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"profiling memory speed profile gpu memory training speed lora ( lora ( emb ) refers training embedding output layer , lora trainable embedding output layer ) q-lora setup single-gpu training .",
"test , experiment single a100-sxm4-80g gpu , use cuda 11.8 pytorch 2.0 .",
"flash attention 2 applied .",
"uniformly use batch size 1 gradient accumulation 8 .",
"profile memory ( gb ) speed ( s/iter ) input different length , namely 256 , 512 , 1024 , 2048 , 4096 , 8192 .",
"also report statistic full-parameter finetuning qwen-7b 2 a100 gpus .",
"report statistic 256 , 512 , 1024 token due limitation gpu memory .",
"qwen-7b , also test performance multinode finetuning .",
"experiment using two server , containing two a100-sxm4-80g gpus , rest configuration qwen-7b experiment .",
"result multinode finetuning marked lora ( multinode ) table .",
"qwen-72b , experiment two way : 1 ) lora fintuning + deepspeed zero 3 4 a100-sxm4-80g gpus 2 ) qlora ( int4 ) fine-tuning single a100-sxm4-80g gpu .",
"note oom occurs 4 a100-sxm4-80g gpus lora ( emb ) fine-tuning lora fine-tuning without deepspeed zero 3 ( pas ` -- deepspeed finetune/ds_config_zero3.json ` [ ` finetune/finetune_lora_ds.sh ` ] ( finetune/finetune_lora_ds.sh ) enable deepspeed zero 3 ) .",
"statistic listed : < table > < tr > < th rowspan= '' 2 '' > model size < /th > < th rowspan= '' 2 '' > method < /th > < th rowspan= '' 2 '' > # node < /th > < th rowspan= '' 2 '' > # gpus per node < /th > < th colspan= '' 6 '' align= '' center '' > sequence length < /th > < /tr > < tr > < th align= '' center '' > 256 < /th > < th align= '' center '' > 512 < /th > < th align= '' center '' > 1024 < /th > < th align= '' center '' > 2048 < /th > < th align= '' center '' > 4096 < /th > < th align= '' center '' > 8192 < /th > < /tr > < tr > < th rowspan= '' 4 '' > 1.8b < /th > < td > lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 6.7g / 1.0s/it < /td > < td align= '' center '' > 7.4g / 1.0s/it < /td > < td align= '' center '' > 8.4g / 1.1s/it < /td > < td align= '' center '' > 11.0g / 1.7s/it < /td > < td align= '' center '' > 16.2g / 3.3s/it < /td > < td align= '' center '' > 21.8g / 6.8s/it < /td > < /tr > < tr > < td > lora ( emb ) < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 13.7g / 1.0s/it < /td > < td align= '' center '' > 14.0g / 1.0s/it < /td > < td align= '' center '' > 14.0g / 1.1s/it < /td > < td align= '' center '' > 15.1g / 1.8s/it < /td > < td align= '' center '' > 19.7g / 3.4s/it < /td > < td align= '' center '' > 27.7g / 7.0s/it < /td > < /tr > < tr > < td > q-lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 5.8g / 1.4s/it < /td > < td align= '' center '' > 6.0g / 1.4s/it < /td > < td align= '' center '' > 6.6g / 1.4s/it < /td > < td align= '' center '' > 7.8g / 2.0s/it < /td > < td align= '' center '' > 10.2g / 3.4s/it < /td > < td align= '' center '' > 15.8g / 6.5s/it < /td > < /tr > < tr > < td > full-parameter < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 43.5g / 2.1s/it < /td > < td align= '' center '' > 43.5g / 2.2s/it < /td > < td align= '' center '' > 43.5g / 2.2s/it < /td > < td align= '' center '' > 43.5g / 2.3s/it < /td > < td align= '' center '' > 47.1g / 2.8s/it < /td > < td align= '' center '' > 48.3g / 5.6s/it < /td > < /tr > < tr > < th rowspan= '' 5 '' > 7b < /th > < td > lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 20.1g / 1.2s/it < /td > < td align= '' center '' > 20.4g / 1.5s/it < /td > < td align= '' center '' > 21.5g / 2.8s/it < /td > < td align= '' center '' > 23.8g / 5.2s/it < /td > < td align= '' center '' > 29.7g / 10.1s/it < /td > < td align= '' center '' > 36.6g / 21.3s/it < /td > < /tr > < tr > < td > lora ( emb ) < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 33.7g / 1.4s/it < /td > < td align= '' center '' > 34.1g / 1.6s/it < /td > < td align= '' center '' > 35.2g / 2.9s/it < /td > < td align= '' center '' > 35.1g / 5.3s/it < /td > < td align= '' center '' > 39.2g / 10.3s/it < /td > < td align= '' center '' > 48.5g / 21.7s/it < /td > < /tr > < tr > < td > q-lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 11.5g / 3.0s/it < /td > < td align= '' center '' > 11.5g / 3.0s/it < /td > < td align= '' center '' > 12.3g / 3.5s/it < /td > < td align= '' center '' > 13.9g / 7.0s/it < /td > < td align= '' center '' > 16.9g / 11.6s/it < /td > < td align= '' center '' > 23.5g / 22.3s/it < /td > < /tr > < tr > < td > full-parameter < /td > < td > 1 < /td > < td > 2 < /td > < td align= '' center '' > 139.2g / 4.0s/it < /td > < td align= '' center '' > 148.0g / 4.0s/it < /td > < td align= '' center '' > 162.0g / 4.5s/it < /td > < td align= '' center '' > - < /td > < td align= '' center '' > - < /td > < td align= '' center '' > - < /td > < /tr > < tr > < td > lora ( multinode ) < /td > < td > 2 < /td > < td > 2 < /td > < td align= '' center '' > 74.7g / 2.09s/it < /td > < td align= '' center '' > 77.6g / 3.16s/it < /td > < td align= '' center '' > 84.9g / 5.17s/it < /td > < td align= '' center '' > 95.1g / 9.25s/it < /td > < td align= '' center '' > 121.1g / 18.1s/it < /td > < td align= '' center '' > 155.5g / 37.4s/it < /td > < /tr > < tr > < th rowspan= '' 3 '' > 14b < /th > < td > lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 34.6g / 1.6s/it < /td > < td align= '' center '' > 35.1g / 2.4s/it < /td > < td align= '' center '' > 35.3g / 4.4s/it < /td > < td align= '' center '' > 37.4g / 8.4s/it < /td > < td align= '' center '' > 42.5g / 17.0s/it < /td > < td align= '' center '' > 55.2g / 36.0s/it < /td > < /tr > < tr > < td > lora ( emb ) < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 51.2 / 1.7s/it < /td > < td align= '' center '' > 51.1g / 2.6s/it < /td > < td align= '' center '' > 51.5g / 4.6s/it < /td > < td align= '' center '' > 54.1g / 8.6s/it < /td > < td align= '' center '' > 56.8g / 17.2s/it < /td > < td align= '' center '' > 67.7g / 36.3s/it < /td > < /tr > < tr > < td > q-lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 18.7g / 5.3s/it < /td > < td align= '' center '' > 18.4g / 6.3s/it < /td > < td align= '' center '' > 18.9g / 8.2s/it < /td > < td align= '' center '' > 19.9g / 11.8s/it < /td > < td align= '' center '' > 23.0g / 20.1s/it < /td > < td align= '' center '' > 27.9g / 38.3s/it < /td > < /tr > < tr > < th rowspan= '' 2 '' > 72b < /th > < td > lora + deepspeed zero3 < /td > < td > 1 < /td > < td > 4 < /td > < td align= '' center '' > 215.4g / 17.6s/it < /td > < td align= '' center '' > 217.7g / 20.5s/it < /td > < td align= '' center '' > 222.6g / 29.4s/it < /td > < td align= '' center '' > 228.8g / 45.7s/it < /td > < td align= '' center '' > 249.0g / 83.4s/it < /td > < td align= '' center '' > 289.2g / 161.5s/it < /td > < /tr > < tr > < td > q-lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 61.4g / 27.4s/it < /td > < td align= '' center '' > 61.4g / 31.5s/it < /td > < td align= '' center '' > 62.9g / 41.4s/it < /td > < td align= '' center '' > 64.1g / 59.5s/it < /td > < td align= '' center '' > 68.0g / 97.7s/it < /td > < td align= '' center '' > 75.6g / 179.8s/it < /td > < /tr > < /table > < br >"
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],
[
"profiling memory speed profile gpu memory training speed lora ( lora ( emb ) refers training embedding output layer , lora trainable embedding output layer ) q-lora setup single-gpu training .",
"test , experiment single a100-sxm4-80g gpu , use cuda 11.8 pytorch 2.0 .",
"flash attention 2 applied .",
"uniformly use batch size 1 gradient accumulation 8 .",
"profile memory ( gb ) speed ( s/iter ) input different length , namely 256 , 512 , 1024 , 2048 , 4096 , 8192 .",
"also report statistic full-parameter finetuning qwen-7b 2 a100 gpus .",
"report statistic 256 , 512 , 1024 token due limitation gpu memory .",
"qwen-7b , also test performance multinode finetuning .",
"experiment using two server , containing two a100-sxm4-80g gpus , rest configuration qwen-7b experiment .",
"result multinode finetuning marked lora ( multinode ) table .",
"qwen-72b , experiment two way : 1 ) lora fintuning + deepspeed zero 3 4 a100-sxm4-80g gpus 2 ) qlora ( int4 ) fine-tuning single a100-sxm4-80g gpu .",
"note oom occurs 4 a100-sxm4-80g gpus lora ( emb ) fine-tuning lora fine-tuning without deepspeed zero 3 ( pas ` -- deepspeed finetune/ds_config_zero3.json ` [ ` finetune/finetune_lora_ds.sh ` ] ( finetune/finetune_lora_ds.sh ) enable deepspeed zero 3 ) .",
"statistic listed : < table > < tr > < th rowspan= '' 2 '' > model size < /th > < th rowspan= '' 2 '' > method < /th > < th rowspan= '' 2 '' > # node < /th > < th rowspan= '' 2 '' > # gpus per node < /th > < th colspan= '' 6 '' align= '' center '' > sequence length < /th > < /tr > < tr > < th align= '' center '' > 256 < /th > < th align= '' center '' > 512 < /th > < th align= '' center '' > 1024 < /th > < th align= '' center '' > 2048 < /th > < th align= '' center '' > 4096 < /th > < th align= '' center '' > 8192 < /th > < /tr > < tr > < th rowspan= '' 4 '' > 1.8b < /th > < td > lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 6.7g / 1.0s/it < /td > < td align= '' center '' > 7.4g / 1.0s/it < /td > < td align= '' center '' > 8.4g / 1.1s/it < /td > < td align= '' center '' > 11.0g / 1.7s/it < /td > < td align= '' center '' > 16.2g / 3.3s/it < /td > < td align= '' center '' > 21.8g / 6.8s/it < /td > < /tr > < tr > < td > lora ( emb ) < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 13.7g / 1.0s/it < /td > < td align= '' center '' > 14.0g / 1.0s/it < /td > < td align= '' center '' > 14.0g / 1.1s/it < /td > < td align= '' center '' > 15.1g / 1.8s/it < /td > < td align= '' center '' > 19.7g / 3.4s/it < /td > < td align= '' center '' > 27.7g / 7.0s/it < /td > < /tr > < tr > < td > q-lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 5.8g / 1.4s/it < /td > < td align= '' center '' > 6.0g / 1.4s/it < /td > < td align= '' center '' > 6.6g / 1.4s/it < /td > < td align= '' center '' > 7.8g / 2.0s/it < /td > < td align= '' center '' > 10.2g / 3.4s/it < /td > < td align= '' center '' > 15.8g / 6.5s/it < /td > < /tr > < tr > < td > full-parameter < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 43.5g / 2.1s/it < /td > < td align= '' center '' > 43.5g / 2.2s/it < /td > < td align= '' center '' > 43.5g / 2.2s/it < /td > < td