistö, Vuonna 2022 Jääkiekko et al. 2023 a, b ja b QWEN-tokenisaattorin suorituskyky puristuksen osalta on kuvattu kuvassa Tässä vertailussa olemme arvioineet QWEN: n verrattuna useisiin muihin tokenisaattoreihin, mukaan lukien XLM-R Ääniä Bakuun ja sisäistä Tuloksemme paljastavat, että QWEN saavuttaa suuremman puristustehokkuuden kuin sen kilpailijat useimmissa kielissä. Tämä tarkoittaa, että palvelun kustannuksia voidaan merkittävästi vähentää, koska pienempi määrä QWEN-merkkejä voi välittää enemmän tietoa kuin sen kilpailijat. Lisäksi olemme tehneet alustavia kokeita varmistaaksemme, että QWEN:n sanaston koon skaalaaminen ei vaikuta negatiivisesti esikoulutetun mallin jälkiarviointiin. 3. (Yhteenveto ja al, vuonna 2019), (Touvron et al., Vuonna 2023 a) ja Inc. Vuonna 2023 a) (Maailmanlaajuinen tiimi Vuonna 2023 2.3 Architecture QWEN is designed using a modified version of the Transformer architecture. Specifically, we have adopted the recent open-source approach of training large language models, LLaMA which is widely regarded as the top open-source LLM. Our modifications to the architecture include: (Häppäri ja muut) 2023a), . Based on preliminary experimental findings, we have opted for the untied embedding approach instead of tying the weights of input embedding and output projection. This decision was made in order to achieve better performance with the price of memory costs. Embedding and output projection . We have chosen RoPE (Rotary Positional Embedding) as our preferred option for incorporating positional information into our model. RoPE has been widely adopted and has demonstrated success in contemporary large language models, notably PaLM and LLaMA In particular, we have opted to use FP32 precision for the inverse frequency matrix, rather than BF16 or FP16, in order to prioritize model performance and achieve higher accuracy. Positional embedding (Su et al., 2021) (Chowdhery et al., 2022; Anil et al., 2023) (Touvron et al., 2023a;b). . For most layers, we remove biases following but we add biases in the QKV layer of attention to enhance the extrapolation ability of the model Bias Chowdhery et al. (2022), (Su, 2023b). . In modern Transformer models, pre-normalization is the most widely used approach, which has been shown to improve training stability compared to post-normalization. Recent research has suggested alternative methods for better training stability, which we plan to explore in future versions of our model. Additionally, we have replaced the traditional layer normalization technique described in with RMSNorm This change has resulted in equivalent performance while also improving efficiency. Pre-Norm & RMSNorm (Ba et al., 2016) (Jiang et al., 2023). . We have selected SwiGLU as our activation function, a combination of Swish and Gated Linear Unit Our initial experiments have shown that activation functions based on GLU generally outperform other baseline options, such as GeLU As is common practice in previous research, we have reduced the dimension of the feed-forward network (FFN) from 4 times the hidden size to 8/3 of the hidden size. Activation function (Shazeer, 2020) (Ramachandran et al., 2017) (Dauphin et al., 2017). (Hendrycks & Gimpel, 2016). 2.4 Training To train QWEN, we follow the standard approach of autoregressive language modeling, as described in This involves training the model to predict the next token based on the context provided by the previous tokens. We train models with context lengths of 2048. To create batches of data, we shuffle and merge the documents, and then truncate them to the specified context lengths. To improve computational efficiency and reduce memory usage, we employ Flash Attention in the attention modules We adopt the standard optimizer AdamW for pretraining optimization. We set the hyperparameters 1 = 0 * 9 2 = 0 * 95 ja = 10−8. We use a cosine learning rate schedule with a specified peak learning rate for each model size. The learning rate is decayed to a minimum learning rate of 10% of the peak learning rate. All the models are trained with BFloat16 mixed precision for training stability. Radford et al. (2018). (Dao et al., 2022). (Kingma & Ba, 2014; Loshchilov & Hutter, Vuonna 2017 β β ϵ 2.5 Context Length Extension Transformer models have a significant limitation in terms of the context length for their attention mechanism. As the context length increases, the quadratic-complexity computation leads to a drastic increase in both computation and memory costs. In this work, we have implemented simple training-free techniques that are solely applied during inference to extend the context length of the model. One of the key techniques we have used is NTK-aware interpolation (bloc97, 2023). Unlike position interpolation (PI) which scales each dimension of RoPE equally, NTK-aware interpolation adjusts the base of RoPE to prevent the loss of high-frequency information in a training-free manner. To further improve performance, we have also implemented a trivial extension called dynamic NTK-aware interpolation, which is later formally discussed in It dynamically changes the scale by chunks, avoiding severe performance degradation. These techniques allow us to effectively extend the context length of Transformer models without compromising their computational efficiency or accuracy. (Chen et al., 2023a) (Peng et al., 2023a). QWEN additionally incorporates two attention mechanisms: LogN-Scaling and window attention LogN-Scaling rescales the dot product of the query and value by a factor that depends on the ratio of the context length to the training length, ensuring that the entropy of the attention value remains stable as the context length grows. Window attention restricts the attention to a limited context window, preventing the model from attending to tokens that are too far away. (Chiang & Cholak, 2022; Su, 2023a) (Beltagy et al., 2020). We also observed that the long-context modeling ability of our model varies across layers, with lower layers being more sensitive in context length extension compared to the higher layers. To leverage this observation, we assign different window sizes to each layer, using shorter windows for lower layers and longer windows for higher layers. 