metadata
base_model: mistralai/Mistral-Nemo-Base-2407
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
license: apache-2.0
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
model-index:
- name: Mistral-Nemo-Instruct-2407
results:
- task:
type: squad_answerable-judge
dataset:
name: squad_answerable
type: multi-choices
metrics:
- type: judge_match
value: '0.685'
args:
results:
squad_answerable-judge:
exact_match,strict_match: 0.6852522530110334
exact_match_stderr,strict_match: 0.004262305820311226
alias: squad_answerable-judge
context_has_answer-judge:
exact_match,strict_match: 0.7906976744186046
exact_match_stderr,strict_match: 0.04412480456048906
alias: context_has_answer-judge
group_subtasks:
context_has_answer-judge: []
squad_answerable-judge: []
configs:
context_has_answer-judge:
task: context_has_answer-judge
group: dg
dataset_path: DataGuard/eval-multi-choices
dataset_name: context_has_answer_judge
test_split: test
doc_to_text: >-
<s>[INST]You are asked to determine if a question has the
answer in the context, and answer with a simple Yes or No.
Example:
Question: How is the weather today? Context: How is the
traffic today? It is horrible. Does the question have the
answer in the Context?
Answer: No
Question: How is the weather today? Context: Is the weather
good today? Yes, it is sunny. Does the question have the
answer in the Context?
Answer: Yes
Question: {{question}}
Context: {{similar_question}} {{similar_answer}}
Does the question have the answer in the Context?
[/INST]
doc_to_target: '{{''Yes'' if is_relevant in [''Yes'', 1] else ''No''}}'
description: ''
target_delimiter: ' '
fewshot_delimiter: |+
metric_list:
- metric: exact_match
output_type: generate_until
generation_kwargs:
until:
- <|im_end|>
do_sample: false
temperature: 0.3
repeats: 1
filter_list:
- name: strict_match
filter:
- function: regex
regex_pattern: Yes|No
group_select: -1
- function: take_first
should_decontaminate: false
squad_answerable-judge:
task: squad_answerable-judge
group: dg
dataset_path: DataGuard/eval-multi-choices
dataset_name: squad_answerable_judge
test_split: test
doc_to_text: >-
<s>[INST]You are asked to determine if a question has the
answer in the context, and answer with a simple Yes or No.
Example:
Question: How is the weather today? Context: The traffic is
horrible. Does the question have the answer in the Context?
Answer: No
Question: How is the weather today? Context: The weather is
good. Does the question have the answer in the Context?
Answer: Yes
Question: {{question}}
Context: {{context}}
Does the question have the answer in the Context?
[/INST]
doc_to_target: '{{''Yes'' if is_relevant in [''Yes'', 1] else ''No''}}'
description: ''
target_delimiter: ' '
fewshot_delimiter: |+
metric_list:
- metric: exact_match
output_type: generate_until
generation_kwargs:
until:
- <|im_end|>
do_sample: false
temperature: 0.3
repeats: 1
filter_list:
- name: strict_match
filter:
- function: regex
regex_pattern: Yes|No
group_select: -1
- function: take_first
should_decontaminate: false
versions:
context_has_answer-judge: Yaml
squad_answerable-judge: Yaml
n-shot: {}
config:
model: vllm
model_args: >-
pretrained=mistralai/Mistral-Nemo-Instruct-2407,tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.8,max_model_len=2048,trust_remote_code=True
batch_size: auto
batch_sizes: []
bootstrap_iters: 100000
git_hash: cddf85d
pretty_env_info: >-
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.35
Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC
11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-149-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA L40
Nvidia driver version: 535.54.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits
virtual
Byte Order: Little Endian
CPU(s): 256
On-line CPU(s) list: 0-254
Off-line CPU(s) list: 255
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7773X 64-Core
Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 64
Socket(s): 2
Stepping: 2
Frequency boost: enabled
CPU max MHz: 2200.0000
CPU min MHz: 0.0000
BogoMIPS: 4400.14
Flags: fpu vme de pse tsc msr pae mce
cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse
sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm
constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid
aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1
sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm
cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse
3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core
perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3
invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall
fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap
clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc
cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf
xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale
vmcb_clean flushbyasid decodeassists pausefilter pfthreshold
v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid
overflow_recov succor smca
Virtualization: AMD-V
L1d cache: 4 MiB (128 instances)
L1i cache: 4 MiB (128 instances)
L2 cache: 64 MiB (128 instances)
L3 cache: 1.5 GiB (16 instances)
NUMA node(s): 16
NUMA node0 CPU(s): 0-7,128-135
NUMA node1 CPU(s): 8-15,136-143
NUMA node2 CPU(s): 16-23,144-151
NUMA node3 CPU(s): 24-31,152-159
NUMA node4 CPU(s): 32-39,160-167
NUMA node5 CPU(s): 40-47,168-175
NUMA node6 CPU(s): 48-55,176-183
NUMA node7 CPU(s): 56-63,184-191
NUMA node8 CPU(s): 64-71,192-199
NUMA node9 CPU(s): 72-79,200-207
NUMA node10 CPU(s): 80-87,208-215
NUMA node11 CPU(s): 88-95,216-223
NUMA node12 CPU(s): 96-103,224-231
NUMA node13 CPU(s): 104-111,232-239
NUMA node14 CPU(s): 112-119,240-247
NUMA node15 CPU(s): 120-127,248-254
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store
Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs
barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB
conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS
Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] torch==2.4.0
[pip3] torchaudio==2.0.2+cu118
[pip3] torchvision==0.19.0
[pip3] triton==3.0.0
[conda] Could not collect
transformers_version: 4.44.1
- task:
type: context_has_answer-judge
dataset:
name: context_has_answer
type: multi-choices
metrics:
- type: judge_match
value: '0.791'
args:
results:
squad_answerable-judge:
exact_match,strict_match: 0.6852522530110334
exact_match_stderr,strict_match: 0.004262305820311226
alias: squad_answerable-judge
context_has_answer-judge:
exact_match,strict_match: 0.7906976744186046
exact_match_stderr,strict_match: 0.04412480456048906
alias: context_has_answer-judge
group_subtasks:
context_has_answer-judge: []
squad_answerable-judge: []
configs:
context_has_answer-judge:
task: context_has_answer-judge
group: dg
dataset_path: DataGuard/eval-multi-choices
dataset_name: context_has_answer_judge
test_split: test
doc_to_text: >-
<s>[INST]You are asked to determine if a question has the
answer in the context, and answer with a simple Yes or No.