align= '' center '' > 43.5g / 2.3s/it < /td > < td align= '' center '' > 47.1g / 2.8s/it < /td > < td align= '' center '' > 48.3g / 5.6s/it < /td > < /tr > < tr > < th rowspan= '' 5 '' > 7b < /th > < td > lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 20.1g / 1.2s/it < /td > < td align= '' center '' > 20.4g / 1.5s/it < /td > < td align= '' center '' > 21.5g / 2.8s/it < /td > < td align= '' center '' > 23.8g / 5.2s/it < /td > < td align= '' center '' > 29.7g / 10.1s/it < /td > < td align= '' center '' > 36.6g / 21.3s/it < /td > < /tr > < tr > < td > lora ( emb ) < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 33.7g / 1.4s/it < /td > < td align= '' center '' > 34.1g / 1.6s/it < /td > < td align= '' center '' > 35.2g / 2.9s/it < /td > < td align= '' center '' > 35.1g / 5.3s/it < /td > < td align= '' center '' > 39.2g / 10.3s/it < /td > < td align= '' center '' > 48.5g / 21.7s/it < /td > < /tr > < tr > < td > q-lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 11.5g / 3.0s/it < /td > < td align= '' center '' > 11.5g / 3.0s/it < /td > < td align= '' center '' > 12.3g / 3.5s/it < /td > < td align= '' center '' > 13.9g / 7.0s/it < /td > < td align= '' center '' > 16.9g / 11.6s/it < /td > < td align= '' center '' > 23.5g / 22.3s/it < /td > < /tr > < tr > < td > full-parameter < /td > < td > 1 < /td > < td > 2 < /td > < td align= '' center '' > 139.2g / 4.0s/it < /td > < td align= '' center '' > 148.0g / 4.0s/it < /td > < td align= '' center '' > 162.0g / 4.5s/it < /td > < td align= '' center '' > - < /td > < td align= '' center '' > - < /td > < td align= '' center '' > - < /td > < /tr > < tr > < td > lora ( multinode ) < /td > < td > 2 < /td > < td > 2 < /td > < td align= '' center '' > 74.7g / 2.09s/it < /td > < td align= '' center '' > 77.6g / 3.16s/it < /td > < td align= '' center '' > 84.9g / 5.17s/it < /td > < td align= '' center '' > 95.1g / 9.25s/it < /td > < td align= '' center '' > 121.1g / 18.1s/it < /td > < td align= '' center '' > 155.5g / 37.4s/it < /td > < /tr > < tr > < th rowspan= '' 3 '' > 14b < /th > < td > lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 34.6g / 1.6s/it < /td > < td align= '' center '' > 35.1g / 2.4s/it < /td > < td align= '' center '' > 35.3g / 4.4s/it < /td > < td align= '' center '' > 37.4g / 8.4s/it < /td > < td align= '' center '' > 42.5g / 17.0s/it < /td > < td align= '' center '' > 55.2g / 36.0s/it < /td > < /tr > < tr > < td > lora ( emb ) < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 51.2 / 1.7s/it < /td > < td align= '' center '' > 51.1g / 2.6s/it < /td > < td align= '' center '' > 51.5g / 4.6s/it < /td > < td align= '' center '' > 54.1g / 8.6s/it < /td > < td align= '' center '' > 56.8g / 17.2s/it < /td > < td align= '' center '' > 67.7g / 36.3s/it < /td > < /tr > < tr > < td > q-lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 18.7g / 5.3s/it < /td > < td align= '' center '' > 18.4g / 6.3s/it < /td > < td align= '' center '' > 18.9g / 8.2s/it < /td > < td align= '' center '' > 19.9g / 11.8s/it < /td > < td align= '' center '' > 23.0g / 20.1s/it < /td > < td align= '' center '' > 27.9g / 38.3s/it < /td > < /tr > < tr > < th rowspan= '' 2 '' > 72b < /th > < td > lora + deepspeed zero3 < /td > < td > 1 < /td > < td > 4 < /td > < td align= '' center '' > 215.4g / 17.6s/it < /td > < td align= '' center '' > 217.7g / 20.5s/it < /td > < td align= '' center '' > 222.6g / 29.4s/it < /td > < td align= '' center '' > 228.8g / 45.7s/it < /td > < td align= '' center '' > 249.0g / 83.4s/it < /td > < td align= '' center '' > 289.2g / 161.5s/it < /td > < /tr > < tr > < td > q-lora < /td > < td > 1 < /td > < td > 1 < /td > < td align= '' center '' > 61.4g / 27.4s/it < /td > < td align= '' center '' > 61.4g / 31.5s/it < /td > < td align= '' center '' > 62.9g / 41.4s/it < /td > < td align= '' center '' > 64.1g / 59.5s/it < /td > < td align= '' center '' > 68.0g / 97.7s/it < /td > < td align= '' center '' > 75.6g / 179.8s/it < /td > < /tr > < /table > < br >"
]
] | Profiling of Memory and Speed
We profile the GPU memory and training speed of both LoRA (LoRA (emb) refers to training the embedding and output layer, while LoRA has no trainable embedding and output layer) and Q-LoRA in the setup of single-GPU training. In this test, we experiment on a single A100-SXM4-80G GPU, and we use CUDA 11.8 and Pytorch 2.0. Flash attention 2 is applied. We uniformly use a batch size of 1 and gradient accumulation of 8. We profile the memory (GB) and speed (s/iter) of inputs of different lengths, namely 256, 512, 1024, 2048, 4096, and 8192. We also report the statistics of full-parameter finetuning with Qwen-7B on 2 A100 GPUs. We only report the statistics of 256, 512, and 1024 tokens due to the limitation of GPU memory.
For Qwen-7B, we also test the performance of multinode finetuning. We experiment using two servers, each containing two A100-SXM4-80G GPUs, and the rest of configurations are the same as other Qwen-7B experiments. The results of multinode finetuning are marked as LoRA (multinode) in the table.
For Qwen-72B, we experiment in two ways: 1) Lora fintuning + DeepSpeed ZeRO 3 on 4 A100-SXM4-80G GPUs and 2) QLora (int4) fine-tuning on a single A100-SXM4-80G GPU. Note that OOM occurs on 4 A100-SXM4-80G GPUs both with LoRA (emb) fine-tuning and LoRA fine-tuning without Deepspeed ZeRO 3 (you can pass `--deepspeed finetune/ds_config_zero3.json` to [`finetune/finetune_lora_ds.sh`](finetune/finetune_lora_ds.sh) to enable DeepSpeed ZeRO 3).
The statistics are listed below:
<table>
<tr>
<th rowspan="2">Model Size</th><th rowspan="2">Method</th><th rowspan="2">#Nodes</th><th rowspan="2">#GPUs per node</th><th colspan="6" align="center">Sequence Length</th>
</tr>
<tr>
<th align="center">256</th><th align="center">512</th><th align="center">1024</th><th align="center">2048</th><th align="center">4096</th><th align="center">8192</th>
</tr>
<tr>
<th rowspan="4">1.8B</th><td>LoRA</td>
<td>1</td><td>1</td>
<td align="center">6.7G / 1.0s/it</td><td align="center">7.4G / 1.0s/it</td><td align="center">8.4G / 1.1s/it</td><td align="center">11.0G / 1.7s/it</td><td align="center">16.2G / 3.3s/it</td><td align="center">21.8G / 6.8s/it</td>
</tr>
<tr>
<td>LoRA (emb)</td>
<td>1</td><td>1</td>
<td align="center">13.7G / 1.0s/it</td><td align="center">14.0G / 1.0s/it</td><td align="center">14.0G / 1.1s/it</td><td align="center">15.1G / 1.8s/it</td><td align="center">19.7G / 3.4s/it</td><td align="center">27.7G / 7.0s/it</td>
</tr>
<tr>
<td>Q-LoRA</td>
<td>1</td><td>1</td>
<td align="center">5.8G / 1.4s/it</td><td align="center">6.0G / 1.4s/it</td><td align="center">6.6G / 1.4s/it</td><td align="center">7.8G / 2.0s/it</td><td align="center">10.2G / 3.4s/it</td><td align="center">15.8G / 6.5s/it</td>
</tr>
<tr>
<td>Full-parameter</td>
<td>1</td><td>1</td>
<td align="center">43.5G / 2.1s/it</td><td align="center">43.5G / 2.2s/it</td><td align="center">43.5G / 2.2s/it</td><td align="center">43.5G / 2.3s/it</td><td align="center">47.1G / 2.8s/it</td><td align="center">48.3G / 5.6s/it</td>
</tr>
<tr>
<th rowspan="5">7B</th>
<td>LoRA</td>
<td>1</td><td>1</td>
<td align="center">20.1G / 1.2s/it</td><td align="center">20.4G / 1.5s/it</td><td align="center">21.5G / 2.8s/it</td><td align="center">23.8G / 5.2s/it</td><td align="center">29.7G / 10.1s/it</td><td align="center">36.6G / 21.3s/it</td>
</tr>
<tr>
<td>LoRA (emb)</td>
<td>1</td><td>1</td>
<td align="center">33.7G / 1.4s/it</td><td align="center">34.1G / 1.6s/it</td><td align="center">35.2G / 2.9s/it</td><td align="center">35.1G / 5.3s/it</td><td align="center">39.2G / 10.3s/it</td><td align="center">48.5G / 21.7s/it</td>
</tr>
<tr>
<td>Q-LoRA</td>
<td>1</td><td>1</td>
<td align="center">11.5G / 3.0s/it</td><td align="center">11.5G / 3.0s/it</td><td align="center">12.3G / 3.5s/it</td><td align="center">13.9G / 7.0s/it</td><td align="center">16.9G / 11.6s/it</td><td align="center">23.5G / 22.3s/it</td>
</tr>
<tr>
<td>Full-parameter</td>
<td>1</td><td>2</td>
<td align="center">139.2G / 4.0s/it</td><td align="center">148.0G / 4.0s/it</td><td align="center">162.0G / 4.5s/it</td><td align="center">-</td><td align="center">-</td><td align="center">-</td>
</tr>
<tr>
<td>LoRA (multinode)</td>
<td>2</td><td>2</td>
<td align="center">74.7G / 2.09s/it</td><td align="center">77.6G / 3.16s/it</td><td align="center">84.9G / 5.17s/it</td><td align="center">95.1G / 9.25s/it</td><td align="center">121.1G / 18.1s/it</td><td align="center">155.5G / 37.4s/it</td>
</tr>
<tr>
<th rowspan="3">14B</th>
<td>LoRA</td>
<td>1</td><td>1</td>
<td align="center">34.6G / 1.6s/it</td><td align="center">35.1G / 2.4s/it</td><td align="center">35.3G / 4.4s/it</td><td align="center">37.4G / 8.4s/it</td><td align="center">42.5G / 17.0s/it</td><td align="center">55.2G / 36.0s/it</td>
</tr>
<tr>
<td>LoRA (emb)</td>
<td>1</td><td>1</td>
<td align="center">51.2 / 1.7s/it</td><td align="center">51.1G / 2.6s/it</td><td align="center">51.5G / 4.6s/it</td><td align="center">54.1G / 8.6s/it</td><td align="center">56.8G / 17.2s/it</td><td align="center">67.7G / 36.3s/it</td>
</tr>
<tr>
<td>Q-LoRA</td>
<td>1</td><td>1</td>
<td align="center">18.7G / 5.3s/it</td><td align="center">18.4G / 6.3s/it</td><td align="center">18.9G / 8.2s/it</td><td align="center">19.9G / 11.8s/it</td><td align="center">23.0G / 20.1s/it</td><td align="center">27.9G / 38.3s/it</td>
</tr>
<tr>
<th rowspan="2">72B</th>
<td>LoRA + Deepspeed Zero3</td>
<td>1</td><td>4</td>
<td align="center">215.4G / 17.6s/it</td><td align="center">217.7G / 20.5s/it</td><td align="center">222.6G / 29.4s/it</td><td align="center">228.8G / 45.7s/it</td><td align="center">249.0G / 83.4s/it</td><td align="center">289.2G / 161.5s/it</td>
</tr>
<tr>
<td>Q-LoRA</td>
<td>1</td><td>1</td>
<td align="center">61.4G / 27.4s/it</td><td align="center">61.4G / 31.5s/it</td><td align="center">62.9G / 41.4s/it</td><td align="center">64.1G / 59.5s/it</td><td align="center">68.0G / 97.7s/it</td><td align="center">75.6G / 179.8s/it</td>
</tr>
</table>
<br>
| https://github.com/QwenLM/Qwen | -1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
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https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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] | vLLM
For deployment and fast inference, we suggest using vLLM.