2.6 Experimental Results To evaluate the zero-shot and few-shot learning capabilities of our models, we conduct a thorough benchmark assessment using a series of datasets. We compare QWEN with the most recent open-source base models, including LLaMA LLAMA 2 MPT Falcon Baichuan2 ( ChatGLM2 Sisäinen Kymppiä and StableBeluga2 Our evaluation covers a total of 7 popular benchmarks, which are MMLU (5-shot) C-Eval (5-shot) GSM8K (8-shot) MATH (4-shot) Vapaaehtoinen (0-shot) MBPP (0-shot) and BBH (Big Bench Hard) (3 shot) We aim to provide a comprehensive summary of the overall performance of our models across these benchmarks. (Touvron et al., 2023a), (Touvron et al., Vuonna 2023 b) (Mosaic ML, 2023), (Almazrouei et al., 2023), Yin et al. 2023), (ChatGLM2 Team, 2023), (InternLM Team, 2023), (Inc., 2023b), (Stability AI, 2023). (Hendrycks et al., 2020), (Huang et al., 2023), (Cobbe et al., 2021), (Hendrycks et al., 2021), (Chen et al., 2021), (Austin et al., 2021), (Suzgun et al., Vuonna 2022 In this evaluation, we focus on the base language models without alignment and collect the baselines’ best scores from their official results and OpenCompass Tulokset esitetään taulukossa (OpenCompass Team, 2023). 2. Our experimental results demonstrate that the three QWEN models exhibit exceptional performance across all downstream tasks. It is worth noting that even the larger models, such as LLaMA2-70B, are outperformed by QWEN-14B in 3 tasks. QWEN-7B also performs admirably, surpassing LLaMA2-13B and achieving comparable results to Baichuan2-13B. Notably, despite having a relatively small number of parameters, QWEN-1.8B is capable of competitive performance on certain tasks and even outperforms larger models in some instances. The findings highlight the impressive capabilities of the QWEN models, particularly QWEN-14B, and suggest that smaller models, such as QWEN-1.8B, can still achieve strong performance in certain applications. To evaluate the effectiveness of context length extension, Table presents the test results on arXiv in terms of perplexity (PPL). These results demonstrate that by combining NTK-aware interpolation, LogN-Scaling, and layer-wise window assignment, we can effectively maintain the performance of our models in the context of over 8192 tokens. 3 3 3 Alignment Pretrained large language models have been found to be not aligned with human behavior, making them unsuitable for serving as AI assistants in most cases. Recent research has shown that the use of alignment techniques, such as supervised finetuning (SFT) and reinforcement learning from human feedback (RLHF), can significantly improve the ability of language models to engage in natural conversation. In this section, we will delve into the details of how QWEN models have been trained using SFT and RLHF, and evaluate their performance in the context of chat-based assistance. 3.1 Valvottu finetuning To gain an understanding of human behavior, the initial step is to carry out SFT, which finetunes a pretrained LLM on chat-style data, including both queries and responses. In the following sections, we will delve into the details of data construction and training methods. 3.1 Tiedot To enhance the capabilities of our supervised finetuning datasets, we have annotated conversations in multiple styles. While conventional datasets contain a vast amount of data prompted with questions, instructions, and answers in natural language, our approach takes it a step further by annotating human-style conversations. This practice, inspired by aims at improving the model’s helpfulness by focusing on natural language generation for diverse tasks. To ensure the model’s ability to generalize to a wide range of scenarios, we specifically excluded data formatted in prompt templates that could potentially limit its capabilities. Furthermore, we have prioritized the safety of the language model by annotating data related to safety concerns such as violence, bias, and pornography. (Wei et al., 2022a) Oulussa et al. (2022), In addition to data quality, we have observed that the training method can significantly impact the final performance of the model. To achieve this, we utilized the ChatML-style format which is a versatile meta language capable of describing both the metadata (such as roles) and the content of a turn. This format enables the model to effectively distinguish between various types of information, including system setup, user inputs, and assistant outputs, among others. By leveraging this approach, we can enhance the model’s ability to accurately process and analyze complex conversational data. (OpenAI, 2022), 3.1.2 Training Consistent with pretraining, we also apply next-token prediction as the training task for SFT. We apply the loss masks for the system and user inputs. More details are demonstrated in Section A.1.1. The model’s training process utilizes the AdamW optimizer, with the following hyperparameters: 1 pistettä 0* 2 set to 0. set to 10−8. The sequence length is limited to 2048, and the batch size is 128. The model undergoes a total of 4000 steps, with the learning rate gradually increased over the first 1430 steps, reaching a peak of 2 10−6. To prevent overfitting, weight decay is applied with a value of 0. . .*0. β 9, β 95, and ϵ × 1, drop-out on asetettu 0 1, and gradient clipping is enforced with a limit of 1 3.2 Reinforcement Learning from Human Feedback While SFT has proven to be effective, we acknowledge that its generalization and creativity capa-bilities may be limited, and it is prone to overfitting. To address this issue, we have implemented Reinforcement Learning from Human Feedback (RLHF) to further align SFT models with human preferences, following the approaches of This process involves training a reward model and using Proximal Policy Optimization (PPO) to conduct policy training. Ouyang et al. (2022); Kristinusko ja al. (2017). (Schulman et al., 2017) 3.2.1 Reward Model Jotta voit luoda onnistuneen palkitsemismallin, kuten rakentaa suuren kielimallin (LLM), on ratkaisevan tärkeää suorittaa ensin esikoulutus ja sitten hienosäätely. necessitates a vast dataset of comparison data. This dataset consists of sample pairs, each containing two distinct responses for a single query and their corresponding preferences. Similarly, finetuning