@@ -62,7 +62,7 @@ For a detailed starting guide, please see the [Quick Tour](https://huggingface.c
model=HuggingFaceH4/zephyr-7b-beta
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.3--model-id$model
docker run --gpus all --shm-size 1g -p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.4--model-id$model
```
And then you can make requests like
...
...
@@ -76,7 +76,7 @@ curl 127.0.0.1:8080/generate \
**Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.3-rocm --model-id $model` instead of the command above.
**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4-rocm --model-id $model` instead of the command above.
To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
token=<your cli READ token>
docker run --gpus all --shm-size 1g -eHUGGING_FACE_HUB_TOKEN=$token-p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.3--model-id$model
docker run --gpus all --shm-size 1g -eHUGGING_FACE_HUB_TOKEN=$token-p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.4--model-id$model
{"openapi":"3.0.3","info":{"title":"Text Generation Inference","description":"Text Generation Webserver","contact":{"name":"Olivier Dehaene"},"license":{"name":"Apache 2.0","url":"https://www.apache.org/licenses/LICENSE-2.0"},"version":"1.3.4"},"paths":{"/":{"post":{"tags":["Text Generation Inference"],"summary":"Generate tokens if `stream == false` or a stream of token if `stream == true`","description":"Generate tokens if `stream == false` or a stream of token if `stream == true`","operationId":"compat_generate","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/CompatGenerateRequest"}}},"required":true},"responses":{"200":{"description":"Generated Text","content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateResponse"}},"text/event-stream":{"schema":{"$ref":"#/components/schemas/StreamResponse"}}}},"422":{"description":"Input validation error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Input validation error"}}}},"424":{"description":"Generation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Request failed during generation"}}}},"429":{"description":"Model is overloaded","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Model is overloaded"}}}},"500":{"description":"Incomplete generation","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Incomplete generation"}}}}}}},"/generate":{"post":{"tags":["Text Generation Inference"],"summary":"Generate tokens","description":"Generate tokens","operationId":"generate","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateRequest"}}},"required":true},"responses":{"200":{"description":"Generated Text","content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateResponse"}}}},"422":{"description":"Input validation error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Input validation error"}}}},"424":{"description":"Generation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Request failed during generation"}}}},"429":{"description":"Model is overloaded","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Model is overloaded"}}}},"500":{"description":"Incomplete generation","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Incomplete generation"}}}}}}},"/generate_stream":{"post":{"tags":["Text Generation Inference"],"summary":"Generate a stream of token using Server-Sent Events","description":"Generate a stream of token using Server-Sent Events","operationId":"generate_stream","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateRequest"}}},"required":true},"responses":{"200":{"description":"Generated Text","content":{"text/event-stream":{"schema":{"$ref":"#/components/schemas/StreamResponse"}}}},"422":{"description":"Input validation error","content":{"text/event-stream":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Input validation error"}}}},"424":{"description":"Generation Error","content":{"text/event-stream":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Request failed during generation"}}}},"429":{"description":"Model is overloaded","content":{"text/event-stream":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Model is overloaded"}}}},"500":{"description":"Incomplete generation","content":{"text/event-stream":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Incomplete generation"}}}}}}},"/health":{"get":{"tags":["Text Generation Inference"],"summary":"Health check method","description":"Health check method","operationId":"health","responses":{"200":{"description":"Everything is working fine"},"503":{"description":"Text generation inference is down","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"unhealthy","error_type":"healthcheck"}}}}}}},"/info":{"get":{"tags":["Text Generation Inference"],"summary":"Text Generation Inference endpoint info","description":"Text Generation Inference endpoint info","operationId":"get_model_info","responses":{"200":{"description":"Served model info","content":{"application/json":{"schema":{"$ref":"#/components/schemas/Info"}}}}}}},"/metrics":{"get":{"tags":["Text