Update README.md

README.md

@@ -1,9 +1,32 @@
 ---
 license: apache-2.0
 base_model:
-- mistralai/Devstral-Small-2505
+- mistralai/Devstral-Small-2507
 language:
 - en
+- fr
+- de
+- es
+- pt
+- it
+- ja
+- ko
+- ru
+- zh
+- ar
+- fa
+- id
+- ms
+- ne
+- pl
+- ro
+- sr
+- sv
+- tr
+- uk
+- vi
+- hi
+- bn
 pipeline_tag: text-generation
 tags:
 - merge
@@ -21,14 +44,16 @@ tags:
 library_name: transformers
 ---
 
-<h2>Mistral-Devstral-2505-CODER-Brainstorm40x-44B</h2>
+(uploading...)
+
+<h2>Mistral-Devstral-2507-CODER-Brainstorm20x-34B</h2>
 
 This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats.
 The source code can also be used directly.
 
 This model contains Brainstorm 40x, combined with Mistral's 24B Coder (instruct model):
 
-https://huggingface.co/mistralai/Devstral-Small-2505
+https://huggingface.co/mistralai/Devstral-Small-2507
 
 Information on the 24B Mistral model below, followed by Brainstorm 40x adapter (by DavidAU) and then a complete help
 section for running LLM / AI models.
@@ -54,15 +79,20 @@ For simpler coding problems, lower quants will work well; but for complex/multi-
 
 ---
 
-# Devstral Small 1.0
+# Devstral Small 1.1
 
-Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positionates it as the #1 open source model on this [benchmark](#benchmark-results). 
+Devstral is an agentic LLM for software engineering tasks built under a collaboration between [Mistral AI](https://mistral.ai/) and [All Hands AI](https://www.all-hands.dev/) 🙌. Devstral excels at using tools to explore codebases, editing multiple files and power software engineering agents. The model achieves remarkable performance on SWE-bench which positions it as the #1 open source model on this [benchmark](#benchmark-results). 
 
 It is finetuned from [Mistral-Small-3.1](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503), therefore it has a long context window of up to 128k tokens. As a coding agent, Devstral is text-only and before fine-tuning from `Mistral-Small-3.1` the vision encoder was removed.
 
 For enterprises requiring specialized capabilities (increased context, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.
 
-Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral).
+Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral-2507).
+
+**Updates compared to [`Devstral Small 1.0`](https://huggingface.co/mistralai/Devstral-Small-2505):**
+- Improved performance, please refer to the [benchmark results](#benchmark-results).
+- `Devstral Small 1.1` is still great when paired with OpenHands. This new version also generalizes better to other prompts and coding environments. 
+- Supports [Mistral's function calling format](https://mistralai.github.io/mistral-common/usage/tools/).
 
 
 ## Key Features:
@@ -73,29 +103,31 @@ Learn more about Devstral in our [blog post](https://mistral.ai/news/devstral).
 - **Tokenizer**: Utilizes a Tekken tokenizer with a 131k vocabulary size.
 
 
-
 ## Benchmark Results
 
 ### SWE-Bench
 
-Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior open-source SoTA by 6%.
+Devstral Small 1.1 achieves a score of **53.6%** on SWE-Bench Verified, outperforming Devstral Small 1.0 by +6,8% and the second best state of the art model by +11.4%.
 
-| Model            | Scaffold           | SWE-Bench Verified (%) |
-|------------------|--------------------|------------------------|
-| Devstral         | OpenHands Scaffold | **46.8**               |
-| GPT-4.1-mini     | OpenAI Scaffold    | 23.6                   |
-| Claude 3.5 Haiku | Anthropic Scaffold | 40.6                   |
-| SWE-smith-LM 32B | SWE-agent Scaffold | 40.2                   |
+| Model              | Agentic Scaffold   | SWE-Bench Verified (%) |
+|--------------------|--------------------|------------------------|
+| Devstral Small 1.1 | OpenHands Scaffold | **53.6**               |
+| Devstral Small 1.0 | OpenHands Scaffold | *46.8*                 |
+| GPT-4.1-mini       | OpenAI Scaffold    | 23.6                   |
+| Claude 3.5 Haiku   | Anthropic Scaffold | 40.6                   |
+| SWE-smith-LM 32B   | SWE-agent Scaffold | 40.2                   |
+| Skywork SWE        | OpenHands Scaffold | 38.0                   |
+| DeepSWE            | R2E-Gym   Scaffold | 42.2                   |
 
 
  When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 and Qwen3 232B-A22B.
 