If you use cuda 12.1 and pytorch 2.1, you can directly use the following command to install vLLM.
```bash
| https://github.com/QwenLM/Qwen | -1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
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"flash-attention",
"large-language-models",
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"natural-language-processing",
"pretrained-models"
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git clone https://github.com/QwenLM/vllm-gptq
cd vllm-gptq
pip install -e .
```
Otherwise, please refer to the official vLLM [Installation Instructions](https://docs.vllm.ai/en/latest/getting_started/installation.html), or our [vLLM repo for GPTQ quantization](https://github.com/QwenLM/vllm-gptq).
| https://github.com/QwenLM/Qwen | 0 | [
"chinese",
"flash-attention",
"large-language-models",
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"first , install fastchat : `` ` bash pip install `` fschat [ model_worker , webui ] '' `` ` run qwen vllm fastchat , need launch controller : `` ` bash python -m fastchat.serve.controller `` ` launch model worker , mean loading model inference .",
"single gpu inference , directly run : `` ` bash python -m fastchat.serve.vllm_worker -- model-path $ model_path -- trust-remote-code -- dtype bfloat16"
]
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"single gpu inference , directly run : `` ` bash python -m fastchat.serve.vllm_worker -- model-path $ model_path -- trust-remote-code -- dtype bfloat16"
]
] | vLLM + Web Demo / OpenAI-like API
You can use FastChat to lauch a web demo or an OpenAI API server. First, install FastChat:
```bash
pip install "fschat[model_worker,webui]"
```
To run Qwen with vLLM and FastChat, you need launch a controller by:
```bash
python -m fastchat.serve.controller
```
Then you can launch the model worker, which means loading your model for inference. For single GPU inference, you can directly run:
```bash
python -m fastchat.serve.vllm_worker --model-path $model_path --trust-remote-code --dtype bfloat16
| https://github.com/QwenLM/Qwen | 0 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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]
] | Web UI
We provide code for users to build a web UI demo (thanks to @wysaid). Before you start, make sure you install the following packages:
```
pip install -r requirements_web_demo.txt
```
Then run the command below and click on the generated link:
```bash
python web_demo.py
```
<p align="center">
<br>
<img src="assets/web_demo.gif" width="600" />
<br>
<p>
| https://github.com/QwenLM/Qwen | 0 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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"meet problem launching api deployment , updating package latest version probably solve .",
"using api also simple .",
"see example : `` ` python import openai openai.api_base = `` http : //localhost:8000/v1 '' openai.api_key = `` none ''"
]
] | API
We provide methods to deploy local API based on OpenAI API (thanks to @hanpenggit). Before you start, install the required packages:
```bash
pip install fastapi uvicorn "openai<1.0" pydantic sse_starlette
```
Then run the command to deploy your API:
```bash
python openai_api.py
```
You can change your arguments, e.g., `-c` for checkpoint name or path, `--cpu-only` for CPU deployment, etc. If you meet problems launching your API deployment, updating the packages to the latest version can probably solve them.
Using the API is also simple. See the example below:
```python
import openai
openai.api_base = "http://localhost:8000/v1"
openai.api_key = "none"
| https://github.com/QwenLM/Qwen | 0 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
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] | 🐳 Docker
To simplify the deployment process, we provide docker images with pre-built environments: [qwenllm/qwen](https://hub.docker.com/r/qwenllm/qwen). You only need to install the driver and download model files to launch demos, deploy OpenAI API, and finetune the model.
| https://github.com/QwenLM/Qwen | -1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
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]
] | Preparation
1. Install the correct version of Nvidia driver depending on the image to use:
- `qwenllm/qwen:cu117` (**recommend**): `>= 515.48.07`
- `qwenllm/qwen:cu114` (w/o flash-attention): `>= 470.82.01`
- `qwenllm/qwen:cu121`: `>= 530.30.02`
- `qwenllm/qwen:latest`: same as `qwenllm/qwen:cu117`
2. Install and configure [docker](https://docs.docker.com/engine/install/) and [nvidia-container-toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html):
```bash
| https://github.com/QwenLM/Qwen | 1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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[
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]
] | test if docker is correctly installed
sudo docker run hello-world
| https://github.com/QwenLM/Qwen | -1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
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]
] | test if nvidia-container-toolkit is correctly installed
sudo docker run --rm --runtime=nvidia --gpus all ubuntu nvidia-smi
```
3. Download model checkpoints and codes to your environment (see [here](#DownloadModel)).
| https://github.com/QwenLM/Qwen | 1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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]
] | Deployment
Here we use Qwen-7B-Chat as an example. Before launching a web demo or API, you can setup the configuration as shown below:
```bash
IMAGE_NAME=qwenllm/qwen:cu117
PORT=8901
CHECKPOINT_PATH=/path/to/Qwen-7B-Chat | https://github.com/QwenLM/Qwen | -1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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"bash",
"image_name=qwenllm/qwen",
":",
"cu117",
"checkpoint_path=/path/to/qwen-7b"
],
[
"finetuning method finetuning using pre-built docker image basically [ chapter ] ( # finetuning ) ( already installed dependency image ) : following example single-gpu lora : `` ` bash image_name=qwenllm/qwen : cu117 checkpoint_path=/path/to/qwen-7b"
]
] | [
[
"finetuning",
"method",
"finetuning",
"using",
"pre-built",
"docker",
"image",
"basically",
"[",
"chapter",
"]",
"(",
"#",
"finetuning",
")",
"(",
"already",
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"dependency",
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"finetuning method finetuning using pre-built docker image basically [ chapter ] ( # finetuning ) ( already installed dependency image ) : following example single-gpu lora : `` ` bash image_name=qwenllm/qwen : cu117 checkpoint_path=/path/to/qwen-7b"
]
] | Finetuning
The method of finetuning using the pre-built Docker image is basically the same as [the above chapter](#Finetuning) (we have already installed dependencies in the image):
The following is an example of single-GPU LoRA:
```bash
IMAGE_NAME=qwenllm/qwen:cu117
CHECKPOINT_PATH=/path/to/Qwen-7B | https://github.com/QwenLM/Qwen | -1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
https://raw.githubusercontent.com/QwenLM/Qwen/main/README.md | [
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"tool usage qwen-chat optimized tool usage function calling capability .",
"user develop agent , langchain application , even augment qwen python code interpreter .",
"provide documentation implement tool call based principle react prompting , please refer [ react example ] ( examples/react_prompt.md ) .",
"based principle , provide support function calling [ openai_api.py ] ( openai_api.py ) .",
"tested model 's tool calling capability open-source chinese evaluation benchmark found qwen-chat consistently performs well : < table > < tr > < th colspan= '' 4 '' align= '' center '' > chinese tool-use benchmark ( version 20231206 ) < /th > < /tr > < tr > < th align= '' center '' > model < /th > < th align= '' center '' > tool selection ( acc.↑ ) < /th > < th align= '' center '' > tool input ( rouge-l↑ ) < /th > < th align= '' center '' > false positive error↓ < /th > < /tr > < tr > < td > gpt-4 < /td > < td align= '' center '' > 98.0 % < /td > < td align= '' center '' > 0.953 < /td > < td align= '' center '' > 23.9 % < /td > < /tr > < tr > < td > gpt-3.5 < /td > < td align= '' center '' > 74.5 % < /td > < td align= '' center '' > 0.807 < /td > < td align= '' center '' > 80.6 % < /td > < /tr > < tr > < td > qwen-1_8b-chat < /td > < td align= '' center '' > 85.0 % < /td > < td align= '' center '' > 0.839 < /td > < td align= '' center '' > 27.6 % < /td > < /tr > < tr > < td > qwen-7b-chat < /td > < td align= '' center '' > 95.5 % < /td > < td align= '' center '' > 0.900 < /td > < td align= '' center '' > 11.6 % < /td > < /tr > < tr > < td > qwen-14b-chat < /td > < td align= '' center '' > 96.9 % < /td > < td align= '' center '' > 0.917 < /td > < td align= '' center '' > 5.6 % < /td > < /tr > < tr > < td > qwen-72b-chat < /td > < td align= '' center '' > 98.2 % < /td > < td align= '' center '' > 0.927 < /td > < td align= '' center '' > 1.1 % < /td > < /tr > < /table > ass qwen 's ability use python code interpreter task mathematical problem solving , data visualization , general-purpose task file handling web scraping , created open-sourced benchmark specifically designed evaluating capability .",
"find benchmark [ link ] ( http : //github.com/qwenlm/qwen-agent/tree/main/benchmark ) .",
"observed qwen performs well term code executability result accuracy generating code : < table > < tr > < th colspan= '' 5 '' align= '' center '' > code interpreter benchmark ( version 20231206 ) < /th > < /tr > < tr > < th rowspan= '' 2 '' align= '' center '' > model < /th > < th colspan= '' 3 '' align= '' center '' > accuracy code execution result ( % ) < /th > < th colspan= '' 1 '' align= '' center '' > executable rate code ( % ) < /th > < /tr > < tr > < th align= '' center '' > math↑ < /th > < th align= '' center '' > visualization-hard↑ < /th > < th align= '' center '' > visualization-easy↑ < /th > < th align= '' center '' > general↑ < /th > < /tr > < tr > < td > gpt-4 < /td > < td align= '' center '' > 82.8 < /td > < td align= '' center '' > 66.7 < /td > < td align= '' center '' > 60.8 < /td > < td align= '' center '' > 82.8 < /td > < /tr > < tr > < td > gpt-3.5 < /td > < td align= '' center '' > 47.3 < /td > < td align= '' center '' > 33.3 < /td > < td align= '' center '' > 55.7 < /td > < td align= '' center '' > 74.1 < /td > < /tr > < tr > < td > llama2-13b-chat < /td > < td align= '' center '' > 8.3 < /td > < td align= '' center '' > 1.2 < /td > < td align= '' center '' > 15.2 < /td > < td align= '' center '' > 48.3 < /td > < /tr > < tr > < td > codellama-13b-instruct < /td > < td align= '' center '' > 28.2 < /td > < td align= '' center '' > 15.5 < /td > < td align= '' center '' > 21.5 < /td > < td align= '' center '' > 74.1 < /td > < /tr > < tr > < td > internlm-20b-chat < /td > < td align= '' center '' > 34.6 < /td > < td align= '' center '' > 10.7 < /td > < td align= '' center '' > 25.1 < /td > < td align= '' center '' > 65.5 < /td > < /tr > < tr > < td > chatglm3-6b < /td > < td align= '' center '' > 54.2 < /td > < td align= '' center '' > 4.8 < /td > < td align= '' center '' > 15.2 < /td > < td align= '' center '' > 67.1 < /td > < /tr > < tr > < td > qwen-1.8b-chat < /td > < td align= '' center '' > 25.6 < /td > < td align= '' center '' > 21.4 < /td > < td align= '' center '' > 22.8 < /td > < td align= '' center '' > 65.5 < /td > < /tr > < tr > < td > qwen-7b-chat < /td > < td align= '' center '' > 41.9 < /td > < td align= '' center '' > 23.8 < /td > < td align= '' center '' > 38.0 < /td > < td align= '' center '' > 67.2 < /td > < /tr > < tr > < td > qwen-14b-chat < /td > < td align= '' center '' > 58.4 < /td > < td align= '' center '' > 31.0 < /td > < td align= '' center '' > 45.6 < /td > < td align= '' center '' > 65.5 < /td > < /tr > < tr > < td > qwen-72b-chat < /td > < td align= '' center '' > 72.7 < /td > < td align= '' center '' > 41.7 < /td > < td align= '' center '' > 43.0 < /td > < td align= '' center '' > 82.8 < /td > < /tr > < /table > < p align= '' center '' > < br > < img src= '' assets/code_interpreter_showcase_001.jpg '' / > < br > < p > < br >"