Generation Inference"],"summary":"Prometheus metrics scrape endpoint","description":"Prometheus metrics scrape endpoint","operationId":"metrics","responses":{"200":{"description":"Prometheus Metrics","content":{"text/plain":{"schema":{"type":"string"}}}}}}},"/tokenize":{"post":{"tags":["Text Generation Inference"],"summary":"Tokenize inputs","description":"Tokenize inputs","operationId":"tokenize","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/GenerateRequest"}}},"required":true},"responses":{"200":{"description":"Tokenized ids","content":{"application/json":{"schema":{"$ref":"#/components/schemas/TokenizeResponse"}}}},"404":{"description":"No tokenizer found","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"No fast tokenizer available"}}}}}}},"/v1/chat/completions":{"post":{"tags":["Text Generation Inference"],"summary":"Generate tokens","description":"Generate tokens","operationId":"chat_completions","requestBody":{"content":{"application/json":{"schema":{"$ref":"#/components/schemas/ChatRequest"}}},"required":true},"responses":{"200":{"description":"Generated Text","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ChatCompletionChunk"}}}},"422":{"description":"Input validation error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Input validation error"}}}},"424":{"description":"Generation Error","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Request failed during generation"}}}},"429":{"description":"Model is overloaded","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Model is overloaded"}}}},"500":{"description":"Incomplete generation","content":{"application/json":{"schema":{"$ref":"#/components/schemas/ErrorResponse"},"example":{"error":"Incomplete generation"}}}}}}}},"components":{"schemas":{"BestOfSequence":{"type":"object","required":["generated_text","finish_reason","generated_tokens","prefill","tokens"],"properties":{"finish_reason":{"$ref":"#/components/schemas/FinishReason"},"generated_text":{"type":"string","example":"test"},"generated_tokens":{"type":"integer","format":"int32","example":1,"minimum":0},"prefill":{"type":"array","items":{"$ref":"#/components/schemas/PrefillToken"}},"seed":{"type":"integer","format":"int64","example":42,"nullable":true,"minimum":0},"tokens":{"type":"array","items":{"$ref":"#/components/schemas/Token"}},"top_tokens":{"type":"array","items":{"type":"array","items":{"$ref":"#/components/schemas/Token"}}}}},"ChatCompletion":{"type":"object","required":["id","object","created","model","system_fingerprint","choices","usage"],"properties":{"choices":{"type":"array","items":{"$ref":"#/components/schemas/ChatCompletionComplete"}},"created":{"type":"integer","format":"int64","example":"1706270835","minimum":0},"id":{"type":"string"},"model":{"type":"string","example":"mistralai/Mistral-7B-Instruct-v0.2"},"object":{"type":"string"},"system_fingerprint":{"type":"string"},"usage":{"$ref":"#/components/schemas/Usage"}}},"ChatCompletionChoice":{"type":"object","required":["index","delta"],"properties":{"delta":{"$ref":"#/components/schemas/ChatCompletionDelta"},"finish_reason":{"type":"string","nullable":true},"index":{"type":"integer","format":"int32","minimum":0},"logprobs":{"type":"number","format":"float","nullable":true}}},"ChatCompletionChunk":{"type":"object","required":["id","object","created","model","system_fingerprint","choices"],"properties":{"choices":{"type":"array","items":{"$ref":"#/components/schemas/ChatCompletionChoice"}},"created":{"type":"integer","format":"int64","example":"1706270978","minimum":0},"id":{"type":"string"},"model":{"type":"string","example":"mistralai/Mistral-7B-Instruct-v0.2"},"object":{"type":"string"},"system_fingerprint":{"type":"string"}}},"ChatCompletionDelta":{"type":"object","required":["role","content"],"properties":{"content":{"type":"string","example":"What is Deep Learning?"},"role":{"type":"string","example":"user"}}},"ChatRequest":{"type":"object","required":["model"],"properties":{"frequency_penalty":{"type":"number","format":"float","description":"Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far,\ndecreasing the model's likelihood to repeat the same line verbatim.","example":"1.0","nullable":true},"logit_bias":{"type":"array","items":{"type":"number","format":"float"},"description":"UNUSED\nModify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens\n(specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically,\nthe bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model,\nbut values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should\nresult in a ban or exclusive selection of the relevant token.","nullable":true},"logprobs":{"type":"boolean","description":"Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each\noutput token returned in the content of message.","example":"false","nullable":true},"max_tokens":{"type":"integer","format":"int32","description":"The maximum number of tokens that can be generated in the chat completion.","example":"32","nullable":true,"minimum":0},"messages":{"type":"array","items":{"$ref":"#/components/schemas/Message"},"description":"A list of messages comprising the conversation so far."