-![SWE Benchmark](assets/swe_bench.png)
+![SWE Benchmark](assets/swe_benchmark.png)
 
 ## Usage
 
 We recommend to use Devstral with the [OpenHands](https://github.com/All-Hands-AI/OpenHands/tree/main) scaffold.
-You can use it either through our API or by running locally. 
+You can use it either through our API or by running locally.
 
 ### API 
 Follow these [instructions](https://docs.mistral.ai/getting-started/quickstart/#account-setup) to create a Mistral account and get an API key.
@@ -104,19 +136,19 @@ Then run these commands to start the OpenHands docker container.
 ```bash
 export MISTRAL_API_KEY=<MY_KEY>
 
-docker pull docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik
+mkdir -p ~/.openhands && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2507","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json
 
-mkdir -p ~/.openhands-state && echo '{"language":"en","agent":"CodeActAgent","max_iterations":null,"security_analyzer":null,"confirmation_mode":false,"llm_model":"mistral/devstral-small-2505","llm_api_key":"'$MISTRAL_API_KEY'","remote_runtime_resource_factor":null,"github_token":null,"enable_default_condenser":true}' > ~/.openhands-state/settings.json
+docker pull docker.all-hands.dev/all-hands-ai/runtime:0.48-nikolaik
 
 docker run -it --rm --pull=always \
-    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.39-nikolaik \
+    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.48-nikolaik \
     -e LOG_ALL_EVENTS=true \
     -v /var/run/docker.sock:/var/run/docker.sock \
-    -v ~/.openhands-state:/.openhands-state \
+    -v ~/.openhands:/.openhands \
     -p 3000:3000 \
     --add-host host.docker.internal:host-gateway \
     --name openhands-app \
-    docker.all-hands.dev/all-hands-ai/openhands:0.39
+    docker.all-hands.dev/all-hands-ai/openhands:0.48
 ```
 
 ### Local inference 
@@ -130,106 +162,27 @@ The model can also be deployed with the following libraries:
 - [`ollama`](https://github.com/ollama/ollama): See [here](#ollama)
 
 
-### OpenHands (recommended)
-
-#### Launch a server to deploy Devstral Small 1.0
-
-Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral Small 1.0`.
-
-In the case of the tutorial we spineed up a vLLM server running the command:
-```bash
-vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
-```
-
-The server address should be in the following format: `http://<your-server-url>:8000/v1`
-
-#### Launch OpenHands
-
-You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation).
-
-The easiest way to launch OpenHands is to use the Docker image:
-```bash
-docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik
-
-docker run -it --rm --pull=always \
-    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \
-    -e LOG_ALL_EVENTS=true \
-    -v /var/run/docker.sock:/var/run/docker.sock \
-    -v ~/.openhands-state:/.openhands-state \
-    -p 3000:3000 \
-    --add-host host.docker.internal:host-gateway \
-    --name openhands-app \
-    docker.all-hands.dev/all-hands-ai/openhands:0.38
-```
-
-
-Then, you can access the OpenHands UI at `http://localhost:3000`.
-
-#### Connect to the server
-
-When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier.
-
-Fill the following fields:
-- **Custom Model**: `openai/mistralai/Devstral-Small-2505`
-- **Base URL**: `http://<your-server-url>:8000/v1`
-- **API Key**: `token` (or any other token you used to launch the server if any)
-
-#### Use OpenHands powered by Devstral
-
-Now you're good to use Devstral Small inside OpenHands by **starting a new conversation**. Let's build a To-Do list app.
+#### vLLM (recommended)
 
 <details>
-  <summary>To-Do list app</summary
-
-1. Let's ask Devstral to generate the app with the following prompt:
-
-```txt
-Build a To-Do list app with the following requirements:
-- Built using FastAPI and React.
-- Make it a one page app that:
-   - Allows to add a task.
-   - Allows to delete a task.
-   - Allows to mark a task as done.
-   - Displays the list of tasks.
-- Store the tasks in a SQLite database.
-```
-
-![Agent prompting](assets/tuto_open_hands/agent_prompting.png)
-
-
-2. Let's see the result
-
-You should see the agent construct the app and be able to explore the code it generated.
-
-If it doesn't do it automatically, ask Devstral to deploy the app or do it manually, and then go the front URL deployment to see the app.
-
-![Agent working](assets/tuto_open_hands/agent_working.png)
-![App UI](assets/tuto_open_hands/app_ui.png)
-
-
-3. Iterate
-
-Now that you have a first result you can iterate on it by asking your agent to improve it. For example, in the app generated we could click on a task to mark it checked but having a checkbox would improve UX. You could also ask it to add a feature to edit a task, or to add a feature to filter the tasks by status.
-
-Enjoy building with Devstral Small and OpenHands!
-
-</details>
-
-
-### vLLM (recommended)
+<summary>Expand</summary
 
 We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
 to implement production-ready inference pipelines.
 