is also conducted on this type of comparison data, but with a higher quality due to the presence of quality annotations. (Bai et al., 2022b), During the fine-tuning phase, we gather a variety of prompts and adjust the reward model based on human feedback for responses from the QWEN models. To ensure the diversity and complexity of user prompts are properly taken into account, we have created a classification system with around 6600 detailed tags and implemented a balanced sampling algorithm that considers both diversity and complexity when selecting prompts for annotation by the reward model To generate a wide range of responses, we have utilized QWEN models of different sizes and sampling strategies, as diverse responses can help reduce annotation difficulties and enhance the performance of the reward model. These responses are then evaluated by annotators following a standard annotation guideline, and comparison pairs are formed based on their scores. Lu et al. 2023). In creating the reward model, we utilize the same-sized pre-trained language model QWEN to initiate the process. It is important to mention that we have incorporated a pooling layer into the original QWEN model to extract the reward for a sentence based on a specific end token. The learning rate for this process has been set to a constant value of 3 10−6, and the batch size is 64. Additionally, the sequence length is set to 2048, and the training process lasts for a single epoch. × We adopted the accuracy on the test dataset as an important but not exclusive evaluation metric for the reward model. In Table we report the test pairwise accuracy of PMP and reward models on diverse human preference benchmark datasets Specifically, QWEN Helpful-base and QWEN Helpful-online are our proprietary datasets. The responses in QWEN Helpful-base are generated from QWEN without RLHF, whereas QWEN Helpful-online includes responses from QWEN with RLHF. The results show that the PMP model demonstrates high generalization capabilities on out-of-distribution data, and the reward model demonstrates significant improvement on our QWEN reward datasets. 4, (Bai et al., 2022b; Stiennon et al., 2020; Ethayarajh ja al, 2022; Lightman et al., 2023). 3.2.2 Reinforcement Learning Our Proximal Policy Optimization (PPO) process involves four models: the policy model, value model, reference model, and reward model. Before starting the PPO procedure, we pause the policy model’s updates and focus solely on updating the value model for 50 steps. This approach ensures that the value model can adapt to different reward models effectively. PPO-toiminnon aikana käytämme strategiaa, jossa otetaan samanaikaisesti kaksi vastausta kuhunkin kyselyyn.Tämä strategia on osoittautunut tehokkaammaksi sisäisten vertailuarviointien perusteella. Asetamme KL:n poikkeamaosuuden tasolle 0**04 ja normalisoimme palkkion kulkevan keskiarvon perusteella. The policy and value models have learning rates of 1 10−6 and 5 10−6, respectively. To enhance training stability, we utilize value loss clipping with a clip value of 0*. . .*0, there is a faster increase in reward, ultimately resulting in consistently higher evaluation rewards under similar conditions. × × 15. For inference, the policy top-p is set to 0 9. Our findings indicate that although the entropy is slightly lower than when top-p is set to 1 Additionally, we have implemented a pretrained gradient to mitigate the alignment tax. Empirical findings indicate that, with this specific reward model, the KL penalty is adequately robust to counteract the alignment tax in benchmarks that are not strictly code or math in nature, such as those that test common sense knowledge and reading comprehension. It is imperative to utilize a significantly larger volume of the pretrained data in comparison to the PPO data to ensure the effectiveness of the pretrained gradient. Additionally, our empirical study suggests that an overly large value for this coefficient can considerably impede the alignment to the reward model, eventually compromising the ultimate alignment, while an overly small value would only have a marginal effect on alignment tax reduction. 3.3 Automatic and Human Evaluation of Aligned Models To showcase the effectiveness of our aligned models, we conduct a comparison with other aligned models on well-established benchmarks, including MMLU C-Eval GSM8K HumanEval and BBH Laajasti käytetyn vähäkuvausasetuksen lisäksi testaamme yhdenmukaistettuja malleja nollakuvausasetuksessa osoittaaksemme, kuinka hyvin mallit noudattavat ohjeita. Nollakuvausasetuksen kehotus koostuu ohjeesta ja kysymyksestä ilman aiempia esimerkkejä asiayhteydessä. (Hendrycks et al., 2020), (Huang et al., Vuonna 2023 (Cobbe et al., Vuonna 2021 (Chen et al., 2021), (Suzgun et al., 2022). (OpenCompass Team, 2023). The results in Table demonstrate the effectiveness of our aligned models in understanding human instructions and generating appropriate responses. QWEN-14B-Chat outperforms all other models except ChatGPT (OpenAI, 2022) and LLAMA 2-CHAT-70B (Touvron et al., 2023b) in all datasets, including MMLU (Hendrycks et al., 2020), C-Eval (Huang et al., 2023), GSM8K (Cobbe et al., 2021), HumanEval (Chen et al., 2021), and BBH (Suzgun et al., 2022). 5 In particular, QWEN’s performance in HumanEval, which measures the quality of generated codes, is significantly higher than that of other open-source models. Moreover, QWEN’s performance is consistently better than that of open-source models of similar size, such as LLaMA2 ChatGLM2 InternLM and Baichuan2 This suggests that our alignment approach, which involves fine-tuning the model on a large dataset of human conversations, has been effective in improving the model’s ability to understand and generate human-like language./im (Häppäri ja muut) 2023b), (ChatGLM2 Team, 2023), (InternLM Team, 2023), (Hän et al. 2023). Despite this, we have reservations about the ability of traditional benchmark evaluation to accurately measure the performance and potential of chat models trained with alignment techniques in today’s landscape. The results mentioned earlier provide some evidence of our competitive standing, but we believe that it is crucial to develop new evaluation methods specifically tailored to aligned models. We believe that human evaluation is crucial, which is why we have created a carefully curated dataset for this purpose. Our process involved collecting 300 instructions in Chinese that covered a wide range of topics, including knowledge, language understanding, creative writing, coding, and mathematics. To evaluate the performance of different models, we chose the SFT version of QWEN-CHAT-7B and the SFT and RLHF versions of QWEN-CHAT-14B, and added two strong baselines, GPT-3.5 and GPT-4 , vertailua varten. Kunkin ohjeen osalta pyysimme kolmea kommenttihenkilöä luokittelemaan mallin vastaukset hyödyllisyyden, informatiivisuuden, pätevyyden ja muiden asiaankuuluvien tekijöiden kokonaispistemäärän perusteella. Tietokokonaisuutemme ja arviointimenetelmämme tarjoavat kattavan ja tiukan arvioinnin eri kielimallien kyvyistä eri aloilla. 4 Figure illustrates the win rates of the various models. For each model, we report the percentage of wins, ties, and losses against GPT-3.5, with the segments of each bar from bottom to top representing these statistics. The experimental results clearly demonstrate that the RLHF model outperforms the SFT models by significant margins, indicating that RLHF can encourage the model to generate responses that are more preferred by humans. In terms of overall performance, we find that the RLHF model significantly outperforms the SFT models, falling behind GPT-4. This indicates the effectiveness of RLHF for aligning to human preference. To provide a more comprehensive understanding of the models’ performance, we include a case study with examples from different models in Appendix Nonetheless, it remains difficult to accurately capture the gap between our models and the proprietary models. As such, a more extensive and rigorous assessment is required for the chat models. 4 A.2.2. 3.4 Tool Use, Code Interpreter, and Agent The QWEN models, which are designed to be versatile, have the remarkable ability to assist with (semi-)automating daily tasks by leveraging their skills in tool-use and planning. As such, they can serve as agents or copilots to help streamline various tasks. We explore QWEN’s proficiency in the following areas: • Utilizing unseen tools through ReAct prompting Katso pöytä (Yao et al., 2022) 6). • Using a Python code interpreter to enhance math reasoning, data analysis, and more (see Table and Table 7 8). • Functioning as an agent that accesses Hugging Face’s extensive collection of multimodal models while engaging with humans (see Table 9). To enhance QWEN’s capabilities as an agent or copilot, we employ the self-instruct strategy for SFT. Specifically, we utilize the in-context learning capability of QWEN for self-instruction. By providing a few examples, we can prompt QWEN to generate more relevant queries and generate outputs that follow a specific format, such as ReAct We then apply rules and involve human annotators to filter out any noisy samples. Afterwards, the samples are incorporated into QWEN’s training data, resulting in an updated version of QWEN that is more dependable for self-instruction. We iterate through this process multiple times until we gather an ample number of samples that possess both exceptional quality and a wide range of diversity. As a result, our final collection consists of around 2000 high-quality samples. (Hän et al. 2023c) (Yao et al., 2022). During the finetuning process, we mix these high-quality samples with all the other general-purpose SFT samples, rather than introducing an additional training stage. By doing so, we are able to retain essential general-purpose capabilities that are also pertinent for constructing agent applications. We have created and made publicly available a benchmark for evaluating QWEN’s ability to call plugins, tools, functions, or APIs using ReAct Prompting (see To ensure fair evaluation, we have excluded any plugins that were included in QWEN’s training set from the evaluation set. The benchmark assesses the model’s accuracy in selecting the correct plugin from a pool of up to five candidates, as well as the plausibility of the parameters passed into the plugin and the frequency of false positives. In this evaluation, a false positive occurs when the model incorrectly invokes a plugin in response to a query, despite not being required to do so. Using Tools via ReAct Prompting Qwen Team, Alibaba Group, 2023b). The results presented in Table demonstrate that QWEN consistently achieves higher accuracy in identifying the relevance of a query to the available tools as the model size increases. However, the table also highlights that beyond a certain point, there is little improvement in performance when it comes to selecting the appropriate tool and providing relevant arguments. This suggests that the current preliminary benchmark may be relatively easy and may require further enhancement in future iterations. It is worth noting that GPT-3.5 stands out as an exception, displaying suboptimal performance on this particular benchmark. This could potentially be attributed to the fact that the benchmark primarily focuses on the Chinese language, which may not align well with GPT-3.5’s capabilities. Additionally, we observe that GPT-3.5 tends to attempt to use at least one tool, even if the query cannot be effectively addressed by the provided tools. 