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---
license: other
license_name: deepseek-license
license_link: LICENSE
---
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->
<div align="center">
<img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
</div>
<hr>
<div align="center" style="line-height: 1;">
<a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
<img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
<img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
<img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
<img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<div align="center" style="line-height: 1;">
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;">
<img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
<a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;">
<img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
</a>
</div>
<p align="center">
<a href="#4-api-platform">API Platform</a> |
<a href="#5-how-to-run-locally">How to Use</a> |
<a href="#6-license">License</a> |
</p>
<p align="center">
<a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><b>Paper Link</b>👁️</a>
</p>
# DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
## 1. Introduction
We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K.
<p align="center">
<img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true">
</p>
In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks. The list of supported programming languages can be found [here](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/supported_langs.txt).
## 2. Model Downloads
We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public.
<div align="center">
| **Model** | **#Total Params** | **#Active Params** | **Context Length** | **Download** |
| :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: |
| DeepSeek-Coder-V2-Lite-Base | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) |
| DeepSeek-Coder-V2-Lite-Instruct | 16B | 2.4B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) |
| DeepSeek-Coder-V2-Base | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) |
| DeepSeek-Coder-V2-Instruct | 236B | 21B | 128k | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) |
</div>
## 3. Chat Website
You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)
## 4. API Platform
We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price.
<p align="center">
<img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true">
</p>
## 5. How to run locally
**Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.**
### Inference with Huggingface's Transformers
You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
#### Code Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
#### Code Insertion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<｜fim▁hole｜>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
```
#### Chat Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
messages=[
{ 'role': 'user', 'content': "write a quick sort algorithm in python."}
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
# tokenizer.eos_token_id is the id of <｜end▁of▁sentence｜> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
```
The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
An example of chat template is as belows:
```bash
<｜begin▁of▁sentence｜>User: {user_message_1}
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
Assistant:
```
You can also add an optional system message:
```bash
<｜begin▁of▁sentence｜>{system_message}
User: {user_message_1}
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
Assistant:
```
### Inference with vLLM (recommended)
To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
max_model_len, tp_size = 8192, 1
model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)
sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
messages_list = [
[{"role": "user", "content": "Who are you?"}],
[{"role": "user", "content": "write a quick sort algorithm in python."}],
[{"role": "user", "content": "Write a piece of quicksort code in C++."}],
]
prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
generated_text = [output.outputs[0].text for output in outputs]
print(generated_text)
```
## 6. License
This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use.
## 7. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).

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{
"architectures": [
"DeepseekV2ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_deepseek.DeepseekV2Config",
"AutoModel": "modeling_deepseek.DeepseekV2Model",
"AutoModelForCausalLM": "modeling_deepseek.DeepseekV2ForCausalLM"
},
"aux_loss_alpha": 0.001,
"bos_token_id": 100000,
"eos_token_id": 100001,
"first_k_dense_replace": 1,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 10944,
"kv_lora_rank": 512,
"max_position_embeddings": 163840,
"model_type": "deepseek_v2",
"moe_intermediate_size": 1408,
"moe_layer_freq": 1,
"n_group": 1,
"n_routed_experts": 64,
"n_shared_experts": 2,
"norm_topk_prob": false,
"num_attention_heads": 16,
"num_experts_per_tok": 6,
"num_hidden_layers": 27,
"num_key_value_heads": 16,
"pretraining_tp": 1,
"q_lora_rank": null,
"qk_nope_head_dim": 128,
"qk_rope_head_dim": 64,
"rms_norm_eps": 1e-06,
"rope_scaling": {
"beta_fast": 32,
"beta_slow": 1,
"factor": 40,
"mscale": 0.707,
"mscale_all_dim": 0.707,
"original_max_position_embeddings": 4096,
"type": "yarn"
},
"rope_theta": 10000,
"routed_scaling_factor": 1.0,
"scoring_func": "softmax",
"seq_aux": true,
"tie_word_embeddings": false,
"topk_group": 1,
"topk_method": "greedy",
"torch_dtype": "bfloat16",
"transformers_version": "4.39.3",
"use_cache": true,
"v_head_dim": 128,
"vocab_size": 102400
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
DEEPSEEK_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class DeepseekV2Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DeepseekV2Model`]. It is used to instantiate an DeepSeek
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the DeepSeek-V2.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 102400):
Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`DeepseekV2Model`]
hidden_size (`int`, *optional*, defaults to 4096):
Dimension of the hidden representations.
