Spaces:
Running
Running
File size: 4,270 Bytes
c5e73ca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
# TGI
The local tokenizer config can be supplied to TGI through the flag `--tokenizer-config-path`, documented [here](https://huggingface.co/docs/text-generation-inference/basic_tutorials/launcher#tokenizerconfigpath).
# vLLM
A local chat template can be supplied to vLLM through the flag `--chat-template`. It is not explicitly documented, but can be found mentioned in GitHub Issues relating to the topic.
# Llama-2 models on TGI
There is a [known bug with TGI](https://github.com/huggingface/text-generation-inference/issues/1534) in which the default `tokenizer_config.json` is not handled properly by TGI by applying chat templating. While this is resolved, we are using a modified `tokenizer_config.json` that is compatible with TGI. Note that the chat templating jinja itself the same, with the exception of removing 2 calls to `.strip()`, which TGI reports errors on.
For reference, here is the original unmodified chat template:
```
{% if messages[0]['role'] == 'system' %}
{% set loop_messages = messages[1:] %}
{% set system_message = messages[0]['content'] %}
{% else %}
{% set loop_messages = messages %}
{% set system_message = false %}
{% endif %}
{% for message in loop_messages %}
{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}
{% endif %}
{% if loop.index0 == 0 and system_message != false %}
{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}
{% else %}
{% set content = message['content'] %}
{% endif %}
{% if message['role'] == 'user' %}
{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}
{% elif message['role'] == 'assistant' %}
{{ ' ' + content.strip() + ' ' + eos_token }}
{% endif %}
{% endfor %}
```
We also note that the `eos_token` and `bos_token` are originally provided as maps, but the TGI implementation only accepts a string. So we also modify them to only contain the `content` string.
For reference, here is the original unmodified `tokenizer_config.json`:
```
{
"add_bos_token": true,
"add_eos_token": false,
"bos_token": {
"__type": "AddedToken",
"content": "<s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content + ' ' + eos_token }}{% endif %}{% endfor %}",
"clean_up_tokenization_spaces": false,
"eos_token": {
"__type": "AddedToken",
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"legacy": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": null,
"padding_side": "right",
"sp_model_kwargs": {},
"tokenizer_class": "LlamaTokenizer",
"unk_token": {
"__type": "AddedToken",
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}
```
# Mistral with chat templating
Mistral for chatting has not been explicitly trained using a distinct system prompt. Therefore, the default Mistral `tokenizer_config.json` explicitly assumes that the system role does not exist. To keep our benchmarks consistent across models, we reenginered the original Mistral chat template to account for a system prompt. We simply preppend the system prompt to the first user prompt in a given conversation.
|