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"tool usage qwen-chat optimized tool usage function calling capability .",
"user develop agent , langchain application , even augment qwen python code interpreter .",
"provide documentation implement tool call based principle react prompting , please refer [ react example ] ( examples/react_prompt.md ) .",
"based principle , provide support function calling [ openai_api.py ] ( openai_api.py ) .",
"tested model 's tool calling capability open-source chinese evaluation benchmark found qwen-chat consistently performs well : < table > < tr > < th colspan= '' 4 '' align= '' center '' > chinese tool-use benchmark ( version 20231206 ) < /th > < /tr > < tr > < th align= '' center '' > model < /th > < th align= '' center '' > tool selection ( acc.↑ ) < /th > < th align= '' center '' > tool input ( rouge-l↑ ) < /th > < th align= '' center '' > false positive error↓ < /th > < /tr > < tr > < td > gpt-4 < /td > < td align= '' center '' > 98.0 % < /td > < td align= '' center '' > 0.953 < /td > < td align= '' center '' > 23.9 % < /td > < /tr > < tr > < td > gpt-3.5 < /td > < td align= '' center '' > 74.5 % < /td > < td align= '' center '' > 0.807 < /td > < td align= '' center '' > 80.6 % < /td > < /tr > < tr > < td > qwen-1_8b-chat < /td > < td align= '' center '' > 85.0 % < /td > < td align= '' center '' > 0.839 < /td > < td align= '' center '' > 27.6 % < /td > < /tr > < tr > < td > qwen-7b-chat < /td > < td align= '' center '' > 95.5 % < /td > < td align= '' center '' > 0.900 < /td > < td align= '' center '' > 11.6 % < /td > < /tr > < tr > < td > qwen-14b-chat < /td > < td align= '' center '' > 96.9 % < /td > < td align= '' center '' > 0.917 < /td > < td align= '' center '' > 5.6 % < /td > < /tr > < tr > < td > qwen-72b-chat < /td > < td align= '' center '' > 98.2 % < /td > < td align= '' center '' > 0.927 < /td > < td align= '' center '' > 1.1 % < /td > < /tr > < /table > ass qwen 's ability use python code interpreter task mathematical problem solving , data visualization , general-purpose task file handling web scraping , created open-sourced benchmark specifically designed evaluating capability .",
"find benchmark [ link ] ( http : //github.com/qwenlm/qwen-agent/tree/main/benchmark ) .",
"observed qwen performs well term code executability result accuracy generating code : < table > < tr > < th colspan= '' 5 '' align= '' center '' > code interpreter benchmark ( version 20231206 ) < /th > < /tr > < tr > < th rowspan= '' 2 '' align= '' center '' > model < /th > < th colspan= '' 3 '' align= '' center '' > accuracy code execution result ( % ) < /th > < th colspan= '' 1 '' align= '' center '' > executable rate code ( % ) < /th > < /tr > < tr > < th align= '' center '' > math↑ < /th > < th align= '' center '' > visualization-hard↑ < /th > < th align= '' center '' > visualization-easy↑ < /th > < th align= '' center '' > general↑ < /th > < /tr > < tr > < td > gpt-4 < /td > < td align= '' center '' > 82.8 < /td > < td align= '' center '' > 66.7 < /td > < td align= '' center '' > 60.8 < /td > < td align= '' center '' > 82.8 < /td > < /tr > < tr > < td > gpt-3.5 < /td > < td align= '' center '' > 47.3 < /td > < td align= '' center '' > 33.3 < /td > < td align= '' center '' > 55.7 < /td > < td align= '' center '' > 74.1 < /td > < /tr > < tr > < td > llama2-13b-chat < /td > < td align= '' center '' > 8.3 < /td > < td align= '' center '' > 1.2 < /td > < td align= '' center '' > 15.2 < /td > < td align= '' center '' > 48.3 < /td > < /tr > < tr > < td > codellama-13b-instruct < /td > < td align= '' center '' > 28.2 < /td > < td align= '' center '' > 15.5 < /td > < td align= '' center '' > 21.5 < /td > < td align= '' center '' > 74.1 < /td > < /tr > < tr > < td > internlm-20b-chat < /td > < td align= '' center '' > 34.6 < /td > < td align= '' center '' > 10.7 < /td > < td align= '' center '' > 25.1 < /td > < td align= '' center '' > 65.5 < /td > < /tr > < tr > < td > chatglm3-6b < /td > < td align= '' center '' > 54.2 < /td > < td align= '' center '' > 4.8 < /td > < td align= '' center '' > 15.2 < /td > < td align= '' center '' > 67.1 < /td > < /tr > < tr > < td > qwen-1.8b-chat < /td > < td align= '' center '' > 25.6 < /td > < td align= '' center '' > 21.4 < /td > < td align= '' center '' > 22.8 < /td > < td align= '' center '' > 65.5 < /td > < /tr > < tr > < td > qwen-7b-chat < /td > < td align= '' center '' > 41.9 < /td > < td align= '' center '' > 23.8 < /td > < td align= '' center '' > 38.0 < /td > < td align= '' center '' > 67.2 < /td > < /tr > < tr > < td > qwen-14b-chat < /td > < td align= '' center '' > 58.4 < /td > < td align= '' center '' > 31.0 < /td > < td align= '' center '' > 45.6 < /td > < td align= '' center '' > 65.5 < /td > < /tr > < tr > < td > qwen-72b-chat < /td > < td align= '' center '' > 72.7 < /td > < td align= '' center '' > 41.7 < /td > < td align= '' center '' > 43.0 < /td > < td align= '' center '' > 82.8 < /td > < /tr > < /table > < p align= '' center '' > < br > < img src= '' assets/code_interpreter_showcase_001.jpg '' / > < br > < p > < br >"
]
] | Tool Usage
Qwen-Chat has been optimized for tool usage and function calling capabilities. Users can develop agents, LangChain applications, and even augment Qwen with a Python Code Interpreter.
We provide documentation on how to implement tool calls based on the principle of ReAct Prompting, please refer to [the ReAct example](examples/react_prompt.md). Based on this principle, we provide support for function calling in [openai_api.py](openai_api.py).
We have tested the model's tool calling capabilities on our open-source Chinese evaluation benchmark and found that Qwen-Chat consistently performs well:
<table>
<tr>
<th colspan="4" align="center">Chinese Tool-Use Benchmark (Version 20231206)</th>
</tr>
<tr>
<th align="center">Model</th><th align="center">Tool Selection (Acc.↑)</th><th align="center">Tool Input (Rouge-L↑)</th><th align="center">False Positive Error↓</th>
</tr>
<tr>
<td>GPT-4</td><td align="center">98.0%</td><td align="center">0.953</td><td align="center">23.9%</td>
</tr>
<tr>
<td>GPT-3.5</td><td align="center">74.5%</td><td align="center">0.807</td><td align="center">80.6%</td>
</tr>
<tr>
<td>Qwen-1_8B-Chat</td><td align="center">85.0%</td><td align="center">0.839</td><td align="center">27.6%</td>
</tr>
<tr>
<td>Qwen-7B-Chat</td><td align="center">95.5%</td><td align="center">0.900</td><td align="center">11.6%</td>
</tr>
<tr>
<td>Qwen-14B-Chat</td><td align="center">96.9%</td><td align="center">0.917</td><td align="center">5.6%</td>
</tr>
<tr>
<td>Qwen-72B-Chat</td><td align="center">98.2%</td><td align="center">0.927</td><td align="center">1.1%</td>
</tr>
</table>
To assess Qwen's ability to use the Python Code Interpreter for tasks such as mathematical problem solving, data visualization, and other general-purpose tasks such as file handling and web scraping, we have created and open-sourced a benchmark specifically designed for evaluating these capabilities. You can find the benchmark at this [link](https://github.com/QwenLM/Qwen-Agent/tree/main/benchmark).
We have observed that Qwen performs well in terms of code executability and result accuracy when generating code:
<table>
<tr>
<th colspan="5" align="center">Code Interpreter Benchmark (Version 20231206)</th>
</tr>
<tr>
<th rowspan="2" align="center">Model</th>
<th colspan="3" align="center">Accuracy of Code Execution Results (%)</th>
<th colspan="1" align="center">Executable Rate of Code (%)</th>
</tr>
<tr>
<th align="center">Math↑</th><th align="center">Visualization-Hard↑</th><th align="center">Visualization-Easy↑</th><th align="center">General↑</th>
</tr>
<tr>
<td>GPT-4</td>
<td align="center">82.8</td>
<td align="center">66.7</td>
<td align="center">60.8</td>
<td align="center">82.8</td>
</tr>
<tr>
<td>GPT-3.5</td>
<td align="center">47.3</td>
<td align="center">33.3</td>
<td align="center">55.7</td>
<td align="center">74.1</td>
</tr>
<tr>
<td>LLaMA2-13B-Chat</td>
<td align="center">8.3</td>
<td align="center">1.2</td>
<td align="center">15.2</td>
<td align="center">48.3</td>
</tr>
<tr>
<td>CodeLLaMA-13B-Instruct</td>
<td align="center">28.2</td>
<td align="center">15.5</td>
<td align="center">21.5</td>
<td align="center">74.1</td>
</tr>
<tr>
<td>InternLM-20B-Chat</td>
<td align="center">34.6</td>
<td align="center">10.7</td>
<td align="center">25.1</td>
<td align="center">65.5</td>
</tr>
<tr>
<td>ChatGLM3-6B</td>
<td align="center">54.2</td>
<td align="center">4.8</td>
<td align="center">15.2</td>
<td align="center">67.1</td>
</tr>
<tr>
<td>Qwen-1.8B-Chat</td>
<td align="center">25.6</td>
<td align="center">21.4</td>
<td align="center">22.8</td>
<td align="center">65.5</td>
</tr>
<tr>
<td>Qwen-7B-Chat</td>
<td align="center">41.9</td>
<td align="center">23.8</td>
<td align="center">38.0</td>
<td align="center">67.2</td>
</tr>
<tr>
<td>Qwen-14B-Chat</td>
<td align="center">58.4</td>
<td align="center">31.0</td>
<td align="center">45.6</td>
<td align="center">65.5</td>
</tr>
<tr>
<td>Qwen-72B-Chat</td>
<td align="center">72.7</td>
<td align="center">41.7</td>
<td align="center">43.0</td>
<td align="center">82.8</td>
</tr>
</table>
<p align="center">
<br>
<img src="assets/code_interpreter_showcase_001.jpg" />
<br>
<p>
<br>
| https://github.com/QwenLM/Qwen | -1 | [
"chinese",
"flash-attention",
"large-language-models",
"llm",
"natural-language-processing",
"pretrained-models"
] |
https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/main/README.md | [
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"table content please note ` readme.md ` file markdown ( ` .md ` ) file .",
"downloaded code bundle manning website viewing local computer , recommend using markdown editor previewer proper viewing .",
"n't installed markdown editor yet , [ marktext ] ( http : //www.marktext.cc ) good free option .",
"alternatively , view file github [ http : //github.com/rasbt/llms-from-scratch ] ( http : //github.com/rasbt/llms-from-scratch ) .",
"< br > < br > | chapter title | main code ( quick access ) | code + supplementary | | -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- | -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -| -- -- -- -- -- -- -- -- -- -- -- -- -- -- -- -| | ch 1 : understanding large language model | code | code | | ch 2 : working text data | - [ ch02.ipynb ] ( ch02/01_main-chapter-code/ch02.ipynb ) < br/ > - [ dataloader.ipynb ] ( ch02/01_main-chapter-code/dataloader.ipynb ) ( summary ) < br/ > - [ exercise-solutions.ipynb ] ( ch02/01_main-chapter-code/exercise-solutions.ipynb ) | [ ./ch02 ] ( ./ch02 ) | | ch 3 : coding attention mechanism | - [ ch03.ipynb ] ( ch03/01_main-chapter-code/ch03.ipynb ) < br/ > - [ multihead-attention.ipynb ] ( ch03/01_main-chapter-code/multihead-attention.ipynb ) ( summary ) | [ ./ch03 ] ( ./ch03 ) | | ch 4 : implementing gpt model scratch | - [ ch04.ipynb ] ( ch04/01_main-chapter-code/ch04.ipynb ) < br/ > - [ gpt.py ] ( ch04/01_main-chapter-code/gpt.py ) ( summary ) | [ ./ch04 ] ( ./ch04 ) | | ch 5 : pretraining unlabeled data | q1 2024 | ... | | ch 6 : finetuning text classification | q2 2024 | ... | | ch 7 : finetuning human feedback | q2 2024 | ... | | ch 8 : using large language model practice | q2/3 2024 | ... | | appendix : introduction pytorch * | - [ code-part1.ipynb ] ( appendix-a/03_main-chapter-code/code-part1.ipynb ) < br/ > - [ code-part2.ipynb ] ( appendix-a/03_main-chapter-code/code-part2.ipynb ) < br/ > - [ ddp-script.py ] ( appendix-a/03_main-chapter-code/ddp-script.py ) < br/ > - [ exercise-solutions.ipynb ] ( appendix-a/03_main-chapter-code/exercise-solutions.ipynb ) | [ ./appendix-a ] ( ./appendix-a ) | ( * please see [ ] ( appendix-a/01_optional-python-setup-preferences ) [ ] ( appendix-a/02_installing-python-libraries ) folder need guidance installing python python package . )",
"< br > < br > < img src= '' images/mental-model.jpg '' width= '' 600px '' > ( mental model summarizing content covered book . )"
]
] | Table of Contents
Please note that the `Readme.md` file is a Markdown (`.md`) file. If you have downloaded this code bundle from the Manning website and are viewing it on your local computer, I recommend using a Markdown editor or previewer for proper viewing. If you haven't installed a Markdown editor yet, [MarkText](https://www.marktext.cc) is a good free option.
