pin the gpt-fast repo commit version

[agunapal]

Description

Update to the GPT-Fast example documentation.

Need to pin the commit version.

Fixes #(issue)

Type of change

Please delete options that are not relevant.

• Bug fix (non-breaking change which fixes an issue)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• New feature (non-breaking change which adds functionality)
• This change requires a documentation update

Feature/Issue validation/testing

Please describe the Unit or Integration tests that you ran to verify your changes and relevant result summary. Provide instructions so it can be reproduced.
Please also list any relevant details for your test configuration.

• Test A

curl "http://localhost:8080/predictions/gpt_fast" -T request.json
2024-02-26T23:04:59,678 [INFO ] epollEventLoopGroup-3-2 TS_METRICS - ts_inference_requests_total.Count:1.0|#model_name:gpt_fast,model_version:default|#hostname:ip-172-31-11-40,timestamp:1708988699
2024-02-26T23:04:59,679 [DEBUG] W-9000-gpt_fast_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd PREDICT repeats 1 to backend at: 1708988699679
2024-02-26T23:04:59,680 [INFO ] W-9000-gpt_fast_1.0 org.pytorch.serve.wlm.WorkerThread - Looping backend response at: 1708988699680
2024-02-26T23:04:59,680 [INFO ] W-9000-gpt_fast_1.0-stdout MODEL_LOG - Backend received inference at: 1708988699
2024-02-26T23:04:59,711 [INFO ] W-9000-gpt_fast_1.0 ACCESS_LOG - /127.0.0.1:58004 "PUT /predictions/gpt_fast HTTP/1.1" 200 33
2024-02-26T23:04:59,712 [INFO ] W-9000-gpt_fast_1.0 TS_METRICS - Requests2XX.Count:1.0|#Level:Host|#hostname:ip-172-31-11-40,timestamp:1708988699
 is Paris. It is located in the northern central part of the country and is known for its beautiful architecture, art museums, fashion, and historical landmarks such as the Eiffel Tower and the Louvre Museum.

2024-02-26T23:05:01,016 [INFO ] W-9000-gpt_fast_1.0-stdout MODEL_LOG - Num tokens = 50
Paris is2024-02-26T23:05:01,016 [INFO ] W-9000-gpt_fast_1.0 TS_METRICS - ts_inference_latency_microseconds.Microseconds:1336853.093|#model_name:gpt_fast,model_version:default|#hostname:ip-172-31-11-40,timestamp:1708988701
2024-02-26T23:05:01,016 [INFO ] W-9000-gpt_fast_1.0 TS_METRICS - ts_queue_latency_microseconds.Microseconds:80.835|#model_name:gpt_fast,model_version:default|#hostname:ip-172-31-11-40,timestamp:1708988701
2024-02-26T23:05:01,017 [INFO ] W-9000-gpt_fast_1.0-stdout org.pytorch.serve.wlm.WorkerLifeCycle - result=[METRICS]HandlerTime.Milliseconds:1335.32|#ModelName:gpt_fast,Level:Model|#type:GAUGE|#hostname:ip-172-31-11-40,1708988701,b5c68212-74a0-402d-891a-4421e92f3b6a, pattern=[METRICS]
2024-02-26T23:05:01,017 [DEBUG] W-9000-gpt_fast_1.0 org.pytorch.serve.job.RestJob - Waiting time ns: 80835, Backend time ns: 1337548332
2024-02-26T23:05:01,017 [INFO ] W-9000-gpt_fast_1.0-stdout MODEL_METRICS - HandlerTime.ms:1335.32|#ModelName:gpt_fast,Level:Model|#hostname:ip-172-31-11-40,requestID:b5c68212-74a0-402d-891a-4421e92f3b6a,timestamp:1708988701
2024-02-26T23:05:01,017 [INFO ] W-9000-gpt_fast_1.0 TS_METRICS - QueueTime.Milliseconds:0.0|#Level:Host|#hostname:ip-172-31-11-40,timestamp:1708988701
2024-02-26T23:05:01,017 [INFO ] W-9000-gpt_fast_1.0-stdout org.pytorch.serve.wlm.WorkerLifeCycle - result=[METRICS]PredictionTime.Milliseconds:1335.42|#ModelName:gpt_fast,Level:Model|#type:GAUGE|#hostname:ip-172-31-11-40,1708988701,b5c68212-74a0-402d-891a-4421e92f3b6a, pattern=[METRICS]
2024-02-26T23:05:01,017 [INFO ] W-9000-gpt_fast_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 1331
2024-02-26T23:05:01,017 [INFO ] W-9000-gpt_fast_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:7.0|#Level:Host|#hostname:ip-172-31-11-40,timestamp:1708988701
2024-02-26T23:05:01,017 [INFO ] W-9000-gpt_fast_1.0-stdout MODEL_METRICS - PredictionTime.ms:1335.42|#ModelName:gpt_fast,Level:Model|#hostname:ip-172-31-11-40,requestID:b5c68212-74a0-402d-891a-4421e92f3b6a,timestamp:1708988701

Checklist:

• Did you have fun?
• Have you added tests that prove your fix is effective or that this feature works?
• Has code been commented, particularly in hard-to-understand areas?
• Have you made corresponding changes to the documentation?

[mreso]

LGTM