},"model":{"type":"string","description":"UNUSED\nID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.","example":"mistralai/Mistral-7B-Instruct-v0.2"},"n":{"type":"integer","format":"int32","description":"UNUSED\nHow many chat completion choices to generate for each input message. Note that you will be charged based on the\nnumber of generated tokens across all of the choices. Keep n as 1 to minimize costs.","example":"2","nullable":true,"minimum":0},"presence_penalty":{"type":"number","format":"float","description":"UNUSED\nNumber between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far,\nincreasing the model's likelihood to talk about new topics","example":0.1,"nullable":true},"seed":{"type":"integer","format":"int64","example":42,"nullable":true,"minimum":0},"stream":{"type":"boolean"},"temperature":{"type":"number","format":"float","description":"What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while\nlower values like 0.2 will make it more focused and deterministic.\n\nWe generally recommend altering this or `top_p` but not both.","example":1.0,"nullable":true},"top_logprobs":{"type":"integer","format":"int32","description":"UNUSED\nAn integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with\nan associated log probability. logprobs must be set to true if this parameter is used.","example":"5","nullable":true,"minimum":0},"top_p":{"type":"number","format":"float","description":"An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the\ntokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.","example":0.95,"nullable":true}}},"CompatGenerateRequest":{"type":"object","required":["inputs"],"properties":{"inputs":{"type":"string","example":"My name is Olivier and I"},"parameters":{"$ref":"#/components/schemas/GenerateParameters"},"stream":{"type":"boolean","default":"false"}}},"Details":{"type":"object","required":["finish_reason","generated_tokens","prefill","tokens"],"properties":{"best_of_sequences":{"type":"array","items":{"$ref":"#/components/schemas/BestOfSequence"},"nullable":true},"finish_reason":{"$ref":"#/components/schemas/FinishReason"},"generated_tokens":{"type":"integer","format":"int32","example":1,"minimum":0},"prefill":{"type":"array","items":{"$ref":"#/components/schemas/PrefillToken"}},"seed":{"type":"integer","format":"int64","example":42,"nullable":true,"minimum":0},"tokens":{"type":"array","items":{"$ref":"#/components/schemas/Token"}},"top_tokens":{"type":"array","items":{"type":"array","items":{"$ref":"#/components/schemas/Token"}}}}},"ErrorResponse":{"type":"object","required":["error","error_type"],"properties":{"error":{"type":"string"},"error_type":{"type":"string"}}},"FinishReason":{"type":"string","enum":["length","eos_token","stop_sequence"],"example":"Length"},"GenerateParameters":{"type":"object","properties":{"best_of":{"type":"integer","default":"null","example":1,"nullable":true,"minimum":0,"exclusiveMinimum":0},"decoder_input_details":{"type":"boolean","default":"true"},"details":{"type":"boolean","default":"true"},"do_sample":{"type":"boolean","default":"false","example":true},"max_new_tokens":{"type":"integer","format":"int32","default":"100","example":"20","nullable":true,"minimum":0},"repetition_penalty":{"type":"number","format":"float","default":"null","example":1.03,"nullable":true,"exclusiveMinimum":0},"return_full_text":{"type":"boolean","default":"null","example":false,"nullable":true},"seed":{"type":"integer","format":"int64","default":"null","example":"null","nullable":true,"minimum":0,"exclusiveMinimum":0},"stop":{"type":"array","items":{"type":"string"},"example":["photographer"],"maxItems":4},"temperature":{"type":"number","format":"float","default":"null","example":0.5,"nullable":true,"exclusiveMinimum":0},"top_k":{"type":"integer","format":"int32","default":"null","example":10,"nullable":true,"exclusiveMinimum":0},"top_n_tokens":{"type":"integer","format":"int32","default":"null","example":5,"nullable":true,"minimum":0,"exclusiveMinimum":0},"top_p":{"type":"number","format":"float","default":"null","example":0.95,"nullable":true,"maximum":1,"exclusiveMinimum":0},"truncate":{"type":"integer","default":"null","example":"null","nullable":true,"minimum":0},"typical_p":{"type":"number","format":"float","default":"null","example":0.95,"nullable":true,"maximum":1,"exclusiveMinimum":0},"watermark":{"type":"boolean","default":"false","example":true}}},"GenerateRequest":{"type":"object","required":["inputs"],"properties":{"inputs":{"type":"string","example":"My name is Olivier and