Example:
Question: How is the weather today? Context: How is the
traffic today? It is horrible. Does the question have the
answer in the Context?
Answer: No
Question: How is the weather today? Context: Is the weather
good today? Yes, it is sunny. Does the question have the
answer in the Context?
Answer: Yes
Question: {{question}}
Context: {{similar_question}} {{similar_answer}}
Does the question have the answer in the Context?
[/INST]
doc_to_target: '{{''Yes'' if is_relevant in [''Yes'', 1] else ''No''}}'
description: ''
target_delimiter: ' '
fewshot_delimiter: |+
metric_list:
- metric: exact_match
output_type: generate_until
generation_kwargs:
until:
- <|im_end|>
do_sample: false
temperature: 0.3
repeats: 1
filter_list:
- name: strict_match
filter:
- function: regex
regex_pattern: Yes|No
group_select: -1
- function: take_first
should_decontaminate: false
squad_answerable-judge:
task: squad_answerable-judge
group: dg
dataset_path: DataGuard/eval-multi-choices
dataset_name: squad_answerable_judge
test_split: test
doc_to_text: >-
<s>[INST]You are asked to determine if a question has the
answer in the context, and answer with a simple Yes or No.
Example:
Question: How is the weather today? Context: The traffic is
horrible. Does the question have the answer in the Context?
Answer: No
Question: How is the weather today? Context: The weather is
good. Does the question have the answer in the Context?
Answer: Yes
Question: {{question}}
Context: {{context}}
Does the question have the answer in the Context?
[/INST]
doc_to_target: '{{''Yes'' if is_relevant in [''Yes'', 1] else ''No''}}'
description: ''
target_delimiter: ' '
fewshot_delimiter: |+
metric_list:
- metric: exact_match
output_type: generate_until
generation_kwargs:
until:
- <|im_end|>
do_sample: false
temperature: 0.3
repeats: 1
filter_list:
- name: strict_match
filter:
- function: regex
regex_pattern: Yes|No
group_select: -1
- function: take_first
should_decontaminate: false
versions:
context_has_answer-judge: Yaml
squad_answerable-judge: Yaml
n-shot: {}
config:
model: vllm
model_args: >-
pretrained=mistralai/Mistral-Nemo-Instruct-2407,tensor_parallel_size=1,dtype=auto,gpu_memory_utilization=0.8,max_model_len=2048,trust_remote_code=True
batch_size: auto
batch_sizes: []
bootstrap_iters: 100000
git_hash: cddf85d
pretty_env_info: >-
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.35
Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC
11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-149-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA L40
Nvidia driver version: 535.54.03
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits
virtual
Byte Order: Little Endian
CPU(s): 256
On-line CPU(s) list: 0-254
Off-line CPU(s) list: 255
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7773X 64-Core
Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 64
Socket(s): 2
Stepping: 2
Frequency boost: enabled
CPU max MHz: 2200.0000
CPU min MHz: 0.0000
BogoMIPS: 4400.14
Flags: fpu vme de pse tsc msr pae mce
cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse
sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm
constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid
aperfmperf pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1
sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm
cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse
3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core
perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3
invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall
fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap
clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc
cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf
xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale
vmcb_clean flushbyasid decodeassists pausefilter pfthreshold
v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid
overflow_recov succor smca
Virtualization: AMD-V
L1d cache: 4 MiB (128 instances)
L1i cache: 4 MiB (128 instances)
L2 cache: 64 MiB (128 instances)
L3 cache: 1.5 GiB (16 instances)
NUMA node(s): 16
NUMA node0 CPU(s): 0-7,128-135
NUMA node1 CPU(s): 8-15,136-143
NUMA node2 CPU(s): 16-23,144-151
NUMA node3 CPU(s): 24-31,152-159
NUMA node4 CPU(s): 32-39,160-167
NUMA node5 CPU(s): 40-47,168-175
NUMA node6 CPU(s): 48-55,176-183
NUMA node7 CPU(s): 56-63,184-191
NUMA node8 CPU(s): 64-71,192-199
NUMA node9 CPU(s): 72-79,200-207
NUMA node10 CPU(s): 80-87,208-215
NUMA node11 CPU(s): 88-95,216-223
NUMA node12 CPU(s): 96-103,224-231
NUMA node13 CPU(s): 104-111,232-239
NUMA node14 CPU(s): 112-119,240-247
NUMA node15 CPU(s): 120-127,248-254
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store
Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs
barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, IBPB
conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS
Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==1.24.1
[pip3] torch==2.4.0
[pip3] torchaudio==2.0.2+cu118
[pip3] torchvision==0.19.0
[pip3] triton==3.0.0
[conda] Could not collect
transformers_version: 4.44.1
Needle in a Haystack Evaluation Heatmap
Model Card for Mistral-Nemo-Instruct-2407
The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
For more details about this model please refer to our release blog post.