6 The Python code interpreter is widely regarded as a powerful tool for augmenting the capabilities of an LLM agent. It is worth investigating whether QWEN can harness the full potential of this interpreter to enhance its performance in diverse domains, such as mathematical reasoning and data analysis. To facilitate this exploration, we have developed and made publicly available a benchmark that is specifically tailored for this purpose (see Using Code Interpreter for Math Reasoning and Data Analysis Qwen Team, Alibaba Group, 2023a). The benchmark encompasses three primary categories of tasks: math problem-solving, data visualization, and other general-purpose tasks like file post-processing and web crawling. Within the visualization tasks, we differentiate between two levels of difficulty. The easier level can be achieved by simply writing and executing a single code snippet without the need for advanced planning skills. However, the more challenging level requires strategic planning and executing multiple code snippets in a sequential manner. This is because the subsequent code must be written based on the output of the previous code. For example, an agent may need to examine the structure of a CSV file using one code snippet before proceeding to write and execute additional code to create a plot. Regarding evaluation metrics, we consider both the executability and correctness of the generated code. To elaborate on the correctness metrics, for math problems, we measure accuracy by verifying if the ground truth numerical answer is present in both the code execution result and the final response. When it comes to data visualization, we assess accuracy by utilizing QWEN-VL a powerful multimodal language model. QWEN-VL is capable of answering text questions paired with images, and we rely on it to confirm whether the image generated by the code fulfills the user’s request. (Bai et al., 2023), The results regarding executability and correctness are presented in Table and Table respectively. It is evident that CODE LLAMA generally outperforms LLAMA 2, its generalist counterpart, which is not surprising since this benchmark specifically requires coding skills. However, it is worth noting that specialist models that are optimized for code synthesis do not necessarily outperform generalist models. This is due to the fact that this benchmark encompasses various skills beyond coding, such as abstracting math problems into equations, understanding language-specified constraints, and responding in the specified format such as ReAct. Notably, QWEN-7B-CHAT and QWEN-14B-CHAT surpass all other open-source alternatives of similar scale significantly, despite being generalist models. 7 8, Hugging Face provides a framework called the Hugging Face Agent or Transformers Agent which empowers LLM agents with a curated set of multimodal tools, including speech recognition and image synthesis. This framework allows an LLM agent to interact with humans, interpret natural language commands, and employ the provided tools as needed. Serving as a Hugging Face Agent (Hugging Face, 2023), To evaluate QWEN’s effectiveness as a Hugging Face agent, we utilized the evaluation benchmarks offered by Hugging Face. The results are presented in Table The evaluation results reveal that QWEN performs quite well in comparison to other open-source alternatives, only slightly behind the proprietary GPT-4, demonstrating QWEN’s competitive capabilities. 9. 4 Code-Qwen: Specialized Model for Coding Training on domain-specific data has been shown to be highly effective, particularly in the case of code pretraining and finetuning. A language model that has been reinforced with training on code data can serve as a valuable tool for coding, debugging, and interpretation, among other tasks. In this work, we have developed a series of generalist models using pretraining and alignment techniques. Building on this foundation, we have created domain-specific models for coding by leveraging the base language models of QWEN, including continued pretrained model, CODE-QWEN and supervised finetuned model, CODE-QWEN-CHAT. Both models have 14 billion and 7 billion parameters versions. 4.1 Code Pretraining We believe that relying solely on code data for pretraining can result in a significant loss of the ability to function as a versatile assistant. Unlike previous approaches that focused solely on pretraining on code data we take a different approach by starting with our base models QWEN trained on a combination of text and code data, and then continuing to pretrain on the code data. We continue to pretrain the models on a total of around 90 billion tokens. During the pre-training phase, we initialize the model using the base language models QWEN. Many applications that rely on specialized models for coding may encounter lengthy contextual scenarios, such as tool usage and code interpretation, as mentioned in Section To address this issue, we train our models with context lengths of up to 8192. Similar to base model training in Section we employ Flash Attention in the attention modules, and adopt the standard optimizer AdamW setting 1 = 0*. 2 = 0. = 10−8. We set the learning rate as 6. 10−5 for CODE-QWEN-14B and 3.*0 10−5 CODE-QWEN-7B:lle, jossa on 3 prosentin lämpeneminen iteraatioilla ja ei oppimisnopeuden heikkenemistä. (Li et al., 2022; 2023d), (Rozie`re et al., 2023) 3.4. 2.4, (Työntekijät ja al. 2022) (Kingma & Ba, 2014; Loshchilov ja Hutter, 2017), β 9, β 95, and ϵ 0 × × 4.2 Code Supervised Fine-Tuning After conducting a series of empirical experiments, we have determined that the multi-stage SFT strategy yields the best performance compared to other methods. In the supervised fine-tuning stage, the model CODE-QWEN-CHAT initialized by the code foundation model CODE-QWEN are optimized by the AdamW optimizer ( 1 = 0*. 2 = 0.*95, = 10−8) with a learning rate of 2*. 10−6 and 1.*0 10−5 for the 14B and 7B model respectively. The learning rate increases to the peaking value with the cosine learning rate schedule (3% warm-up steps) and then remains constant. (Kingma & Ba, 2014; Loshchilov & Hutter, 2017) β 9, β ϵ 0 × × 4.3 Evaluation Our CODE-QWEN models have been compared with both proprietary and open-source language models, as shown in Tables and These tables present the results of our evaluation on the test sets of Humaneval MBPP and the multi-lingual code generation benchmark HUMANEVALPACK The comparison is based on the pass@1 performance of the models on these benchmark datasets. The results of this comparison are clearly demonstrated in Tables ja 10 11. (Chen et al., 2021), (Austin et al., 2021), (Muennighoff et al., 2023). 