Alternatively, you can view this and other files on GitHub at [https://github.com/rasbt/LLMs-from-scratch](https://github.com/rasbt/LLMs-from-scratch).
<br>
<br>
| Chapter Title | Main Code (for quick access) | All Code + Supplementary |
|------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|-------------------------------|
| Ch 1: Understanding Large Language Models | No code | No code |
| Ch 2: Working with Text Data | - [ch02.ipynb](ch02/01_main-chapter-code/ch02.ipynb)<br/>- [dataloader.ipynb](ch02/01_main-chapter-code/dataloader.ipynb) (summary)<br/>- [exercise-solutions.ipynb](ch02/01_main-chapter-code/exercise-solutions.ipynb) | [./ch02](./ch02) |
| Ch 3: Coding Attention Mechanisms | - [ch03.ipynb](ch03/01_main-chapter-code/ch03.ipynb)<br/>- [multihead-attention.ipynb](ch03/01_main-chapter-code/multihead-attention.ipynb) (summary) | [./ch03](./ch03) |
| Ch 4: Implementing a GPT Model from Scratch | - [ch04.ipynb](ch04/01_main-chapter-code/ch04.ipynb)<br/>- [gpt.py](ch04/01_main-chapter-code/gpt.py) (summary) | [./ch04](./ch04) |
| Ch 5: Pretraining on Unlabeled Data | Q1 2024 | ... |
| Ch 6: Finetuning for Text Classification | Q2 2024 | ... |
| Ch 7: Finetuning with Human Feedback | Q2 2024 | ... |
| Ch 8: Using Large Language Models in Practice | Q2/3 2024 | ... |
| Appendix A: Introduction to PyTorch* | - [code-part1.ipynb](appendix-A/03_main-chapter-code/code-part1.ipynb)<br/>- [code-part2.ipynb](appendix-A/03_main-chapter-code/code-part2.ipynb)<br/>- [DDP-script.py](appendix-A/03_main-chapter-code/DDP-script.py)<br/>- [exercise-solutions.ipynb](appendix-A/03_main-chapter-code/exercise-solutions.ipynb) | [./appendix-A](./appendix-A) |
(* Please see [this](appendix-A/01_optional-python-setup-preferences) and [this](appendix-A/02_installing-python-libraries) folder if you need more guidance on installing Python and Python packages.)
<br>
<br>
<img src="images/mental-model.jpg" width="600px">
(A mental model summarizing the contents covered in this book.)
| https://github.com/rasbt/LLMs-from-scratch | -1 | [
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WebLLM works as a companion project of [MLC LLM](https://github.com/mlc-ai/mlc-llm).
It reuses the model artifact and builds flow of MLC LLM, please check out
[MLC LLM document](https://llm.mlc.ai/docs/deploy/javascript.html)
on how to add new model weights and libraries to WebLLM.
Here, we go over the high-level idea. There are two elements of the WebLLM package that enables new models and weight variants.
- model_url: Contains a URL to model artifacts, such as weights and meta-data.
- model_lib_url: A URL to the web assembly library (i.e. wasm file) that contains the executables to accelerate the model computations.
Both are customizable in the WebLLM.
```typescript
async main() {
const myLlamaUrl = "/url/to/my/llama";
const appConfig = {
"model_list": [
{
"model_url": myLlamaUrl,
"local_id": "MyLlama-3b-v1-q4f32_0"
"model_lib_url": "/url/to/myllama3b.wasm",
}
],
};
// override default
const chatOpts = {
"repetition_penalty": 1.01
};
const chat = new ChatModule();
// load a prebuilt model
// with a chat option override and app config
// under the hood, it will load the model from myLlamaUrl
// and cache it in the browser cache
// The chat will also load the model library from "/url/to/myllama3b.wasm",
// assuming that it is compatible to the model in myLlamaUrl.
await chat.reload("MyLlama-3b-v1-q4f32_0", chatOpts, appConfig);
}
```
In many cases, we only want to supply the model weight variant, but
not necessarily a new model (e.g. `NeuralHermes-Mistral` can reuse `Mistral`'s
model library; `WizardMath` can reuse `Llama-2`'s model library). For
an example of how a model library is shared by different model variants,
see `examples/simple-chat/src/gh-config.js`. We also provide
a plethora of prebuilt model libraries, including:
- `Llama-2-7b-chat-hf-q4f32_1`: Llama-7b models.
- `RedPajama-INCITE-Chat-3B-v1-q4f32_1`: RedPajama-3B variants.
- `Mistral-7B-Instruct-v0.1-q4f16_1`: Mistral-7B variants.
- and many more at [binary-mlc-llm-libs](https://github.com/mlc-ai/binary-mlc-llm-libs).
| https://github.com/mlc-ai/web-llm | -1 | [
"chatgpt",
"deep-learning",
"language-model",
"llm",
"tvm",
"webgpu",
"webml"
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]
] | Build WebLLM Package From Source
NOTE: you don't need to build by yourself unless you would
like to change the WebLLM package, follow [use WebLLM](#use-web-llm-package) instead.
WebLLM package is a web runtime designed for [MLC LLM](https://github.com/mlc-ai/mlc-llm).
1. Install all the prerequisites for compilation:
1. [emscripten](https://emscripten.org). It is an LLVM-based compiler that compiles C/C++ source code to WebAssembly.
- Follow the [installation instruction](https://emscripten.org/docs/getting_started/downloads.html#installation-instructions-using-the-emsdk-recommended) to install the latest emsdk.
- Source `emsdk_env.sh` by `source path/to/emsdk_env.sh`, so that `emcc` is reachable from PATH and the command `emcc` works.
4. Install jekyll by following the [official guides](https://jekyllrb.com/docs/installation/). It is the package we use for website. This is not needed if you're using nextjs (see next-simple-chat in the examples).
5. Install jekyll-remote-theme by command. Try [gem mirror](https://gems.ruby-china.com/) if install blocked.
```shell
gem install jekyll-remote-theme
```
We can verify the successful installation by trying out `emcc` and `jekyll` in terminal, respectively.
2. Setup necessary environment
Prepare all the necessary dependencies for web build:
```shell
./scripts/prep_deps.sh
```
3. Buld WebLLM Package
```shell
npm run build
```
4. Validate some of the sub-packages
You can then go to the subfolders in [examples](examples) to validate some of the sub-packages.
We use Parcelv2 for bundling. Although Parcel is not very good at tracking parent directory
changes sometimes. When you make a change in the WebLLM package, try to edit the `package.json`
of the subfolder and save it, which will trigger Parcel to rebuild.
| https://github.com/mlc-ai/web-llm | -1 | [
"chatgpt",
"deep-learning",
"language-model",
"llm",
"tvm",
"webgpu",
"webml"
] |
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Once installed, authenticate to Nebuly platform and start building.
| https://github.com/nebuly-ai/nebuly | 0 | [
"ai",
"analytics",
"artificial-intelligence",
"deeplearning",
"large-language-models",
"llm"
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https://raw.githubusercontent.com/LlamaFamily/Llama2-Chinese/main/README.md | [
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"🍄 模型量化 我们对中文微调的模型参数进行了量化，方便以更少的计算资源运行。目前已经在 [ hugging face ] ( http : //huggingface.co/flagalpha ) 上传了13b中文微调模型 [ flagalpha/llama2-chinese-13b-chat ] ( http : //huggingface.co/flagalpha/llama2-chinese-13b-chat ) 的4bit压缩版本 [ flagalpha/llama2-chinese-13b-chat-4bit ] ( http : //huggingface.co/flagalpha/llama2-chinese-13b-chat-4bit ) ，具体调用方式如下： 环境准备： `` ` pip install git+https : //github.com/panqiwei/autogptq.git `` ` `` ` python transformer import autotokenizer auto_gptq import autogptqforcausallm model = autogptqforcausallm.from_quantized ( 'flagalpha/llama2-chinese-13b-chat-4bit ' , device= '' cuda:0 '' ) tokenizer = autotokenizer.from_pretrained ( 'flagalpha/llama2-chinese-13b-chat-4bit ' , use_fast=false ) input_ids = tokenizer ( [ ' < > human : 怎么登上火星\\n < /s > < > assistant : ' ] , return_tensors= '' pt '' , add_special_tokens=false ) .input_ids.to ( 'cuda ' ) generate_input = { `` input_ids '' : input_ids , `` max_new_tokens '' :512 , `` do_sample '' : true , `` top_k '' :50 , `` top_p '' :0.95 , `` temperature '' :0.3 , `` repetition_penalty '' :1.3 , `` eos_token_id '' : tokenizer.eos_token_id , `` bos_token_id '' : tokenizer.bos_token_id , `` pad_token_id '' : tokenizer.pad_token_id } generate_ids = model.generate ( * * generate_input ) text = tokenizer.decode ( generate_ids [ 0 ] ) print ( text ) `` `"
]
] | 🍄 模型量化
我们对中文微调的模型参数进行了量化，方便以更少的计算资源运行。目前已经在[Hugging Face](https://huggingface.co/FlagAlpha)上传了13B中文微调模型[FlagAlpha/Llama2-Chinese-13b-Chat](https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat)的4bit压缩版本[FlagAlpha/Llama2-Chinese-13b-Chat-4bit](https://huggingface.co/FlagAlpha/Llama2-Chinese-13b-Chat-4bit)，具体调用方式如下：
环境准备：
```
pip install git+https://github.com/PanQiWei/AutoGPTQ.git
```
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model = AutoGPTQForCausalLM.from_quantized('FlagAlpha/Llama2-Chinese-13b-Chat-4bit', device="cuda:0")
tokenizer = AutoTokenizer.from_pretrained('FlagAlpha/Llama2-Chinese-13b-Chat-4bit',use_fast=False)
input_ids = tokenizer(['<s>Human: 怎么登上火星\n</s><s>Assistant: '], return_tensors="pt",add_special_tokens=False).input_ids.to('cuda')
generate_input = {
"input_ids":input_ids,
"max_new_tokens":512,
"do_sample":True,
"top_k":50,
"top_p":0.95,
"temperature":0.3,
"repetition_penalty":1.3,
"eos_token_id":tokenizer.eos_token_id,
"bos_token_id":tokenizer.bos_token_id,
"pad_token_id":tokenizer.pad_token_id
}
generate_ids = model.generate(**generate_input)
text = tokenizer.decode(generate_ids[0])
print(text)
```
| https://github.com/LlamaFamily/Llama2-Chinese | 0 | [
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We provide a docker container that helps you start running OpenLLM:
```bash
docker run --rm -it -p 3000:3000 ghcr.io/bentoml/openllm start facebook/opt-1.3b --backend pt
```
> [!NOTE]
> Given you have access to GPUs and have setup [nvidia-docker](https://github.com/NVIDIA/nvidia-container-toolkit), you can additionally pass in `--gpus`
> to use GPU for faster inference and optimization
>```bash
> docker run --rm --gpus all -p 3000:3000 -it ghcr.io/bentoml/openllm start HuggingFaceH4/zephyr-7b-beta --backend vllm
> ```
| https://github.com/bentoml/OpenLLM | 1 | [
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The following provides instructions for how to get started with OpenLLM locally.
