| Algorithm | Domain | Arch | Encoder | Inputs | Len | RL |
| Duan et al. (2016) | Meta RL | separate | GRU | oard | 1000 | TRPO,PPO |
| Wang et al. (2017) | Meta RL | shared | LSTM | oart | 5-150 | A2C |
| Baseline from Rakelly et al. (2019) | Meta RL | separate | GRU | oard | 100 | PPO |
| Baseline from Zintgraf et al. (2020) | Meta RL | separate | GRU | oard | Max | A2C,PPO |
| Baseline from Fakoor et al. (2020) | MetaRL | separate | GRU | oar | 10-25 | TD3 |
| Baseline from Yu et al. (2019) | Meta RL | separate | GRU | oard | 500 | PPO |
| Kostrikov (2018) | POMDP | shared | GRU | 0 | 5-2048 | PPO,A2C |
| Wang et al. (2020) | POMDP | separate | LSTM | oa | 150 | TD3, SAC |
| Meng et al. (2021) | POMDP | separate | LSTM | oa | 1-5 | TD3 |
| Yang & Nguyen (2021) | POMDP | separate | both | oa | Max | TD3, SAC |
| Baseline from Igl et al. (2018) | POMDP | shared | GRU | oa | 25 | A2C |
| Baseline from Han et al. (2020) | POMDP | shared | LSTM | 0 | 64 | SAC |
| Baseline from Zhang et al. (2021) | Robust RL | separate | LSTM | 0 | 100 | PPO |
| Baseline1 from Packer et al. (2018) | Generalization | shared | LSTM | 0 | 128-512 | PPO,A2C |
| Baseline2 from Packer et al. (2018) | Generalization | separate | LSTM | oard | 128-512 | PPO,A2C |
| Our method | Meta RL | separate | LSTM | oard | 64 | TD3 |
| Our method | POMDP | separate | GRU | oa | 64 | TD3 |
| Our method | Robust RL | separate | LSTM | 0 | 64 | TD3 |
| Our method | Generalization | separate | LSTM | 0 | 64 | TD3 |
+
+Model-free RL Algorithms. Recurrent model-free RL can be understood as applying an off-theshelf model-free RL algorithm with an actor and a Q function parametrized to take sequences of inputs. As such, the choice of the underlying model-free RL algorithm is paramount. Most prior work on continuous control POMDP problems used on-policy algorithms, such as A2C (Mnih et al., 2016), TRPO (Schulman et al., 2015) or PPO (Schulman et al., 2017). While off-policy algorithms such as TD3 (Fujimoto et al., 2018) and SAC (Haarnoja et al., 2018a;b) greatly improve the performance in continuous control MDP problems in terms of sample efficiency and asymptotic performance, these methods are rarely used in recurrent model-free RL baselines (Rakelly et al., 2019; Zhang et al., 2020; Zintgraf et al., 2020). In the experiment section (Sec. 5.1), we will show that using these off-policy algorithms for recurrent model-free RL provides results that are better than using on-policy algorithms and are comparable to their specialized methods in POMDP. This echoes the finding that model-free off-policy TD3-Context (Fakoor et al., 2020) can be better than the specialized method PEARL (Rakelly et al., 2019) in meta RL.
+
+RNN Variants and Context Length. RNN training is known to be unstable, especially with long sequences input (Bengio et al., 1994). The RNN variants like LSTM (Hochreiter & Schmidhuber, 1997) and GRU (Chung et al., 2014) mitigate the training issues, but still may fail to learn long-term dependencies (Trinh et al., 2018). In POMDP problems, these dependencies reflect the memory that an agent must have to solve a task. For example, a POMDP that hides velocities from observations theoretically requires a short memory length to infer velocities through consecutive positions (Meng et al., 2021). Prior work in POMDPs choose a variety set of context lengths for RNNs from 1 to 2048 (see the “Len” column of Table 2), and we select three representatives of short (5), medium (64), and long length (larger than 100) in the experiments (Sec. 5) for comparison. We also try both LSTM and GRU as RNN variants to compare their performance. We find that the optimal context length and RNN variant are task-specific (see Sec. 5.2).
+
+# 5 EXPERIMENTS
+
+Our experiments aim to answer two questions. First, how does a well-tuned implementation of recurrent model-free RL compare to specialized POMDP methods, such as purpose-designed meta RL and robust RL algorithms? To give these prior methods the strongest possible footing, we will compare prior methods on the specific problem types for which they were developed (i.e., meta RL algorithms were tested on meta RL tasks). Our second question studies which design decisions are essential for recurrent model-free RL. We put the environment details in Appendix D.