10 11. Our analysis reveals that specialized models, specifically CODE-QWEN and CODE-QWEN-CHAT, sig-nificantly outperform previous baselines with similar parameter counts, such as OCTOGEEX Käyttöohjeet5+ and CodeGeeX2 In fact, these models even rival the performance of larger models like Starcoder (Muen-nighoff et al., 2023), (Wang et al., 2023d), (Zheng et al., 2023). (Li et al., 2023d). When compared to some of the extremely large-scale closed-source models, CODE-QWEN and CODE-QWEN-CHAT demonstrate clear advantages in terms of pass@1. However, it is important to note that these models fall behind the state-of-the-art methods, such as GPT-4, in general. Nonetheless, with the continued scaling of both model size and data size, we believe that this gap can be narrowed in the near future. It is crucial to emphasize that the evaluations mentioned previously are insufficient for grasping the full extent of the strengths and weaknesses of the models. In our opinion, it is necessary to develop more rigorous tests to enable us to accurately assess our relative performance in comparison to GPT-4. 5 Math-Qwen: Specialized Model for Mathematics Reasoning We have created a mathematics-specialized model series called MATH-QWEN-CHAT, which is built on top of the QWEN pretrained language models. Specifically, we have developed assistant models that are specifically designed to excel in arithmetic and mathematics and are aligned with human behavior. We are releasing two versions of this model series, MATH-QWEN-14B-CHAT and MATH-QWEN-7B-CHAT, which have 14 billion and 7 billion parameters, respectively. 5.1 Training We carry out math SFT on our augmented math instructional dataset for mathematics reasoning, and therefore we obtain the chat model, MATH-QWEN-CHAT, directly. Owing to shorter average lengths of the math SFT data, we use a sequence length of 1024 for faster training. Most user inputs in the math SFT dataset are examination questions, and it is easy for the model to predict the input format and it is meaningless for the model to predict the input condition and numbers which could be random. Thus, we mask the inputs of the system and user to avoid loss computation on them and find masking them accelerates the convergence during our preliminary experiments. For optimization, we use the AdamW optimizer with the same hyperparameters of SFT except that we use a peak learning rate of 2 10−5 and a training step of 50 000. × 5.2 Arviointi We evaluate models on the test sets of GSM8K (Grade school math) MATH (Challenging competition math problems) Math401 (Arithmetic ability) ( and Math23K (Chinese grade school math) We compare MATH-QWEN-CHAT with proprietary models ChatGPT and Minerva and open-sourced math-specialized model RFT ( WizardMath and GAIRMath-Abel in Table MATH-QWEN-CHAT models show better math reasoning and arithmetic abilities compared to open-sourced models and QWEN-CHAT models of similar sizes. Compared to proprietary models, MATH-QWEN-7B-CHAT outperforms Minerva-8B in MATH. MATH-QWEN-14B-CHAT is chasing Minerva-62B and GPT-3.5 in GSM8K and MATH and delivers better performance on arithmetic ability and Chinese math problems. (Cobbe et al., 2021), (Hendrycks et al., 2021), Yuan et al., 2023b), (Wang et al., 2017). (Lewkowycz et al., 2022) Yuan et al., 2023a), (Luo et al., 2023a), (Pohjanmaan et al. 2023a) 12. 6 Related Work 6.1 Large Language Models The excitement of LLM began with the introduction of the Transformer architecture ( which was then applied to pretraining large-scale data by researchers such as These efforts led to significant success in transfer learning, with model sizes growing from 100 million to over 10 billion parameters Vaswani et al., 2017), Radford et al. (2018); Devlin et al. (2018); Liisa ja al. (2019). (Raffel et al., 2020; Shoeybi et al., 2019). In 2020, the release of GPT-3, a massive language model that is 10 times larger than T5, demonstrated the incredible potential of few-shot and zero-shot learning through prompt engineering and in-context learning, and later chain-of-thought prompting This success has led to a number of studies exploring the possibilities of further scaling these models As a result, the community has come to view these large language models as essential foundations for downstream models (Wei et al., 2022c). (Scao et al., 2022; Zhang et al., 2022; Du et al., 2021; Zeng et al., 2022; Lepikhin et al., 2020; Fedus et al., 2022; Du et al., 2022; Black et al., 2022; Rae et al., 2021; Hoffmann et al., 2022; Chowdhery et al., 2022; Thoppilan et al., 2022). (Bommasani et al., 2021). The birth of ChatGPT and the subsequent launch of GPT-4 marked two historic moments in the field of artificial intelligence, demonstrating that large language models (LLMs) can serve as effective AI assistants capable of communicating with humans. These events have sparked interests among researchers and developers in building language models that are aligned with human values and potentially even capable of achieving artificial general intelligence (AGI) (OpenAI, 2022) (OpenAI, 2023) (Anil et al., 2023; Anthropic, 2023a;b). One notable development in this area is the emergence of open-source LLMs, specifically LLaMA Tuli 2 which have been recognized as the most powerful open-source language models ever created. This has led to a surge of activity in the open-source community with a series of large language models being developed collaboratively to build upon this progress (Touvron et al., 2023a) (Touvron et al., 2023b), (Wolf et al., 2019), (Mosaic ML, 2023; Hämäläinen et al., 2023; ChatGLM2 Team, 2023; Yang et al., 2023; sisäinen tiimi, 2023). 6.2 Alignment The community was impressed by the surprising effectiveness of alignment on LLMs. Previously, LLMs without alignment often struggle with issues such as repetitive generation, hallucination, and deviation from human preferences. Since 2021, researchers have been diligently working on developing methods to enhance the performance of LLMs in downstream tasks Furthermore, researchers have been actively exploring ways to align LLMs with human instructions One major challenge in alignment research is the difficulty of collecting data. While OpenAI has utilized its platform to gather human prompts or instructions, it is not feasible for others to collect such data. (Wei et al., 2022a; Sanh et al., 2021; Longpre et al., 2023; Chung et al., 2022; Muennighoff et al., 2022). (Häppäri et al., 2022; Askell et al., 2021; Bai et al., 2022b;c). However, there has been some progress in this area, such as the self-instruct approach proposed in This innovative work offers a potential solution to the data collection problem in alignment research. As a result, there has been a surge in open-source chat data, including Alpaca