I"},"parameters":{"$ref":"#/components/schemas/GenerateParameters"}}},"GenerateResponse":{"type":"object","required":["generated_text"],"properties":{"details":{"allOf":[{"$ref":"#/components/schemas/Details"}],"nullable":true},"generated_text":{"type":"string","example":"test"}}},"Info":{"type":"object","required":["model_id","model_dtype","model_device_type","max_concurrent_requests","max_best_of","max_stop_sequences","max_input_length","max_total_tokens","waiting_served_ratio","max_batch_total_tokens","max_waiting_tokens","validation_workers","version"],"properties":{"docker_label":{"type":"string","example":"null","nullable":true},"max_batch_total_tokens":{"type":"integer","format":"int32","example":"32000","minimum":0},"max_best_of":{"type":"integer","example":"2","minimum":0},"max_concurrent_requests":{"type":"integer","description":"Router Parameters","example":"128","minimum":0},"max_input_length":{"type":"integer","example":"1024","minimum":0},"max_stop_sequences":{"type":"integer","example":"4","minimum":0},"max_total_tokens":{"type":"integer","example":"2048","minimum":0},"max_waiting_tokens":{"type":"integer","example":"20","minimum":0},"model_device_type":{"type":"string","example":"cuda"},"model_dtype":{"type":"string","example":"torch.float16"},"model_id":{"type":"string","description":"Model info","example":"bigscience/blomm-560m"},"model_pipeline_tag":{"type":"string","example":"text-generation","nullable":true},"model_sha":{"type":"string","example":"e985a63cdc139290c5f700ff1929f0b5942cced2","nullable":true},"sha":{"type":"string","example":"null","nullable":true},"validation_workers":{"type":"integer","example":"2","minimum":0},"version":{"type":"string","description":"Router Info","example":"0.5.0"},"waiting_served_ratio":{"type":"number","format":"float","example":"1.2"}}},"Message":{"type":"object","required":["role","content"],"properties":{"content":{"type":"string","example":"My name is David and I"},"role":{"type":"string","example":"user"}}},"PrefillToken":{"type":"object","required":["id","text","logprob"],"properties":{"id":{"type":"integer","format":"int32","example":0,"minimum":0},"logprob":{"type":"number","format":"float","example":-0.34,"nullable":true},"text":{"type":"string","example":"test"}}},"SimpleToken":{"type":"object","required":["id","text","start","stop"],"properties":{"id":{"type":"integer","format":"int32","example":0,"minimum":0},"start":{"type":"integer","example":0,"minimum":0},"stop":{"type":"integer","example":2,"minimum":0},"text":{"type":"string","example":"test"}}},"StreamDetails":{"type":"object","required":["finish_reason","generated_tokens"],"properties":{"finish_reason":{"$ref":"#/components/schemas/FinishReason"},"generated_tokens":{"type":"integer","format":"int32","example":1,"minimum":0},"seed":{"type":"integer","format":"int64","example":42,"nullable":true,"minimum":0}}},"StreamResponse":{"type":"object","required":["index","token"],"properties":{"details":{"allOf":[{"$ref":"#/components/schemas/StreamDetails"}],"default":"null","nullable":true},"generated_text":{"type":"string","default":"null","example":"test","nullable":true},"index":{"type":"integer","format":"int32","minimum":0},"token":{"$ref":"#/components/schemas/Token"},"top_tokens":{"type":"array","items":{"$ref":"#/components/schemas/Token"}}}},"Token":{"type":"object","required":["id","text","logprob","special"],"properties":{"id":{"type":"integer","format":"int32","example":0,"minimum":0},"logprob":{"type":"number","format":"float","example":-0.34,"nullable":true},"special":{"type":"boolean","example":"false"},"text":{"type":"string","example":"test"}}},"TokenizeResponse":{"type":"array","items":{"$ref":"#/components/schemas/SimpleToken"}}}},"tags":[{"name":"Text Generation Inference","description":"Hugging Face Text Generation Inference API"}]}
"description":"Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far,\ndecreasing the model's likelihood to repeat the same line verbatim.",
"example":"1.0",
"nullable":true
},
"logit_bias":{
"type":"array",
"items":{
"type":"number",
"format":"float"
},
"description":"UNUSED\nModify the likelihood of specified tokens appearing in the completion. Accepts a JSON object that maps tokens\n(specified by their token ID in the tokenizer) to an associated bias value from -100 to 100. Mathematically,\nthe bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model,\nbut values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should\nresult in a ban or exclusive selection of the relevant token.",
"nullable":true
},
"logprobs":{
"type":"boolean",
"description":"Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each\noutput token returned in the content of message.",
"example":"false",
"nullable":true
},
"max_tokens":{
"type":"integer",
"format":"int32",
"description":"The maximum number of tokens that can be generated in the chat completion.",
"example":"32",
"nullable":true,
"minimum":0
},
"messages":{
"type":"array",
"items":{
"$ref":"#/components/schemas/Message"
},
"description":"A list of messages comprising the conversation so far."
