--- 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 Privacy Policy. 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: '[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: '[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: '[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: '[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 ![Needle in a Haystack Evaluation Heatmap EN](./niah_heatmap_en.png) ![Needle in a Haystack Evaluation Heatmap DE](./niah_heatmap_de.png) # 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](https://huggingface.co/mistralai/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](https://mistral.ai/news/mistral-nemo/). ## 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`](https://github.com/mistralai/mistral-inference): See [here](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407#mistral-inference) - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers) - [`NeMo`](https://github.com/NVIDIA/NeMo): See [nvidia/Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct) ### Mistral Inference #### Install It is recommended to use `mistralai/Mistral-Nemo-Instruct-2407` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling. ``` pip install mistral_inference ``` #### Download ```py 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 ```py 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 ```py 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 > [!IMPORTANT] > NOTE: Until a new release has been made, you need to install transformers from source: > ```sh > 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. ```py 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](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in the `transformers` docs for more information. ```python 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](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling), 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. > [!TIP] > 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