MOSS Dolly Evol-Instruct and others Similarly, there has been an increase in open-source chat models, such as Alpaca Vicuna Guanaco MOSS WizardLM and others Wang et al. (2023c). (Taori et al., 2023), (Sun et al., 2023a), (Conover et al., 2023), (Xu et al., 2023b), (Sun et al., 2023b; Xu et al., 2023a;c; Chen et al., 2023c; Ding et al., 2023; Ji et al., 2023; Yang, 2023). (Taori et al., Vuonna 2023 (Chiang et al., Vuonna 2023 (Dettmers et al., 2023), (Sun et al., 2023a), (Xu et al., 2023b), (Xu et al., 2023c; Chen et al., 2023c; Ding et al., 2023; Wang et al. 2023b). To train an effective chat model, available solutions are mostly based on SFT and RLHF While SFT is similar to pretraining, it focuses on instruction following using the aforementioned data. However, for many developers, the limited memory capacity is a major obstacle to further research in SFT. As a result, parameter-efficient tuning methods, such as LoRA ., ja Q-Loira have gained popularity in the community. LoRA tunes only low-rank adapters, while Q-LoRA builds on LoRA and utilizes 4-bit quantized LLMs and paged attention In terms of RLHF, recent methods such as PPO have been adopted, but there are also alternative techniques aimed at addressing the complexity of optimization, such as RRHF ( DPO ja pro Despite the ongoing debate about the effectiveness of RLHF, more evidence is needed to understand how it enhances the intelligence of LLMs and what potential drawbacks it may have. (Ouyang et al., 2022). Hu et al 2021) (Dettmers et al., 2023), (Työntekijät et al., 2022; Frantar et al., 2022; Kwon et al., 2023). (Schulman et al., 2017; Touvron et al., 2023b) Yuan et al., 2023c), (Rafailov et al., 2023), (Song et al., 2023). 6.3 Tool Use and Agents LLM’s planning function allows for the invocation of tools, such as APIs or agent capabilities, through in-context learning, as demonstrated by introduced ReAct, a generation format that enables the model to generate thoughts on which tool to use, accept input from API observations, and generate a response. GPT-3.5 and GPT-4, when prompted with few shots, have shown consistent and impressive performance. In addition to tool usage, LLMs can utilize external memory sources like knowledge bases or search engines to generate more accurate and informative answers. This has led to the popularity of frameworks like LangChain The research on LLMs for tool use has also sparked interest in building agents with LLM capabilities, such as agents that can call different AI models embodied lifelong learning or multimodal agents and multiple agents interacting with each other and even building a micro-society Jääkiekko et al. (2023). Yao et al. (2022) (Hu et al., 2023; Zhong et al., 2023b) (Nakano et al., 2021; Liu et al., 2023b) (LangChain, Inc., 2023). (Shen et al., 2023; Li et al., 2023a), (Wang et al., 2023a; Driess et al., 2023), (Hän et al , 2023b; Li et al., 2023b; Xu et al., 2023d; Hong et al., 2023). 6.4 LLM for Coding Previous research has demonstrated that LLMs possess remarkable capabilities in code understanding and generation, particularly those with massive numbers of parameters Moreover, several LLMs have been pre-trained, continued pre-trained, or fine-tuned on coding-related data, which has resulted in significantly improved performance compared to general-purpose LLMs. These models include Codex AlphaCode SantaCoder Starcoder-Base InCoder CodeT5 koodix and CODE LLAMA In addition to these models, recent studies have focused on developing specialized alignment techniques for coding, such as Code Llama-Instruct and StarCoder Nämä mallit voivat auttaa kehittäjiä erilaisissa koodiin liittyvissä tehtävissä, mukaan lukien koodin tuottaminen. code completion code translation bug fixing code refinement and code question answering In a word, LLMs have the potential to revolutionize the field of coding by providing developers with powerful tools for code comprehension, generation, and related tasks. (Häppäri ja muut) 2022; Anil et al., 2023; Rae et al., 2021; Hoffmann et al., 2022). Chen et al. (2021), (Li et al. ) 2022), (Allal et al., 2023), (Li et al., 2023d), (Fried et al., 2022), (Wang et al., 2021), (Hahmo ja al. Vuonna 2023 (Rozie`re et al., 2023). (Rozie`re et al., 2023) (Li et al., 2023d). (Chen et al., 2021; Austin et al., 2021), (Zhang et al., 2023a), (Szafraniec et al., 2023), (Muennighoff et al., 2023), (Liu et al., 2023c), (Liu & Wan, 2021). 6.5 LLM for Mathematics LLMs with a certain model scale have been found to possess the ability to perform mathematical reasoning In order to encourage LLMs to achieve better performance on math-related tasks, researchers have employed techniques such as chain-of-thought prompting ja Scratchpad which have shown promising results. Additionally, self-consistency and least-to-most prompting have further improved the performance of these models on these tasks. However, prompt engineering is a time-consuming process that requires a lot of trial and error, and it is still difficult for LLMs to consistently perform well or achieve satisfactory results in solving mathematical problems. Moreover, simply scaling the data and model size is not an efficient way to improve a model’s mathematical reasoning abilities. Instead, pretraining on math-related corpora has been shown to consistently enhance these capabilities Additionally, fine-tuning on math-related instruction-following datasets has also been effective and more cost-effective than math-specific pretraining. Despite their limitations in terms of accuracy, LLMs still have significant potential to assist users with practical mathematical problems. There is ample scope for further development in this area. (Wei et al., 2022b; Suzgun et al., 2022). (Wei et al., 2022c) (Nye et al., 2021), (Wang et al., Vuonna 2022) (Zhou et al., 2022) (Hendrycks et al., 2021; Lewkowycz et al., Vuonna 2022 Taylor et al., 2022; Lightman et al., 2023). (Si et al., 2023; Yuan et al., Vuonna 2023a Luo et al., 2023a; Yue et al., 2023; Chern et al., 2023a; Jaakko ja al. 2023), 7 Conclusion In this report, we present the QWEN series of large language models, which showcase the latest advancements in natural language processing. With 14B, 7B, and 1.8B parameters, these models have been pre-trained on massive