+
+Code Implementation. We release a modular and highly-configurable implementation of recurrent (off-policy) model-free RL: url. Our implementation is efficient in terms of computer memory compared to previous off-policy RL methods for POMDPs $2 0 0 \mathrm { x }$ less RAM than Han et al. (2020) and $9 \mathbf { x }$ less GPU memory than Dorfman et al. (2020)). Please see the appendix A for details, including an explanation of why our implementation is more memory-efficient than prior work.
+
+# 5.1 RECURRENT MODEL-FREE RL IS COMPARABLE WITH PRIOR SPECIALIZED METHODS
+
+While prior work has studied a range of different POMDP settings (e.g., meta RL, occluded observations), recurrent model-free RL is a ubiquitous baseline (Han et al., 2020; Humplik et al., 2019; Igl et al., 2018; Rakelly et al., 2019; Zintgraf et al., 2020). However, prior work consistently report that this baseline is reported to be unperformed to more specialized methods. This section casts doubt on that claim, showing that a well-tuned implementation of recurrent model-free RL can perform at least as well as more specialized methods.
+
+We study four subareas of POMDPs: the “standard” POMDP, meta RL, robust RL, and generalization in RL. We tune a wide range of decision factors shown in Sec. 4 in our implemented recurrent model-free RL. Appendix A.3 shows the details of the tuning options. For each subarea, we show the performance of a single variant that works best across the environments in that subarea, compared with the prior specialized methods in this subsection. In other words, the following plots of each subarea report the same model-free recurrent RL algorithm with the same hyperparameters. The exact configurations of each subarea can be found in the last four rows of Table 2. Under this restricted setting, we find that our implementation can actually outperform prior (specialized) methods by a wide margin across the four subareas.
+
+For each plot of learning curves, we show three approaches as reference. First, an Oracle policy has access to the POMDP hidden states, turning the POMDP into an MDP; this policy should therefore be treated as an upper bound on the performance that any POMDP method should receive. Second, as a lower bound, we use a Markovian policy to solve the POMDP. Both Oracle policy and Markovian policy are trained with the same hyperparameters as our recurrent model-free RL implementation. Third, we add a Random policy, which represents a trivial lower bound. We show the full learning curves in Appendix E.1 due to the space limit. See Appendix B for details about the implementation of these comparisons.
+
+“Standard” POMDP. Our first experiments look at the “standard” POMDPs that typically occlude some part of states in the environment. We will compare against VRM (Han et al., 2020), a recent state-of-the-art model-based POMDP algorithm. We directly apply the environment design of VRM paper that occludes either positions&angles or velocities of the simulated robot in PyBullet (Coumans & Bai, 2016). There are 8 environments {Hopper, Ant, Walker, Cheetah}- $\{ \mathrm { P } , \mathrm { V } \}$ , where “-P” stands for observing positions&angles only, and “-V” stands for observing velocities only. Fig. 1 and Fig. 18 in appendix show that the best single variant of our model-free recurrent RL implementation outperform VRM in 6 out of 8 environments, especially in {Cheetah,Hopper}- $\{ \mathrm { P } \}$ (over $8 0 \%$ of the Oracles). Our results suggest that, while the variational dynamics model used by VRM may be useful for some tasks, a simple recurrent model-free RL baseline can outperform VRM if properly tuned. While we are primarily interested in sample complexity, but not compute, it is worth noting that our recurrent model-free RL implementation is substantially more efficient than the open-source VRM implementation, training $5 \times$ faster and requiring at most $2 0 0 \times$ less RAM usage (see Appendix A).
+
+Meta RL. We next compare the recurrent model-free RL to the meta RL setting, where some indicator of the task is unobserved. We compare our implementation of recurrent model-free RL to a specialized state-of-the-art method, VariBAD (Zintgraf et al., 2020) that explicitly learns the task embeddings by variational model-based objectives. As suggested by Dorfman et al. (2020), we modified VariBAD to use SAC instead of PPO. This change also allows us for fair comparison with our implementation of recurrent model-free RL, which uses SAC and TD3. We adopt the three environments used in Dorfman et al. (2020) for experiments, including Semi-Circle and CheetahVel, and we also adapt Wind to make it harder to solve. Figure 2 shows that our best single variant outperforms VariBAD and even reaches Oracles in the two meta RL environments, Cheetah-Vel and Wind, leaving Semi-Circle in the appendix. Prior work (Rakelly et al., 2019; Zintgraf et al., 2020) show that disentangling task inference and control can stabilize training. However, our experiments suggest that joint training of task inference and control could also have comparable performance if well implemented. Additionally, because recurrent model-free RL is trained end-to-end, without using pre-trained task representations saved in the replay buffer like the off-policy VariBAD (Dorfman et al., 2020), our implementation does not have non-stationarity issue in task representations.
+
+
+Figure 2: Learning curves on two meta RL environments. The single best variant of our implementation on recurrent model-free RL can surpass the specialized meta RL method off-policy VariBAD (Dorfman et al., 2020), and match the performance of an “Oracle” policy that gets to observe the hidden state.
+
+
+Figure 3: Learning curves on one robust RL environment, Cheetah-Robust. We show the average returns (left figure) and worst returns (right figure) of each method. The single best variant of our implementation on recurrent model-free RL can greatly outperform the specialized robust RL method MRPO (Jiang et al., 2021), and surpass the Oracle that are trained with same simulation and gradient steps.
+
+Robust RL. Thirdly, we focus on the robust RL that aims to maximize the worst returns over the tasks. We choose the recent specialized algorithm MRPO (Jiang et al., 2021) as the compared method, and adopt their used environments based on SunBlaze benchmark (Packer et al., 2018). These environments have hidden states that are fixed during one episode, including the density and the friction coefficients of the simulated robots, namely {Cheetah, Hopper, Walker $\}$ -Robust. Fig. 3 shows both the average return and worst return of our single best variant and MRPO on the three environments, where the worst return is measured by the average return in the worst $1 0 \%$ testing tasks following the practice in Jiang et al. (2021). The results are quite surprising: although our implementation, using average-case RL objective and without access to the hidden states, is not expected to surpass MRPO and Oracle with access to hidden states in worst return, we found that our best variant vastly outperforms the specialized MRPO and Oracle in both average return and worst return, with over $8 0 \%$ fewer simulation steps. Our implementation benefits from its memory and off-policy algorithms, while MRPO might suffer from its Markovian on-policy algorithm and a bit ideal Lipschitz assumption in dynamics. Nevertheless, our implementation is around $1 7 . 5 \mathrm { x }$ slower than MRPO given the same simulation steps (see Appendix A), so we only run it with 3M steps with a limited time budget.
+
+Generalization in RL. Finally, we focus on the SunBlaze benchmark from Packer et al. (2018) for investigating generalization in RL, including {Hopper, Cheetah}-Generalize. We pick the best specialized method in the tables of final performance in Packer et al. (2018), Markovian on-policy robust RL method EPOpt-PPO-FF (Rajeswaran et al., 2017a). Fig. 4 show the interpolation and extrapolation success rates in one environment, where in the interpolation the testing tasks have same distribution of hidden states as that of the training tasks, while in the extrapolation the testing distribution is disjoint from that of training. We can see that our model-free method is on par with the EPOpt-PPO-FF in the interpolation benchmark, while EPOpt-PPO-FF requires access to the dynamics parameters but ours does not. In the extrapolation benchmark, our method greatly outperforms the previous method, although our objective does not consider extrapolation.
+
+
+Figure 4: Learning curves on generalization in one RL environment, Hopper-Generalize. We show the interpolation success rates (left figures) and extrapolation success rates (right figures) of each method. The single best variant of our implementation on recurrent model-free RL can be par with the specialized method EPOpt-PPO-FF (Rajeswaran et al., 2017a) in interpolation and outperform it in extrapolation. The data of EPOpt-PPO-FF and A2C-RC (a recurrent model-free on-policy RL method) are copied from the Table 7 & 8 in Packer et al. (2018).
+
+Overall, we can see that with careful tuning on recurrent model-free RL, it can at least perform as well as the specialized or more complicated methods, in various kinds of POMDPs.
+
+# 5.2 WHAT MATTERS IN RECURRENT MODEL-FREE RL ALGORITHMS?
+
+In the previous subsection, we showed that recurrent model-free RL can perform on par with the specialized (state-of-the-art) methods, then a natural question comes: Why our implementation of recurrent model-free RL outperforms the implementation used in prior work?
+
+Our analysis will focus on ablating the five important design decisions introduced in Sec. 4: the actor-critic architecture (Arch), the policy input space (Inputs), the underlying model-free RL algorithm (RL), the RNN encoder (Encoder), and the RNN context length (Len). See Table. 2 for a summary of how prior work made these design decisions. Due to the space limit, we show the ablation results in some but not all the environments to compare the performance between the best single variant and the other variant that only differs in one decision factor. We also provide “single factor analysis” plots for each decision factor by averaging the performance over the other factors in Appendix E.2.
+
+Recurrent Off-Policy Actor-Critic Architecture. First, we ran some experiments with both shared and separate architectures on two toy POMDP environments. Fig. 5 show the results in one of them (see Appendix E.3 for the other). We can see that the shared architecture failed to learn, compared to the separate architecture. The large RNN gradient norm in the shared architecture suggests that the actor and critic losses may cause conflicts in gradient update. Our results echo prior work (Fakoor et al., 2020; Meng et al.,
+
+
+Figure 5: Comparison between shared and separate recurrent actor-critic architecture with all the other hyperparameters same, on Semi-Circle, a toy meta RL environment. We show the performance metric (left) and also gradient norm of the RNN encoder(s) (right, in log-scale). For the separate architecture, :critic and :actor refer to the separate RNN in critic and actor networks, respectively.
+
+2021; Sun et al., 2021) that only consider separate RNN encoders that can achieve high asymptotic rewards, and also echo that (Han et al., 2020) shows poor results in the shared architecture of SAC-LSTM.
+
+Policy Input Space. The 1st row of Table 3 shows the effect of policy input space in a POMDP environment Walker-P. The reward signals could help reveal the missing information of the velocity of the robot base, which is occluded in Walker-P. Similarly, the single factor analysis on policy input in Fig. 12 shows that oar is among the best in “-P” environments. Therefore, it is reasonable that adding previous rewards into policy inputs can increase the performance.
+
+Table 3: Ablation results in our implementation of recurrent model-free RL. In this table, we show how a single change in one decision factor from the variant that is best on average in that subarea, could significantly increase the performance. The first column shows how we change the single decision factor, and the last column shows the performance comparison between the best variant in that subarea (left) and the ablated one (right). For robust RL and generalization in RL, we show the performance metric in worst returns and extrapolation success rates, respectively.
+
+ | | Meta-RL* POMDP Robust RL Gen. in RL |
| Hiddenlayersize | Observ.encoderPrev. Action encoder*Reward encoder*RNNMLP | [32][16][16][128][128,128,128] [256,256] |
| RLhparams | Learning rateDiscount factor ySmoothing coef T SAC temperatureTD3 noisesReplay buffer sizeBatch size | 3e-40.990.005 automatically updated by Haarnoja et al. (2018b)default values from Fujimoto et al. (2018)1e632 64 |
| Traininghparams | Environment stepsGradient steps | 5M 1.5M 3M0.1M 1.5M 0.6M |
| Policyinputs | Largest input spaceBest input space | oard oar oa oaroard oa 0 0 |
+
+Table 6: Settings of the specialized methods we compared in the main paper. For Meta-RL, we take the model on Cheetah-Vel as example.
+
+| Algorithm | Domain | Arch | Encoder | Inputs | Len | RL |
| Duan et al. (2016) | Meta RL | separate | GRU | oard | 1000 | TRPO,PPO |
| Wang et al. (2017) | Meta RL | shared | LSTM | oart | 5-150 | A2C |
| Baseline from Rakelly et al. (2019) | Meta RL | separate | GRU | oard | 100 | PPO |
| Baseline from Zintgraf et al. (2020) | Meta RL | separate | GRU | oard | Max | A2C,PPO |
| Baseline from Fakoor et al. (2020) | MetaRL | separate | GRU | oar | 10-25 | TD3 |
| Baseline from Yu et al. (2019) | Meta RL | separate | GRU | oard | 500 | PPO |
| Kostrikov (2018) | POMDP | shared | GRU | 0 | 5-2048 | PPO,A2C |
| Wang et al. (2020) | POMDP | separate | LSTM | oa | 150 | TD3, SAC |
| Meng et al. (2021) | POMDP | separate | LSTM | oa | 1-5 | TD3 |
| Yang & Nguyen (2021) | POMDP | separate | both | oa | Max | TD3, SAC |
| Baseline from Igl et al. (2018) | POMDP | shared | GRU | oa | 25 | A2C |
| Baseline from Han et al. (2020) | POMDP | shared | LSTM | 0 | 64 | SAC |
| Baseline from Zhang et al. (2021) | Robust RL | separate | LSTM | 0 | 100 | PPO |
| Baseline1 from Packer et al. (2018) | Generalization | shared | LSTM | 0 | 128-512 | PPO,A2C |
| Baseline2 from Packer et al. (2018) | Generalization | separate | LSTM | oard | 128-512 | PPO,A2C |
| Our method | Meta RL | separate | LSTM | oard | 64 | TD3 |
| Our method | POMDP | separate | GRU | oa | 64 | TD3 |
| Our method | Robust RL | separate | LSTM | 0 | 64 | TD3 |
| Our method | Generalization | separate | LSTM | 0 | 64 | TD3 |
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| Duan et al. (2016) | Meta RL | separate | GRU | oard | 1000 | TRPO,PPO |
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| Baseline from Rakelly et al. (2019) | Meta RL | separate | GRU | oard | 100 | PPO |
| Baseline from Zintgraf et al. (2020) | Meta RL | separate | GRU | oard | Max | A2C,PPO |
| Baseline from Fakoor et al. (2020) | MetaRL | separate | GRU | oar | 10-25 | TD3 |
| Baseline from Yu et al. (2019) | Meta RL | separate | GRU | oard | 500 | PPO |
| Kostrikov (2018) | POMDP | shared | GRU | 0 | 5-2048 | PPO,A2C |
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| Meng et al. (2021) | POMDP | separate | LSTM | oa | 1-5 | TD3 |
| Yang & Nguyen (2021) | POMDP | separate | both | oa | Max | TD3, SAC |
| Baseline from Igl et al. (2018) | POMDP | shared | GRU | oa | 25 | A2C |
| Baseline from Han et al. (2020) | POMDP | shared | LSTM | 0 | 64 | SAC |
| Baseline from Zhang et al. (2021) | Robust RL | separate | LSTM | 0 | 100 | PPO |
| Baseline1 from Packer et al. (2018) | Generalization | shared | LSTM | 0 | 128-512 | PPO,A2C |
| Baseline2 from Packer et al. (2018) | Generalization | separate | LSTM | oard | 128-512 | PPO,A2C |
| Our method | Meta RL | separate | LSTM | oard | 64 | TD3 |
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| Our method | Generalization | separate | LSTM | 0 | 64 | TD3 |
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| Hiddenlayersize | Observ.encoderPrev. Action encoder*Reward encoder*RNNMLP | [32][16][16][128][128,128,128] [256,256] |
| RLhparams | Learning rateDiscount factor ySmoothing coef T SAC temperatureTD3 noisesReplay buffer sizeBatch size | 3e-40.990.005 automatically updated by Haarnoja et al. (2018b)default values from Fujimoto et al. (2018)1e632 64 |
| Traininghparams | Environment stepsGradient steps | 5M 1.5M 3M0.1M 1.5M 0.6M |
| Policyinputs | Largest input spaceBest input space | oard oar oa oaroard oa 0 0 |
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+ "html": " | Universal constant C. |
| 2:Initialization: Set VH+1(-） ← 0. |
| 3:forh=H,H-1,...,1do |
| 4: | Phase1: Regular Least-square Value Iteration for conditional variances K |
| 5: | SetΣ← M (sh,ah）(sh,ah）+λI T=1 |
| 6: | 1∑=1Φ(sh,aT）.Vn+1(Sh+1）² K |
| 7: | 1∑=1(sh,aT）·Vn+1(Sh+1) |
| 8: | Set[VarnV+1](,)=<Φ(,),n)[(H+1)2]-[Φ(,)，0){0H+² |
| 9: | Setδh(-,)² ← max{1, Var pn Vh+1(-,)} |
| 10: | Phase2: Weighted Least-square Value Iteration for pessimistic updates |
| 11: | |
| 12: | SetWh←An1(∑=1(sh,ah）.(rh+Vn+1(sh+1))/²(sh,a)) |
| 13: | SetTh(-,)←C√a.((,.)TA¹Φ(,)) 1/2 2Hd (Use Ih for the improved version) + K |
| 14: | Set Qn(·,·)←Φ(.,·)Twh-Ih(·,·) |
| 15: | Set Qn(-,)←min{Qh(-,-),H-h+1}+ |
| 16: 17: end for | Setπn(-1-)← argmaxπh {Qn(,·),πh(-1·)>A,V(-)← maxπn{Qh(,),πh(- |-)>A |
| 18: |