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You have installed Python 3.8 (or later) and `pip`. We highly recommend using a [Virtual Environment](https://docs.python.org/3/library/venv.html) to prevent package conflicts.
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"model list supported model .",
"prune remove saved model , ( optionally bentos ) built openllm locally .",
"query query llm interactively , terminal .",
"start start llmserver supported llm .",
"start-grpc start grpc llmserver supported llm .",
"extension : build-base-container base image builder bentollm .",
"dive-bentos dive bentollm .",
"get-containerfile return containerfile given bento .",
"get-prompt get default prompt used openllm .",
"list-bentos list available bentos built openllm .",
"list-models equivalent openllm model ... playground openllm playground .",
"`` `"
]
] | Install OpenLLM
Install OpenLLM by using `pip` as follows:
```bash
pip install openllm
```
To verify the installation, run:
```bash
$ openllm -h
Usage: openllm [OPTIONS] COMMAND [ARGS]...
██████╗ ██████╗ ███████╗███╗ ██╗██╗ ██╗ ███╗ ███╗
██╔═══██╗██╔══██╗██╔════╝████╗ ██║██║ ██║ ████╗ ████║
██║ ██║██████╔╝█████╗ ██╔██╗ ██║██║ ██║ ██╔████╔██║
██║ ██║██╔═══╝ ██╔══╝ ██║╚██╗██║██║ ██║ ██║╚██╔╝██║
╚██████╔╝██║ ███████╗██║ ╚████║███████╗███████╗██║ ╚═╝ ██║
╚═════╝ ╚═╝ ╚══════╝╚═╝ ╚═══╝╚══════╝╚══════╝╚═╝ ╚═╝.
An open platform for operating large language models in production.
Fine-tune, serve, deploy, and monitor any LLMs with ease.
Options:
-v, --version Show the version and exit.
-h, --help Show this message and exit.
Commands:
build Package a given models into a BentoLLM.
import Setup LLM interactively.
models List all supported models.
prune Remove all saved models, (and optionally bentos) built with OpenLLM locally.
query Query a LLM interactively, from a terminal.
start Start a LLMServer for any supported LLM.
start-grpc Start a gRPC LLMServer for any supported LLM.
Extensions:
build-base-container Base image builder for BentoLLM.
dive-bentos Dive into a BentoLLM.
get-containerfile Return Containerfile of any given Bento.
get-prompt Get the default prompt used by OpenLLM.
list-bentos List available bentos built by OpenLLM.
list-models This is equivalent to openllm models...
playground OpenLLM Playground.
```
| https://github.com/bentoml/OpenLLM | 0 | [
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"mistral",
"ml",
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"mpt",
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"openllm",
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] |
https://raw.githubusercontent.com/bentoml/OpenLLM/main/README.md | [
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]
] | 🧩 Supported models
OpenLLM currently supports the following models. By default, OpenLLM doesn't include dependencies to run all models. The extra model-specific dependencies can be installed with the instructions below.
<!-- update-readme.py: start -->
<details>
<summary>Baichuan</summary>
| https://github.com/bentoml/OpenLLM | -1 | [
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"bentoml",
"falcon",
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"llama",
"llama2",
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"mistral",
"ml",
"mlops",
"model-inference",
"mpt",
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https://raw.githubusercontent.com/bentoml/OpenLLM/main/README.md | [
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"`` ` > * * note : * * baichuan variant deployed openllm .",
"visit [ huggingface model hub ] ( http : //huggingface.co/models ? sort=trending & search=baichuan ) see baichuan-compatible model ."
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"`` ` > * * note : * * baichuan variant deployed openllm .",
"visit [ huggingface model hub ] ( http : //huggingface.co/models ? sort=trending & search=baichuan ) see baichuan-compatible model ."
]
] | Quickstart
> **Note:** Baichuan requires to install with:
> ```bash
> pip install "openllm[baichuan]"
> ```
Run the following command to quickly spin up a Baichuan server:
```bash
TRUST_REMOTE_CODE=True openllm start baichuan-inc/baichuan-7b
```
In a different terminal, run the following command to interact with the server:
```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```
> **Note:** Any Baichuan variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=baichuan) to see more Baichuan-compatible models.
| https://github.com/bentoml/OpenLLM | 0 | [
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"mistral",
"ml",
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"recommended serving usecase production , choose vllm serving .",
"> * * note : * * currently , adapter yet supported vllm .",
"- pytorch : `` ` bash trust_remote_code=true openllm start baichuan-inc/baichuan2-7b-base -- backend pt `` ` < /details > < detail > < summary > chatglm < /summary >"
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"> * * important : * * recommend user explicitly specify ` -- backend ` choose desired backend run model .",
"access gpu , always use ` -- backend vllm ` .",
"- vllm ( recommended ) : install vllm , run ` pip install `` openllm [ vllm ] '' ` `` ` bash trust_remote_code=true openllm start baichuan-inc/baichuan2-7b-base -- backend vllm `` ` > * * important : * * using vllm requires gpu architecture newer 8.0 get best performance serving .",
"recommended serving usecase production , choose vllm serving .",
"> * * note : * * currently , adapter yet supported vllm .",
"- pytorch : `` ` bash trust_remote_code=true openllm start baichuan-inc/baichuan2-7b-base -- backend pt `` ` < /details > < detail > < summary > chatglm < /summary >"
]
] | Supported backends
OpenLLM will support vLLM and PyTorch as default backend. By default, it will use vLLM if vLLM is available, otherwise fallback to PyTorch.
> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
TRUST_REMOTE_CODE=True openllm start baichuan-inc/baichuan2-7b-base --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
TRUST_REMOTE_CODE=True openllm start baichuan-inc/baichuan2-7b-base --backend pt
```
</details>
<details>
<summary>ChatGLM</summary>
| https://github.com/bentoml/OpenLLM | 0 | [
"ai",
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"falcon",
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"llama2",
"llm",
"llm-inference",
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"mistral",
"ml",
"mlops",
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"mpt",
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"openllm",
"stablelm",
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https://raw.githubusercontent.com/bentoml/OpenLLM/main/README.md | [
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"`` ` > * * note : * * chatglm variant deployed openllm .",
"visit [ huggingface model hub ] ( http : //huggingface.co/models ? sort=trending & search=chatglm ) see chatglm-compatible model ."
]
] | Quickstart
> **Note:** ChatGLM requires to install with:
> ```bash
> pip install "openllm[chatglm]"
> ```
Run the following command to quickly spin up a ChatGLM server:
```bash
TRUST_REMOTE_CODE=True openllm start thudm/chatglm-6b
```
In a different terminal, run the following command to interact with the server:
```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```
> **Note:** Any ChatGLM variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=chatglm) to see more ChatGLM-compatible models.
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] | Supported backends
OpenLLM will support vLLM and PyTorch as default backend. By default, it will use vLLM if vLLM is available, otherwise fallback to PyTorch.
> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
TRUST_REMOTE_CODE=True openllm start thudm/chatglm-6b --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
TRUST_REMOTE_CODE=True openllm start thudm/chatglm-6b --backend pt
```
</details>
<details>
<summary>DollyV2</summary>
| https://github.com/bentoml/OpenLLM | 0 | [
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"- pytorch : `` ` bash openllm start databricks/dolly-v2-3b -- backend pt `` ` < /details > < detail > < summary > falcon < /summary >"
]
] | Supported backends
OpenLLM will support vLLM and PyTorch as default backend. By default, it will use vLLM if vLLM is available, otherwise fallback to PyTorch.
> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
openllm start databricks/dolly-v2-3b --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
openllm start databricks/dolly-v2-3b --backend pt
```
</details>
<details>
<summary>Falcon</summary>
| https://github.com/bentoml/OpenLLM | 0 | [
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] | Quickstart
> **Note:** Falcon requires to install with:
> ```bash
> pip install "openllm[falcon]"
> ```
Run the following command to quickly spin up a Falcon server:
```bash
openllm start tiiuae/falcon-7b
```
In a different terminal, run the following command to interact with the server:
```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```
> **Note:** Any Falcon variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=falcon) to see more Falcon-compatible models.
| https://github.com/bentoml/OpenLLM | 0 | [
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> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
openllm start HuggingFaceH4/zephyr-7b-alpha --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
openllm start HuggingFaceH4/zephyr-7b-alpha --backend pt
```
</details>
<details>
<summary>Mixtral</summary>
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> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
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```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
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> ```bash
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> ```
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```bash
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- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
TRUST_REMOTE_CODE=True openllm start mosaicml/mpt-7b --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
TRUST_REMOTE_CODE=True openllm start mosaicml/mpt-7b --backend pt
```
</details>
<details>
<summary>OPT</summary>
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> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
openllm start facebook/opt-125m --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
openllm start facebook/opt-125m --backend pt
```
</details>
<details>
<summary>Phi</summary>
| https://github.com/bentoml/OpenLLM | 0 | [
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```bash
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```bash
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</details>
<details>
<summary>Qwen</summary>
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> **Note:** Qwen requires to install with:
> ```bash
> pip install "openllm[qwen]"
> ```
Run the following command to quickly spin up a Qwen server:
```bash
TRUST_REMOTE_CODE=True openllm start qwen/Qwen-7B-Chat
```
In a different terminal, run the following command to interact with the server:
```bash
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openllm query 'What are large language models?'
```
> **Note:** Any Qwen variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=qwen) to see more Qwen-compatible models.
| https://github.com/bentoml/OpenLLM | 0 | [
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OpenLLM will support vLLM and PyTorch as default backend. By default, it will use vLLM if vLLM is available, otherwise fallback to PyTorch.
> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
TRUST_REMOTE_CODE=True openllm start qwen/Qwen-7B-Chat --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
TRUST_REMOTE_CODE=True openllm start qwen/Qwen-7B-Chat --backend pt
```
</details>
<details>
<summary>StableLM</summary>
| https://github.com/bentoml/OpenLLM | 0 | [
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> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
openllm start stabilityai/stablelm-tuned-alpha-3b --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
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</details>
<details>
<summary>StarCoder</summary>
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> **Note:** StarCoder requires to install with:
> ```bash
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> ```
Run the following command to quickly spin up a StarCoder server:
```bash
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```bash
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```
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"recommended serving usecase production , choose vllm serving .",
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OpenLLM will support vLLM and PyTorch as default backend. By default, it will use vLLM if vLLM is available, otherwise fallback to PyTorch.
> **Important:** We recommend user to explicitly specify `--backend` to choose the desired backend to run the model. If you have access to a GPU, always use `--backend vllm`.
- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
openllm start bigcode/starcoder --backend vllm
```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
openllm start bigcode/starcoder --backend pt
```
</details>
<details>
<summary>Yi</summary>
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- vLLM (Recommended):
To install vLLM, run `pip install "openllm[vllm]"`
```bash
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```
> **Important:** Using vLLM requires a GPU that has architecture newer than 8.0 to get the best performance for serving. It is recommended that for all serving usecase in production, you should choose vLLM for serving.
> **Note:** Currently, adapters are yet to be supported with vLLM.
- PyTorch:
```bash
TRUST_REMOTE_CODE=True openllm start 01-ai/Yi-6B --backend pt
```
</details>
<!-- update-readme.py: stop -->
More models will be integrated with OpenLLM and we welcome your contributions if you want to incorporate your custom LLMs into the ecosystem. Check out [Adding a New Model Guide](https://github.com/bentoml/OpenLLM/blob/main/ADDING_NEW_MODEL.md) to learn more.