intermediate_size (`int`, *optional*, defaults to 11008):
Dimension of the MLP representations.
moe_intermediate_size (`int`, *optional*, defaults to 1407):
Dimension of the MoE representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Transformer decoder.
n_shared_experts (`int`, *optional*, defaults to None):
Number of shared experts, None means dense model.
n_routed_experts (`int`, *optional*, defaults to None):
Number of routed experts, None means dense model.
routed_scaling_factor (`float`, *optional*, defaults to 1.0):
Scaling factor or routed experts.
topk_method (`str`, *optional*, defaults to `gready`):
Topk method used in routed gate.
n_group (`int`, *optional*, defaults to None):
Number of groups for routed experts.
topk_group (`int`, *optional*, defaults to None):
Number of selected groups for each token(for each token, ensuring the selected experts is only within `topk_group` groups).
num_experts_per_tok (`int`, *optional*, defaults to None):
Number of selected experts, None means dense model.
moe_layer_freq (`int`, *optional*, defaults to 1):
The frequency of the MoE layer: one expert layer for every `moe_layer_freq - 1` dense layers.
first_k_dense_replace (`int`, *optional*, defaults to 0):
Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
\--k dense layers--/
norm_topk_prob (`bool`, *optional*, defaults to False):
Whether to normalize the weights of the routed experts.
scoring_func (`str`, *optional*, defaults to 'softmax'):
Method of computing expert weights.
aux_loss_alpha (`float`, *optional*, defaults to 0.001):
Auxiliary loss weight coefficient.
seq_aux = (`bool`, *optional*, defaults to True):
Whether to compute the auxiliary loss for each individual sample.
num_key_value_heads (`int`, *optional*):
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
`num_attention_heads`.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the decoder.
max_position_embeddings (`int`, *optional*, defaults to 2048):
The maximum sequence length that this model might ever be used with.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *optional*):
Padding token id.
bos_token_id (`int`, *optional*, defaults to 1):
Beginning of stream token id.
eos_token_id (`int`, *optional*, defaults to 2):
End of stream token id.
pretraining_tp (`int`, *optional*, defaults to 1):
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
issue](https://github.com/pytorch/pytorch/issues/76232).
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether to tie weight embeddings
rope_theta (`float`, *optional*, defaults to 10000.0):
The base period of the RoPE embeddings.
rope_scaling (`Dict`, *optional*):
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
`max_position_embeddings` to the expected new maximum.