},
"model":{
"type":"string",
"description":"UNUSED\nID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.",
"example":"mistralai/Mistral-7B-Instruct-v0.2"
},
"n":{
"type":"integer",
"format":"int32",
"description":"UNUSED\nHow many chat completion choices to generate for each input message. Note that you will be charged based on the\nnumber of generated tokens across all of the choices. Keep n as 1 to minimize costs.",
"example":"2",
"nullable":true,
"minimum":0
},
"presence_penalty":{
"type":"number",
"format":"float",
"description":"UNUSED\nNumber between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far,\nincreasing the model's likelihood to talk about new topics",
"example":0.1,
"nullable":true
},
"seed":{
"type":"integer",
"format":"int64",
"example":42,
"nullable":true,
"minimum":0
},
"stream":{
"type":"boolean"
},
"temperature":{
"type":"number",
"format":"float",
"description":"What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while\nlower values like 0.2 will make it more focused and deterministic.\n\nWe generally recommend altering this or `top_p` but not both.",
"example":1.0,
"nullable":true
},
"top_logprobs":{
"type":"integer",
"format":"int32",
"description":"UNUSED\nAn integer between 0 and 5 specifying the number of most likely tokens to return at each token position, each with\nan associated log probability. logprobs must be set to true if this parameter is used.",
"example":"5",
"nullable":true,
"minimum":0
},
"top_p":{
"type":"number",
"format":"float",
"description":"An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the\ntokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.",
@@ -8,7 +8,7 @@ Let's say you want to deploy [Falcon-7B Instruct](https://huggingface.co/tiiuae/
model=tiiuae/falcon-7b-instruct
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 1g -p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.3--model-id$model
docker run --gpus all --shm-size 1g -p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.4--model-id$model
```
<Tipwarning={true}>
...
...
@@ -20,7 +20,7 @@ To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://d
TGI also supports ROCm-enabled AMD GPUs (only MI210 and MI250 are tested), details are available in the [Supported Hardware section](./supported_models#supported-hardware) and [AMD documentation](https://rocm.docs.amd.com/en/latest/deploy/docker.html). To launch TGI on ROCm GPUs, please use instead:
```bash
docker run --cap-add=SYS_PTRACE --security-optseccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 1g -p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.3-rocm --model-id$model
docker run --cap-add=SYS_PTRACE --security-optseccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 1g -p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.4-rocm --model-id$model
```
Once TGI is running, you can use the `generate` endpoint by doing requests. To learn more about how to query the endpoints, check the [Consuming TGI](./basic_tutorials/consuming_tgi) section, where we show examples with utility libraries and UIs. Below you can see a simple snippet to query the endpoint.
...
...
@@ -91,7 +91,7 @@ curl 127.0.0.1:8080/generate \
To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
```bash
docker run ghcr.io/huggingface/text-generation-inference:1.3--help
docker run ghcr.io/huggingface/text-generation-inference:1.4--help