Key features
- Released under the Apache 2 License
- Pre-trained and instructed versions
- Trained with a 128k context window
- Trained on a large proportion of multilingual and code data
- Drop-in replacement of Mistral 7B
Model Architecture
Mistral Nemo is a transformer model, with the following architecture choices:
- Layers: 40
- Dim: 5,120
- Head dim: 128
- Hidden dim: 14,336
- Activation Function: SwiGLU
- Number of heads: 32
- Number of kv-heads: 8 (GQA)
- Vocabulary size: 2**17 ~= 128k
- Rotary embeddings (theta = 1M)
Metrics
Main Benchmarks
| Benchmark | Score |
|---|---|
| HellaSwag (0-shot) | 83.5% |
| Winogrande (0-shot) | 76.8% |
| OpenBookQA (0-shot) | 60.6% |
| CommonSenseQA (0-shot) | 70.4% |
| TruthfulQA (0-shot) | 50.3% |
| MMLU (5-shot) | 68.0% |
| TriviaQA (5-shot) | 73.8% |
| NaturalQuestions (5-shot) | 31.2% |
Multilingual Benchmarks (MMLU)
| Language | Score |
|---|---|
| French | 62.3% |
| German | 62.7% |
| Spanish | 64.6% |
| Italian | 61.3% |
| Portuguese | 63.3% |
| Russian | 59.2% |
| Chinese | 59.0% |
| Japanese | 59.0% |
Usage
The model can be used with three different frameworks
mistral_inference: See heretransformers: See hereNeMo: See nvidia/Mistral-NeMo-12B-Instruct
Mistral Inference
Install
It is recommended to use mistralai/Mistral-Nemo-Instruct-2407 with mistral-inference. For HF transformers code snippets, please keep scrolling.
pip install mistral_inference
Download
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', 'Nemo-Instruct')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Mistral-Nemo-Instruct-2407", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
Chat
After installing mistral_inference, a mistral-chat CLI command should be available in your environment. You can chat with the model using
mistral-chat $HOME/mistral_models/Nemo-Instruct --instruct --max_tokens 256 --temperature 0.35
E.g. Try out something like:
How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar.
Instruct following
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path)
prompt = "How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar."
completion_request = ChatCompletionRequest(messages=[UserMessage(content=prompt)])
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])
print(result)
Function calling
from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(
tools=[
Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
)
)
],
messages=[
UserMessage(content="What's the weather like today in Paris?"),
],
)
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])
print(result)
Transformers
NOTE: Until a new release has been made, you need to install transformers from source:
pip install git+https://github.com/huggingface/transformers.git
If you want to use Hugging Face transformers to generate text, you can do something like this.
from transformers import pipeline
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-Nemo-Instruct-2407",max_new_tokens=128)
chatbot(messages)
Function calling with transformers
To use this example, you'll need transformers version 4.42.0 or higher. Please see the
function calling guide
in the transformers docs for more information.
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "mistralai/Mistral-Nemo-Instruct-2407"
tokenizer = AutoTokenizer.from_pretrained(model_id)
def get_current_weather(location: str, format: str):
"""
Get the current weather
Args:
location: The city and state, e.g. San Francisco, CA
format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"])
"""
pass
conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]
tools = [get_current_weather]
# format and tokenize the tool use prompt
inputs = tokenizer.apply_chat_template(
conversation,
tools=tools,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
inputs.to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1000)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Note that, for reasons of space, this example does not show a complete cycle of calling a tool and adding the tool call and tool results to the chat history so that the model can use them in its next generation. For a full tool calling example, please see the function calling guide, and note that Mistral does use tool call IDs, so these must be included in your tool calls and tool results. They should be exactly 9 alphanumeric characters.
Unlike previous Mistral models, Mistral Nemo requires smaller temperatures. We recommend to use a temperature of 0.3.
Limitations
The Mistral Nemo Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall