Synchronize the file contents updated by the meta-llama official:
Browse filestokenizer.json:
Update post-processor to add bos
generation_config.json:
Update generation_config.json
README.md:
Update widget inference parameters
- README.md +29 -23
- config.json +1 -1
- generation_config.json +4 -1
- tokenizer.json +63 -4
README.md
CHANGED
@@ -189,6 +189,29 @@ extra_gated_fields:
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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
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extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
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extra_gated_button_content: Submit
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---
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## meta-llama/Meta-Llama-3-8B-Instruct for CTranslate2
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## How to use
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This repository for use with [CTranslate2](https://github.com/OpenNMT/CTranslate2)
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### Use with CTranslate2
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This example code is obtained from [CTranslate2_transformers](https://opennmt.net/CTranslate2/guides/transformers.html#mpt)
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More detailed information about the `generate_batch` methon can be found at [CTranslate2_Generator.generate_batch](https://opennmt.net/CTranslate2/python/ctranslate2.Generator.html#ctranslate2.Generator.generate_batch).
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```python
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import transformers
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model_id = "avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16"
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
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{"role": "user", "content": "Who are you?"},
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]
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(input_ids))
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results = model.generate_batch([input_tokens], include_prompt_in_result=False, max_length=256, sampling_temperature=0.6, sampling_topp=0.9, end_token=terminators)
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output = tokenizer.decode(results[0].sequences_ids[0])
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print(output)
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```
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## Hardware and Software
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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
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extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
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extra_gated_button_content: Submit
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widget:
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- example_title: Hello
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messages:
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- role: user
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content: Hey my name is Julien! How are you?
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- example_title: Winter holidays
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messages:
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- role: system
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content: You are a helpful and honest assistant. Please, respond concisely and truthfully.
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- role: user
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content: Can you recommend a good destination for Winter holidays?
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- example_title: Programming assistant
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messages:
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- role: system
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content: You are a helpful and honest code and programming assistant. Please, respond concisely and truthfully.
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- role: user
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content: Write a function that computes the nth fibonacci number.
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inference:
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parameters:
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max_new_tokens: 300
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stop:
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- <|end_of_text|>
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- <|eot_id|>
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---
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## meta-llama/Meta-Llama-3-8B-Instruct for CTranslate2
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## How to use
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This repository for use with `[CTranslate2](https://github.com/OpenNMT/CTranslate2)`.
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### Use with CTranslate2
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This example code is obtained from [CTranslate2_transformers](https://opennmt.net/CTranslate2/guides/transformers.html#mpt).
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More detailed information about the `generate_batch` methon can be found at [CTranslate2_Generator.generate_batch](https://opennmt.net/CTranslate2/python/ctranslate2.Generator.html#ctranslate2.Generator.generate_batch).
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```python
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import transformers
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model_id = "avans06/Meta-Llama-3-8B-Instruct-ct2-int8_float16"
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generator = ctranslate2.Generator(model_id, device="auto", compute_type="int8_float16")
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tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
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prompt = "What is the meaning of Large language model?"
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input_tokens = tokenizer.convert_ids_to_tokens(tokenizer.encode(prompt))
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results = generator.generate_batch([input_tokens], include_prompt_in_result=False)
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output = tokenizer.decode(results[0].sequences_ids[0])
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```
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## Hardware and Software
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config.json
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"eos_token": "<|end_of_text|>",
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"layer_norm_epsilon": 1e-05,
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"multi_query_attention": true,
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"unk_token": "
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}
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"eos_token": "<|end_of_text|>",
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"layer_norm_epsilon": 1e-05,
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"multi_query_attention": true,
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"unk_token": ""
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 128000,
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"eos_token_id": [128001, 128009],
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"transformers_version": "4.40.0.dev0"
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}
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{
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"bos_token_id": 128000,
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"eos_token_id": [128001, 128009],
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"do_sample": true,
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"temperature": 0.6,
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"max_length": 4096,
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"top_p": 0.9,
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"transformers_version": "4.40.0.dev0"
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}
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tokenizer.json
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]
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},
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"post_processor": {
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"type": "
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"
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},
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"decoder": {
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"type": "ByteLevel",
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]
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},
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"post_processor": {
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"type": "Sequence",
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"processors": [
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{
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"type": "ByteLevel",
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"add_prefix_space": true,
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"trim_offsets": false,
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"use_regex": true
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},
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{
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"type": "TemplateProcessing",
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"single": [
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{
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"SpecialToken": {
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"id": "<|begin_of_text|>",
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"type_id": 0
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},
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"Sequence": {
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"id": "A",
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"type_id": 0
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],
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"pair": [
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{
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"SpecialToken": {
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"id": "<|begin_of_text|>",
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"type_id": 0
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}
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},
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{
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"Sequence": {
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"id": "A",
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"type_id": 0
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}
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},
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{
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"SpecialToken": {
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"id": "<|begin_of_text|>",
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"type_id": 1
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}
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},
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{
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"Sequence": {
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"id": "B",
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"type_id": 1
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}
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}
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],
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"special_tokens": {
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"<|begin_of_text|>": {
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"id": "<|begin_of_text|>",
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"ids": [
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128000
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],
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"tokens": [
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"<|begin_of_text|>"
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]
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}
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}
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}
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]
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},
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"decoder": {
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"type": "ByteLevel",
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