amounts of data, including trillions of tokens, and fine-tuned using cutting-edge techniques such as SFT and RLHF. Additionally, the QWEN series includes specialized models for coding and mathematics, such as CODE-QWEN, CODE-QWEN-CHAT, and MATH-QWEN-CHAT, which have been trained on domain-specific data to excel in their respective fields. Our results demonstrate that the QWEN series is competitive with existing open-source models and even matches the performance of some proprietary models on comprehensive benchmarks and human evaluation. We believe that the open access of QWEN will foster collaboration and innovation within the community, enabling researchers and developers to build upon our work and push the boundaries of what is possible with language models. By providing these models to the public, we hope to inspire new research and applications that will further advance the field and contribute to our understanding of the variables and techniques introduced in realistic settings. In a nutshell, the QWEN series represents a major milestone in our development of large language models, and we are excited to see how it will be used to drive progress and innovation in the years to come. 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(Bai et al., Vuonna 2022 b) Sen sijaan käännyimme OpenAI:n ehdottamaan ChatML-muotoon. Tämä muoto mahdollistaa erityisten tokenien käytön, ts. ” ” and “ ”, jotka eivät näy esikoulutuksessa, ja siten ratkaista edellä mainittu ongelma. 5 A.2 Arviointi A.2.1 Automaattinen arviointi Antaaksemme kokonaiskuvan QWEN-mallisarjan suorituskyvystä tässä osiossa kuvataan yksityiskohtaisesti malliemme suorituskykyä sekä lähtökohdat kattavassa vertailuarvioinnissa, jonka ehdottaa We report the results in multiple tables based on the officially provided categories, including examination, language, knowledge, understanding, and reasoning. In terms of the performance of the baseline models, we report the higher results between the reported ones and those on the leaderboard. OpenCompass Team (2023). Here we evaluate the models on a series of datasets relevant to the examination. The datasets include: Examination Massive Multi-task Language Understanding is designed for measuring language understanding capabilities. We report 5-shot results. MMLU (Hendrycks et al., 2020) C-Eval is a Chinese evaluation dataset spanning 52 diverse disciplines. We report 5-shot results. C-Eval (Huang et al., 2023) CMMLU (Li et al., 2023c) CMMLU on suunniteltu arvioimaan kiinan kielen ymmärtämisen kykyjä. This is a benchmark consisting of human-centric examina-tions, including college entrance exams, law school admission tests, math competitions, and lawyer qualification tests. We report zero-shot results. AGIEval (Zhong et al., 2023a) This is a benchmark with Gaokao (Chinese college-entrance examination) questions. We report zero-shot results. Gaokao-Bench (Zhang et al., 2023b) ARC is a dataset consisting of grade-school level, multiple-choice science questions. It includes an easy set and a challenge set, which are referred by ARC-e and ARC-c. We report zero-shot results. ARC (Clark et al., 2018) In terms of MMLU, we report the detailed results in Table C-Evalin osalta raportoimme tuloksista taulukossa Muiden tietokokonaisuuksien osalta raportoimme tuloksista taulukossa Huomaa, että AGIEval sisältää kiinan ja englannin osat, kun taas LLAMA 2 raportoi tuloksista vain englanninkielisessä osassa, joten käytämme OpenCompassin tuloksia. 13. 14. 15. Additionally, while CMMLU, AGIEval, and Gaokao-Bench are related to Chinese, and MPT, Falcon, and the LLaMA series were not optimized for Chinese, these models achieved low performance on the datasets. Here we evaluate the models on a series of datasets relevant to knowledge and natural language understanding. The datasets include Knowledge and Understanding BoolQ (Clark et al., 2019) Tämä on QA-tietokokonaisuus, jossa kysymykset koskevat Wikipedian kohtia, ja mallin pitäisi vastata kyllä tai ei annettuun mahdolliseen vastaukseen. CommonsenseQA (Talmor et al., 2019) Tämä on monivalintakysymyksiin vastaava tietokokonaisuus, joka arvioi järjen tietämyksen ymmärtämistä. NaturalQuestions (Kwiatkowski et al., 2019) Se on QA: n tietokokonaisuus, jossa kysymykset ovat käyttäjiltä ja vastaukset tarkistavat asiantuntijat. (P This is dataset to evaluate language understanding by word prediction. It consists of passages related to human subjects. We report zero-shot results. LAMBADA aperno et al., 2016) Tulokset ilmoitetaan taulukossa 16. Ilmoitamme arvioinnin tuloksista perusteluihin liittyvissä tietokokonaisuuksissa keskittymällä luonnonkieliseen perusteluun. Muille, kuten matematiikkaan ja koodaukseen, kuten olemme havainnollistaneet yksityiskohtaisesti, emme raportoi näistä tuloksista toistuvasti. Reasoning HellaSwag (Zellers et al., 2019) Tämä on järjettömän luonnollisen kielen johtopäätöksen (NLI) tietokokonaisuus, jossa kysymykset ovat helppoja ihmisille, mutta kamppailevat aiempien kielimallien kanssa. PIQA (Bisk et al., 2020) Tämä on NLI-tietokokonaisuus, joka arvioi fyysistä tietämystä. SIQA (Sap et al., 2019) Tämä on NLI-tietokokonaisuus, joka arvioi sosiaalisen järjen älykkyyttä. OCNLI (Hu et al., 2020) Tämä on NLI-tietokokonaisuus, joka keskittyy kiinalaisiin. Tulokset ilmoitetaan taulukossa 17. A.2.2 Human Evaluation Tässä osassa esittelemme ihmisen analyysin tapauksia.Omavalmistetuissa arviointitietokannoissamme ohjeet ovat joko manuaalisesti kirjoitettuja tietoja tai manuaalisesti tarkistettuja julkisia tietokantoja, kuten CLiB Jäsen C-Eval FacTool LeetCode ) ja niin edelleen 6 (Hän et al. Vuonna 2023 (Pohjanmaan et al. Vuonna 2023 b) 7 Kussakin tapauksessa osoitamme vastaukset ja Elo-luokitukset Erityisesti, koska inhimillisessä arvioinnissamme olevat tiedot ovat kiinalaisia, tarjoamme myös englanninkielisiä käännöksiä. 8 A.3 Koodin tulkitsijan analyysi Here we provide a case of comparison between CODE LLAMA and QWEN-CHAT. This case demonstrates the advantages of QWEN-CHAT in processing tabular data and performing complex tasks. Tämä artikkeli on saatavilla arkivissä CC by 4.0 Deed (Attribution 4.0 International) -lisenssillä. Tämä paperi on Käyttöoikeus on CC by 4.0 Deed (Attribution 4.0 International). available on arxiv