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diff --git a/parse/dev/OUIFPHEgJU/OUIFPHEgJU.md b/parse/dev/OUIFPHEgJU/OUIFPHEgJU.md
new file mode 100644
index 0000000000000000000000000000000000000000..a83611895a12f548b089b5ad782e0d6530dcbb11
--- /dev/null
+++ b/parse/dev/OUIFPHEgJU/OUIFPHEgJU.md
@@ -0,0 +1,645 @@
+# QLORA: Efficient Finetuning of Quantized LLMs
+
+# Tim Dettmers∗
+
+Artidoro Pagnoni∗
+
+Ari Holtzman
+
+# Luke Zettlemoyer
+
+University of Washington {dettmers,artidoro,ahai,lsz}@cs.washington.edu
+
+# Abstract
+
+We present QLORA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLORA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA). Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching $9 9 . 3 \%$ of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU. QLORA introduces a number of innovations to save memory without sacrificing performance: (a) 4-bit NormalFloat (NF4), a new data type that is information theoretically optimal for normally distributed weights (b) Double Quantization to reduce the average memory footprint by quantizing the quantization constants, and (c) Paged Optimizers to manage memory spikes. We use QLORA to finetune more than 1,000 models, providing a detailed analysis of instruction following and chatbot performance across 8 instruction datasets, multiple model types (LLaMA, T5), and model scales that would be infeasible to run with regular finetuning (e.g. 33B and 65B parameter models). Our results show that QLoRA finetuning on a small high-quality dataset leads to state-of-the-art results, even when using smaller models than the previous SoTA. We provide a detailed analysis of chatbot performance based on both human and GPT-4 evaluations showing that GPT-4 evaluations are a cheap and reasonable alternative to human evaluation. Furthermore, we find that current chatbot benchmarks are not trustworthy to accurately evaluate the performance levels of chatbots. A lemon-picked analysis demonstrates where Guanaco fails compared to ChatGPT. We release all of our models and code, including CUDA kernels for 4-bit training.2
+
+# 1 Introduction
+
+Finetuning large language models (LLMs) is a highly effective way to improve their performance, [40, 63, 43, 62, 60, 37] and to add desirable or remove undesirable behaviors [43, 2, 4]. However, finetuning very large models is prohibitively expensive; regular 16-bit finetuning of a LLaMA 65B parameter model [58] requires more than $7 8 0 ~ \mathrm { G B }$ of GPU memory. While recent quantization methods can reduce the memory footprint of LLMs [14, 13, 18, 67], such techniques only work for inference and break down during training [66].
+
+We demonstrate for the first time that it is possible to finetune a quantized 4-bit model without any performance degradation. Our method, QLORA, uses a novel high-precision technique to quantize a pretrained model to 4-bit, then adds a small set of learnable Low-rank Adapter weights [28] that are tuned by backpropagating gradients through the quantized weights.
+
+QLORA reduces the average memory requirements of finetuning a 65B parameter model from $> 7 8 0 \mathrm { G B }$ of GPU memory to ${ < } 4 8 \mathrm { G B }$ without degrading the runtime or predictive performance compared to a 16- bit fully finetuned baseline. This marks a significant shift in accessibility of LLM finetuning: now the largest publicly available models to date finetunable on a single GPU. Using QLORA, we train the Guanaco family of models, with the second best model reaching $9 7 . 8 \%$ of the performance level of ChatGPT on the Vicuna [10] benchmark, while being trainable in less than 12 hours on a single consumer GPU; using a single professional GPU over 24 hours we achieve $9 9 . 3 \%$ with our largest model, essentially closing the gap to ChatGPT on the Vicuna benchmark. When deployed, our smallest Guanaco model (7B parameters) requires just 5 GB of memory and outperforms a 26 GB Alpaca model by more than 20 percentage points on the Vicuna benchmark (Table 4).
+
+Table 1: Elo ratings for a competition between models, averaged for 10,000 random initial orderings. The winner of a match is determined by GPT-4 which declares which response is better for a given prompt of the the Vicuna benchmark. $9 5 \%$ confidence intervals are shown $( \pm )$ . After GPT4, Guanaco 33B and 65B win the most matches, while Guanaco 13B scores better than Bard.
+
+