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
Whether to use a bias in the query, key, value and output projection layers during self-attention.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import DeepseekV2Model, DeepseekV2Config
>>> # Initializing a Deepseek-V2 style configuration
>>> configuration = DeepseekV2Config()
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "deepseek_v2"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=102400,
hidden_size=4096,
intermediate_size=11008,
moe_intermediate_size = 1407,
num_hidden_layers=30,
num_attention_heads=32,
num_key_value_heads=32,
n_shared_experts = None,
n_routed_experts = None,
ep_size = 1,
routed_scaling_factor = 1.0,
kv_lora_rank = 512,
q_lora_rank = 1536,
qk_rope_head_dim = 64,
v_head_dim = 128,
qk_nope_head_dim = 128,
topk_method = 'gready',
n_group = None,
topk_group = None,
num_experts_per_tok = None,
moe_layer_freq = 1,
first_k_dense_replace = 0,
norm_topk_prob = False,
scoring_func = 'softmax',
aux_loss_alpha = 0.001,
seq_aux = True,
hidden_act="silu",
max_position_embeddings=2048,
initializer_range=0.02,
rms_norm_eps=1e-6,
use_cache=True,
pad_token_id=None,
bos_token_id=100000,
eos_token_id=100001,
pretraining_tp=1,
tie_word_embeddings=False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias=False,
attention_dropout=0.0,
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.moe_intermediate_size = moe_intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_shared_experts = n_shared_experts
self.n_routed_experts = n_routed_experts
self.ep_size = ep_size
self.routed_scaling_factor = routed_scaling_factor
self.kv_lora_rank = kv_lora_rank
self.q_lora_rank = q_lora_rank
self.qk_rope_head_dim = qk_rope_head_dim
self.v_head_dim = v_head_dim
self.qk_nope_head_dim = qk_nope_head_dim
self.topk_method = topk_method
self.n_group = n_group
self.topk_group = topk_group
self.num_experts_per_tok = num_experts_per_tok
self.moe_layer_freq = moe_layer_freq
self.first_k_dense_replace = first_k_dense_replace
self.norm_topk_prob = norm_topk_prob
self.scoring_func = scoring_func
self.aux_loss_alpha = aux_loss_alpha
self.seq_aux = seq_aux
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.pretraining_tp = pretraining_tp
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_bias = attention_bias
self.attention_dropout = attention_dropout
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)

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{
"_from_model_config": true,
"bos_token_id": 100000,
"eos_token_id": 100001,
"do_sample": true,
"temperature": 0.3,
"top_p": 0.95,
"transformers_version": "4.39.3"
}

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version https://git-lfs.github.com/spec/v1
oid sha256:75d08ddaf92b68f751c95e1b4a51dbf5c011d5692f97cc0d71bd32587a3ea8d9
size 8594887410

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version https://git-lfs.github.com/spec/v1
oid sha256:7bf22dfa271527f7a0b8dbd56592722cd8fdcfeb6aad32ebb1110d21882eb1d8
size 8591757456

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version https://git-lfs.github.com/spec/v1
oid sha256:18f5a20f4d737b496e03ff8761834dfa9754ceedd56f54a336d0eab5e0e20968
size 8590718535

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version https://git-lfs.github.com/spec/v1
oid sha256:1365ca25494e6592b6cb11f62f4a63cbdcdd9853e01d67f274d0b282732cc5cd
size 5636263208

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from typing import List, Optional, Union
from transformers.models.llama import LlamaTokenizerFast
class DeepseekTokenizerFast(LlamaTokenizerFast):
def convert_ids_to_tokens(
self, ids: Union[int, List[int]], skip_special_tokens: bool = False
) -> Union[str, List[str]]:
"""
Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
added tokens.
Args:
ids (`int` or `List[int]`):
The token id (or token ids) to convert to tokens.
skip_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not to remove special tokens in the decoding.
Returns:
`str` or `List[str]`: The decoded token(s).
"""
if isinstance(ids, int):
return self._convert_id_to_token(ids)
tokens = []
for index in ids:
index = int(index)
if skip_special_tokens and index in self.all_special_ids:
continue
token = self._tokenizer.id_to_token(index)
tokens.append(token if token is not None else "")
return tokens
def _convert_id_to_token(self, index: int) -> Optional[str]:
token = self._tokenizer.id_to_token(int(index))
return token if token is not None else ""

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{
"add_bos_token": true,
"add_eos_token": false,
"bos_token": {
"__type": "AddedToken",
"content": "<｜begin▁of▁sentence｜>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"clean_up_tokenization_spaces": false,
"eos_token": {
"__type": "AddedToken",
"content": "<｜end▁of▁sentence｜>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"legacy": true,
"model_max_length": 16384,
"pad_token": {
"__type": "AddedToken",
"content": "<｜end▁of▁sentence｜>",
"lstrip": false,
"normalized": true,
"rstrip": false,
"single_word": false
},
"sp_model_kwargs": {},
"unk_token": null,
"tokenizer_class": "LlamaTokenizerFast",
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
}