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```bash
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```bash
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> first to install the dependency. From the GPTQ paper, it is recommended to quantized the weights before serving.
> See [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) for more information on GPTQ quantization.
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"🐍 python sdk llm instantiated ` openllm.llm ` : `` ` python import openllm llm = openllm.llm ( 'microsoft/phi-2 ' ) `` ` main inference api streaming ` generate_iterator ` method : `` ` python async generation llm.generate_iterator ( 'what meaning life ?",
"' ) : print ( generation.outputs [ 0 ] .text ) `` ` > [ ! note ] > motivation behind making ` llm.generate_iterator ` async generator provide support continuous batching vllm backend .",
"async endpoint , prompt > added correctly request queue process vllm backend .",
"also _one-shot_ ` generate ` method : `` ` python await llm.generate ( 'what meaning life ? ' )",
"`` ` method easy use one-shot generation use case , merely served example use ` llm.generate_iterator ` us ` generate_iterator ` hood .",
"> [ ! important ] > need call code synchronous context , use ` asyncio.run ` wrap async function : > > `` ` python > import asyncio > async def generate ( prompt , * * attrs ) : return await llm.generate ( prompt , * * attrs ) > asyncio.run ( generate ( `` meaning life '' , temperature=0.23 ) ) > `` `"
]
] | 🐍 Python SDK
Each LLM can be instantiated with `openllm.LLM`:
```python
import openllm
llm = openllm.LLM('microsoft/phi-2')
```
The main inference API is the streaming `generate_iterator` method:
```python
async for generation in llm.generate_iterator('What is the meaning of life?'):
print(generation.outputs[0].text)
```
> [!NOTE]
> The motivation behind making `llm.generate_iterator` an async generator is to provide support for Continuous batching with vLLM backend. By having the async endpoints, each prompt
> will be added correctly to the request queue to process with vLLM backend.
There is also a _one-shot_ `generate` method:
```python
await llm.generate('What is the meaning of life?')
```
This method is easy to use for one-shot generation use case, but merely served as an example how to use `llm.generate_iterator` as it uses `generate_iterator` under the hood.
> [!IMPORTANT]
> If you need to call your code in a synchronous context, you can use `asyncio.run` that wraps an async function:
>
> ```python
> import asyncio
> async def generate(prompt, **attrs): return await llm.generate(prompt, **attrs)
> asyncio.run(generate("The meaning of life is", temperature=0.23))
> ```
| https://github.com/bentoml/OpenLLM | -1 | [
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https://raw.githubusercontent.com/bentoml/OpenLLM/main/README.md | [
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"`` ` python import transformer agent = transformers.hfagent ( 'http : //localhost:3000/hf/agent ' )"
]
] | Transformers Agents
OpenLLM seamlessly integrates with
[Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents).
> [!WARNING]
> The Transformers Agent is still at an experimental stage. It is
> recommended to install OpenLLM with `pip install -r nightly-requirements.txt`
> to get the latest API update for HuggingFace agent.
```python
import transformers
agent = transformers.HfAgent('http://localhost:3000/hf/agent') | https://github.com/bentoml/OpenLLM | 0 | [
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https://raw.githubusercontent.com/cpacker/MemGPT/main/README.md | [
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"< div align= '' center '' > < img src= '' http : //research.memgpt.ai/assets/img/discord/slash_commands.png '' alt= '' memgpt bot slash command '' width= '' 400 '' > < /div >"
]
] | Quick setup
Join <a href="https://discord.gg/9GEQrxmVyE">Discord</a></strong> and message the MemGPT bot (in the `#memgpt` channel). Then run the following commands (messaged to "MemGPT Bot"):
* `/profile` (to create your profile)
* `/key` (to enter your OpenAI key)
* `/create` (to create a MemGPT chatbot)
Make sure your privacy settings on this server are open so that MemGPT Bot can DM you: \
MemGPT → Privacy Settings → Direct Messages set to ON
<div align="center">
<img src="https://research.memgpt.ai/assets/img/discord/dm_settings.png" alt="set DMs settings on MemGPT server to be open in MemGPT so that MemGPT Bot can message you" width="400">
</div>
You can see the full list of available commands when you enter `/` into the message box.
<div align="center">
<img src="https://research.memgpt.ai/assets/img/discord/slash_commands.png" alt="MemGPT Bot slash commands" width="400">
</div>
| https://github.com/cpacker/MemGPT | -1 | [
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]
] | Running MemGPT locally
Install MemGPT:
```sh
pip install -U pymemgpt
```
Now, you can run MemGPT and start chatting with a MemGPT agent with:
```sh
memgpt run
```
If you're running MemGPT for the first time, you'll see two quickstart options:
1. **OpenAI**: select this if you'd like to run MemGPT with OpenAI models like GPT-4 (requires an OpenAI API key)
2. **MemGPT Free Endpoint**: select this if you'd like to try MemGPT on a top open LLM for free (currently variants of Mixtral 8x7b!)
Neither of these options require you to have an LLM running on your own machine. If you'd like to run MemGPT with your custom LLM setup (or on OpenAI Azure), select **Other** to proceed to the advanced setup.
| https://github.com/cpacker/MemGPT | 0 | [
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You can reconfigure MemGPT's default settings by running:
```sh
memgpt configure
```
| https://github.com/cpacker/MemGPT | -1 | [
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```sh
git clone git@github.com:cpacker/MemGPT.git
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Then navigate to the main `MemGPT` directory, and do:
```sh
pip install -e .
```
Now, you should be able to run `memgpt` from the command-line using the downloaded source code.
If you are having dependency issues using `pip install -e .`, we recommend you install the package using Poetry (see below). Installing MemGPT from source using Poetry will ensure that you are using exact package versions that have been tested for the production build.
<details>
<summary>
<strong>Installing from source (using Poetry)</strong>
</summary>
First, install Poetry using [the official instructions here](https://python-poetry.org/docs/#installing-with-the-official-installer).
Then, you can install MemGPT from source with:
```sh
git clone git@github.com:cpacker/MemGPT.git
poetry shell
poetry install
```
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<details>
<summary><b> Dataset Licenses</b></summary>
Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.
If you're a dataset owner and do not want your dataset to be included in this library, please get in touch through a [GitHub issue](https://github.com/activeloopai/deeplake/issues/new). Thank you for your contribution to the ML community!
</details>
<details>
<summary><b> Usage Tracking</b></summary>
By default, we collect usage data using Bugout (here's the [code](https://github.com/activeloopai/deeplake/blob/853456a314b4fb5623c936c825601097b0685119/deeplake/__init__.py#L24) that does it). It does not collect user data other than anonymized IP address data, and it only logs the Deep Lake library's own actions. This helps our team understand how the tool is used and how to build features that matter to you! After you register with Activeloop, data is no longer anonymous. You can always opt-out of reporting by setting an environmental variable ```BUGGER_OFF``` to ```True```:
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**[Update Feb. 5, 2024] We added support for Code Llama 70B instruct in our example [inference script](./examples/code_llama/code_instruct_example.py). For details on formatting the prompt for Code Llama 70B instruct model please refer to [this document](./docs/inference.md)**.
**[Update Dec. 28, 2023] We added support for Llama Guard as a safety checker for our example inference script and also with standalone inference with an example script and prompt formatting. More details [here](./examples/llama_guard/README.md). For details on formatting data for fine tuning Llama Guard, we provide a script and sample usage [here](./src/llama_recipes/data/llama_guard/README.md).**
**[Update Dec 14, 2023] We recently released a series of Llama 2 demo apps [here](./demo_apps). These apps show how to run Llama (locally, in the cloud, or on-prem), how to use Azure Llama 2 API (Model-as-a-Service), how to ask Llama questions in general or about custom data (PDF, DB, or live), how to integrate Llama with WhatsApp and Messenger, and how to implement an end-to-end chatbot with RAG (Retrieval Augmented Generation).**
The 'llama-recipes' repository is a companion to the [Llama 2 model](https://github.com/facebookresearch/llama). The goal of this repository is to provide examples to quickly get started with fine-tuning for domain adaptation and how to run inference for the fine-tuned models. For ease of use, the examples use Hugging Face converted versions of the models. See steps for conversion of the model [here](#model-conversion-to-hugging-face).
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Llama 2 is a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. In order to help developers address these risks, we have created the [Responsible Use Guide](https://github.com/facebookresearch/llama/blob/main/Responsible-Use-Guide.pdf). More details can be found in our research paper as well. For downloading the models, follow the instructions on [Llama 2 repo](https://github.com/facebookresearch/llama).
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"table content 1 .",
"[ quick start ] ( # quick-start ) 2 .",
"[ model conversion ] ( # model-conversion-to-hugging-face ) 3 .",
"[ fine-tuning ] ( # fine-tuning ) - [ single gpu ] ( # single-gpu ) - [ multi gpu one node ] ( # multiple-gpus-one-node ) - [ multi gpu multi node ] ( # multi-gpu-multi-node ) 4 .",
"[ inference ] ( ./docs/inference.md ) 5 .",
"[ demo apps ] ( # demo-apps ) 6 .",
"[ repository organization ] ( # repository-organization ) 7 .",
"[ license acceptable use policy ] ( # license )"
]
] | Table of Contents
1. [Quick start](#quick-start)
2. [Model Conversion](#model-conversion-to-hugging-face)
3. [Fine-tuning](#fine-tuning)
- [Single GPU](#single-gpu)
- [Multi GPU One Node](#multiple-gpus-one-node)
- [Multi GPU Multi Node](#multi-gpu-multi-node)
4. [Inference](./docs/inference.md)
5. [Demo Apps](#demo-apps)
6. [Repository Organization](#repository-organization)
7. [License and Acceptable Use Policy](#license)
| https://github.com/facebookresearch/llama-recipes | -1 | [
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[Llama 2 Jupyter Notebook](./examples/quickstart.ipynb): This jupyter notebook steps you through how to finetune a Llama 2 model on the text summarization task using the [samsum](https://huggingface.co/datasets/samsum). The notebook uses parameter efficient finetuning (PEFT) and int8 quantization to finetune a 7B on a single GPU like an A10 with 24GB gpu memory.
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Llama-recipes provides a pip distribution for easy install and usage in other projects. Alternatively, it can be installed from source.
| https://github.com/facebookresearch/llama-recipes | 2 | [
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```
pip install --extra-index-url https://download.pytorch.org/whl/test/cu118 llama-recipes
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| https://github.com/facebookresearch/llama-recipes | 0 | [
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Llama-recipes offers the installation of optional packages. There are three optional dependency groups.
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```
pip install --extra-index-url https://download.pytorch.org/whl/test/cu118 llama-recipes[tests]
```
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```
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```
To use the sensitive topics safety checker install with:
```
pip install --extra-index-url https://download.pytorch.org/whl/test/cu118 llama-recipes[auditnlg]
```
Optional dependencies can also be combines with [option1,option2].
| https://github.com/facebookresearch/llama-recipes | 0 | [
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"* * note * * setting defined [ config file ] ( src/llama_recipes/configs/ ) passed args cli running script , need change config file directly .",
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] | Install from source
To install from source e.g. for development use these commands. We're using hatchling as our build backend which requires an up-to-date pip as well as setuptools package.
```
git clone git@github.com:facebookresearch/llama-recipes.git
cd llama-recipes
pip install -U pip setuptools
pip install --extra-index-url https://download.pytorch.org/whl/test/cu118 -e .
```
For development and contributing to llama-recipes please install all optional dependencies:
```
git clone git@github.com:facebookresearch/llama-recipes.git
cd llama-recipes
pip install -U pip setuptools
pip install --extra-index-url https://download.pytorch.org/whl/test/cu118 -e .[tests,auditnlg,vllm]
```
⚠️ **Note** ⚠️ Some features (especially fine-tuning with FSDP + PEFT) currently require PyTorch nightlies to be installed. Please make sure to install the nightlies if you're using these features following [this guide](https://pytorch.org/get-started/locally/).
**Note** All the setting defined in [config files](src/llama_recipes/configs/) can be passed as args through CLI when running the script, there is no need to change from config files directly.
**For more in depth information checkout the following:**
* [Single GPU Fine-tuning](./docs/single_gpu.md)
* [Multi-GPU Fine-tuning](./docs/multi_gpu.md)
* [LLM Fine-tuning](./docs/LLM_finetuning.md)
* [Adding custom datasets](./docs/Dataset.md)
* [Inference](./docs/inference.md)
* [Evaluation Harness](./eval/README.md)
* [FAQs](./docs/FAQ.md)
| https://github.com/facebookresearch/llama-recipes | 0 | [
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] | Model conversion to Hugging Face
The recipes and notebooks in this folder are using the Llama 2 model definition provided by Hugging Face's transformers library.
Given that the original checkpoint resides under models/7B you can install all requirements and convert the checkpoint with:
```bash
| https://github.com/facebookresearch/llama-recipes | -1 | [
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https://raw.githubusercontent.com/facebookresearch/llama-recipes/main/README.md | [
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] | Install Hugging Face Transformers from source
pip freeze | grep transformers | https://github.com/facebookresearch/llama-recipes | 2 | [
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https://raw.githubusercontent.com/facebookresearch/llama-recipes/main/README.md | [
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]
] | verify it is version 4.31.0 or higher
git clone git@github.com:huggingface/transformers.git
cd transformers
pip install protobuf
python src/transformers/models/llama/convert_llama_weights_to_hf.py \
--input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path
```
| https://github.com/facebookresearch/llama-recipes | 0 | [
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"file plotted using [ plot_metrics.py ] ( ./examples/plot_metrics.py ) script , ` python examples/plot_metrics.py -- file_path path/to/metrics.json `"
]
] | Single and Multi GPU Finetune
If you want to dive right into single or multi GPU fine-tuning, run the examples below on a single GPU like A10, T4, V100, A100 etc.
All the parameters in the examples and recipes below need to be further tuned to have desired results based on the model, method, data and task at hand.
**Note:**
* To change the dataset in the commands below pass the `dataset` arg. Current options for integrated dataset are `grammar_dataset`, `alpaca_dataset`and `samsum_dataset`. Additionally, we integrate the OpenAssistant/oasst1 dataset as an [example for a custom dataset](./examples/custom_dataset.py). A description of how to use your own dataset and how to add custom datasets can be found in [Dataset.md](./docs/Dataset.md#using-custom-datasets). For `grammar_dataset`, `alpaca_dataset` please make sure you use the suggested instructions from [here](./docs/single_gpu.md#how-to-run-with-different-datasets) to set them up.
* Default dataset and other LORA config has been set to `samsum_dataset`.
* Make sure to set the right path to the model in the [training config](src/llama_recipes/configs/training.py).
* To save the loss and perplexity metrics for evaluation, enable this by passing `--save_metrics` to the finetuning script. The file can be plotted using the [plot_metrics.py](./examples/plot_metrics.py) script, `python examples/plot_metrics.py --file_path path/to/metrics.json`
| https://github.com/facebookresearch/llama-recipes | -1 | [
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"finetuning",
"langchain",
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"llm",
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https://raw.githubusercontent.com/facebookresearch/llama-recipes/main/README.md | [
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"note : use datasets compliance dataset 's underlying license ( including limited non-commercial us ) [ demo_apps ] ( ./demo_apps ) : contains series llama2-powered apps , quickstart deployment ask llama question unstructured data , structured data , live data , video summary .",
"[ example ] ( ./examples/ ) : contains example script finetuning inference llama 2 model well use safely .",
"[ inference ] ( src/llama_recipes/inference/ ) : includes module inference fine-tuned model .",
"[ model_checkpointing ] ( src/llama_recipes/model_checkpointing/ ) : contains fsdp checkpoint handler .",
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"[ configs ] ( src/llama_recipes/configs/ ) : contains configuration file peft method , fsdp , datasets .",
"[ doc ] ( docs/ ) : example recipe single multi-gpu fine-tuning recipe .",
"[ datasets ] ( src/llama_recipes/datasets/ ) : contains individual script dataset download process .",
"note : use datasets compliance dataset 's underlying license ( including limited non-commercial us ) [ demo_apps ] ( ./demo_apps ) : contains series llama2-powered apps , quickstart deployment ask llama question unstructured data , structured data , live data , video summary .",
"[ example ] ( ./examples/ ) : contains example script finetuning inference llama 2 model well use safely .",
"[ inference ] ( src/llama_recipes/inference/ ) : includes module inference fine-tuned model .",
"[ model_checkpointing ] ( src/llama_recipes/model_checkpointing/ ) : contains fsdp checkpoint handler .",
"[ policy ] ( src/llama_recipes/policies/ ) : contains fsdp script provide different policy , mixed precision , transformer wrapping policy activation checkpointing along precision optimizer ( used running fsdp pure bf16 mode ) .",
"[ utils ] ( src/llama_recipes/utils/ ) : utility file : - ` train_utils.py ` provides training/eval loop train utils .",
"- ` dataset_utils.py ` get preprocessed datasets .",
"- ` config_utils.py ` override configs received cli .",
"- ` fsdp_utils.py ` provides fsdp wrapping policy peft method .",
"- ` memory_utils.py ` context manager track different memory stats train loop ."
]
] | Repository Organization
This repository is organized in the following way:
[benchmarks](./benchmarks): Contains a series of benchmark scripts for Llama 2 models inference on various backends.
[configs](src/llama_recipes/configs/): Contains the configuration files for PEFT methods, FSDP, Datasets.
[docs](docs/): Example recipes for single and multi-gpu fine-tuning recipes.
[datasets](src/llama_recipes/datasets/): Contains individual scripts for each dataset to download and process. Note: Use of any of the datasets should be in compliance with the dataset's underlying licenses (including but not limited to non-commercial uses)
[demo_apps](./demo_apps): Contains a series of Llama2-powered apps, from quickstart deployments to how to ask Llama questions about unstructured data, structured data, live data, and video summary.
[examples](./examples/): Contains examples script for finetuning and inference of the Llama 2 model as well as how to use them safely.
[inference](src/llama_recipes/inference/): Includes modules for inference for the fine-tuned models.
[model_checkpointing](src/llama_recipes/model_checkpointing/): Contains FSDP checkpoint handlers.
[policies](src/llama_recipes/policies/): Contains FSDP scripts to provide different policies, such as mixed precision, transformer wrapping policy and activation checkpointing along with any precision optimizer (used for running FSDP with pure bf16 mode).
[utils](src/llama_recipes/utils/): Utility files for:
- `train_utils.py` provides training/eval loop and more train utils.
- `dataset_utils.py` to get preprocessed datasets.
- `config_utils.py` to override the configs received from CLI.
- `fsdp_utils.py` provides FSDP wrapping policy for PEFT methods.
- `memory_utils.py` context manager to track different memory stats in train loop.
| https://github.com/facebookresearch/llama-recipes | -1 | [
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] | Installation
To get started quickly, you can use a pre-built development environment. **Click the button below** to open the repo in GitHub Codespaces, and then continue the readme!
[](https://codespaces.new/microsoft/promptflow?quickstart=1)
If you want to get started in your local environment, first install the packages:
Ensure you have a python environment, `python=3.9` is recommended.
```sh
pip install promptflow promptflow-tools
```
| https://github.com/microsoft/promptflow | 0 | [
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"next step !",
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]
] | Quick Start ⚡
**Create a chatbot with prompt flow**
Run the command to initiate a prompt flow from a chat template, it creates folder named `my_chatbot` and generates required files within it:
```sh
pf flow init --flow ./my_chatbot --type chat
```
**Setup a connection for your API key**
For OpenAI key, establish a connection by running the command, using the `openai.yaml` file in the `my_chatbot` folder, which stores your OpenAI key (override keys and name with --set to avoid yaml file changes):
```sh
pf connection create --file ./my_chatbot/openai.yaml --set api_key=<your_api_key> --name open_ai_connection
```
For Azure OpenAI key, establish the connection by running the command, using the `azure_openai.yaml` file:
```sh
pf connection create --file ./my_chatbot/azure_openai.yaml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
```
**Chat with your flow**
In the `my_chatbot` folder, there's a `flow.dag.yaml` file that outlines the flow, including inputs/outputs, nodes, connection, and the LLM model, etc
> Note that in the `chat` node, we're using a connection named `open_ai_connection` (specified in `connection` field) and the `gpt-35-turbo` model (specified in `deployment_name` field). The deployment_name filed is to specify the OpenAI model, or the Azure OpenAI deployment resource.
Interact with your chatbot by running: (press `Ctrl + C` to end the session)
```sh
pf flow test --flow ./my_chatbot --interactive
```
**Core value: ensuring "High Quality” from prototype to production**
Explore our [**15-minute tutorial**](examples/tutorials/flow-fine-tuning-evaluation/promptflow-quality-improvement.md) that guides you through prompt tuning ➡ batch testing ➡ evaluation, all designed to ensure high quality ready for production.
Next Step! Continue with the **Tutorial** 👇 section to delve deeper into prompt flow.
| https://github.com/microsoft/promptflow | -1 | [
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] | VS Code Extension
We also offer a VS Code extension (a flow designer) for an interactive flow development experience with UI.
<img src="examples/tutorials/quick-start/media/vsc.png" alt="vsc" width="1000"/>
You can install it from the <a href="https://marketplace.visualstudio.com/items?itemName=prompt-flow.prompt-flow">visualstudio marketplace</a>.
| https://github.com/microsoft/promptflow | -1 | [
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[Getting started with prompt flow](./docs/cloud/azureai/quick-start/index.md): A step by step guidance to invoke your first flow run.
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> More examples can be found [here](https://microsoft.github.io/promptflow/tutorials/index.html#samples). We welcome contributions of new use cases!
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If you're interested in contributing, please start with our dev setup guide: [dev_setup.md](./docs/dev/dev_setup.md).
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```shell
pip install shell-gpt
```
You'll need an OpenAI API key, you can generate one [here](https://beta.openai.com/account/api-keys).
You will be prompted for your key which will then be stored in `~/.config/shell_gpt/.sgptrc`.
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This is a **very handy feature**, which allows you to use `sgpt` shell completions directly in your terminal, without the need to type `sgpt` with prompt and arguments. Shell integration enables the use of ShellGPT with hotkeys in your terminal, supported by both Bash and ZSH shells. This feature puts `sgpt` completions directly into terminal buffer (input line), allowing for immediate editing of suggested commands.
https://github.com/TheR1D/shell_gpt/assets/16740832/bead0dab-0dd9-436d-88b7-6abfb2c556c1
To install shell integration, run `sgpt --install-integration` and restart your terminal to apply changes. This will add few lines to your `.bashrc` or `.zshrc` file. After that, you can use `Ctrl+l` (by default) to invoke ShellGPT. When you press `Ctrl+l` it will replace you current input line (buffer) with suggested command. You can then edit it and just press `Enter` to execute.
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[Function calls](https://platform.openai.com/docs/guides/function-calling) is a powerful feature OpenAI provides. It allows LLM to execute functions in your system, which can be used to accomplish a variety of tasks. To install [default functions](https://github.com/TheR1D/shell_gpt/tree/main/sgpt/default_functions/) run:
```shell
sgpt --install-functions
```
ShellGPT has a convenient way to define functions and use them. In order to create your custom function, navigate to `~/.config/shell_gpt/functions` and create a new .py file with the function name. Inside this file, you can define your function using the following syntax:
```python
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```
Note that if for some reason the function (execute_shell_command) will return an error, LLM might try to accomplish the task based on the output. Let's say we don't have installed `jq` in our system, and we ask LLM to parse JSON file:
```shell
sgpt "parse /tmp/test.json file using jq and return only email value"
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"'re ready use api key different model / provider , press ` + ` button bottom left add new model ` config.json ` .",
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You can try out Continue for free using a proxy server that securely makes calls with our API key to models like GPT-4, Gemini Pro, and Phind CodeLlama via OpenAI, Google, and Together respectively.
Once you're ready to use your own API key or a different model / provider, press the `+` button in the bottom left to add a new model to your `config.json`. Learn more about the models and providers [here](https://continue.dev/docs/model-setup/overview).
| https://github.com/continuedev/continue | -1 | [
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