 **_Installation_**
 
-Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/releases/tag/v0.8.5):
+Make sure you install [`vLLM >= 0.9.1`](https://github.com/vllm-project/vllm/releases/tag/v0.9.1):
 
 ```
 pip install vllm --upgrade
 ```
 
-Doing so should automatically install [`mistral_common >= 1.5.5`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.5).
+Also make sure to have installed [`mistral_common >= 1.7.0`](https://github.com/mistralai/mistral-common/releases/tag/v1.7.0).
+
+```
+pip install mistral-common --upgrade
+```
 
 To check:
 ```
@@ -238,14 +191,14 @@ python -c "import mistral_common; print(mistral_common.__version__)"
 
 You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
 
-#### Server
+**_Launch server_**
 
 We recommand that you use Devstral in a server/client setting. 
 
 1. Spin up a server:
 
 ```
-vllm serve mistralai/Devstral-Small-2505 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
+vllm serve mistralai/Devstral-Small-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
 ```
 
 
@@ -260,7 +213,7 @@ from huggingface_hub import hf_hub_download
 url = "http://<your-server-url>:8000/v1/chat/completions"
 headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
 
-model = "mistralai/Devstral-Small-2505"
+model = "mistralai/Devstral-Small-2507"
 
 def load_system_prompt(repo_id: str, filename: str) -> str:
     file_path = hf_hub_download(repo_id=repo_id, filename=filename)
@@ -285,15 +238,42 @@ messages = [
 
 data = {"model": model, "messages": messages, "temperature": 0.15}
 
+# Devstral Small 1.1 supports tool calling. If you want to use tools, follow this:
+# tools = [ # Define tools for vLLM
+#     {
+#         "type": "function",
+#         "function": {
+#             "name": "git_clone",
+#             "description": "Clone a git repository",
+#             "parameters": {
+#                 "type": "object",
+#                 "properties": {
+#                     "url": {
+#                         "type": "string",
+#                         "description": "The url of the git repository",
+#                     },
+#                 },
+#                 "required": ["url"],
+#             },
+#         },
+#     }
+# ] 
+# data = {"model": model, "messages": messages, "temperature": 0.15, "tools": tools} # Pass tools to payload.
+
 response = requests.post(url, headers=headers, data=json.dumps(data))
 print(response.json()["choices"][0]["message"]["content"])
 ```
+</details>
+
 
-### Mistral-inference
+#### Mistral-inference
+
+<details>
+<summary>Expand</summary
 
 We recommend using mistral-inference to quickly try out / "vibe-check" Devstral.
 
-#### Install
+**_Installation_**
 
 Make sure to have mistral_inference >= 1.6.0 installed.
 
@@ -301,7 +281,7 @@ Make sure to have mistral_inference >= 1.6.0 installed.
 pip install mistral_inference --upgrade
 ```
 
-#### Download
+**_Download_**
 
 ```python
 from huggingface_hub import snapshot_download
@@ -310,10 +290,10 @@ from pathlib import Path
 mistral_models_path = Path.home().joinpath('mistral_models', 'Devstral')
 mistral_models_path.mkdir(parents=True, exist_ok=True)
 
-snapshot_download(repo_id="mistralai/Devstral-Small-2505", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
+snapshot_download(repo_id="mistralai/Devstral-Small-2507", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
 ```
 
-#### Python
+**_Chat_**
 
 You can run the model using the following command:
 
@@ -323,9 +303,15 @@ mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300
 
 You can then prompt it with anything you'd like.
 
-### Transformers
+</details>
+
 
-To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) `    mistral-common >= 1.5.5` to use our tokenizer.
+#### Transformers
+
+<details>
+<summary>Expand</summary
+
+To make the best use of our model with transformers make sure to have [installed](https://github.com/mistralai/mistral-common) `mistral-common >= 1.7.0` to use our tokenizer.
 
 ```bash
 pip install mistral-common --upgrade
@@ -350,12 +336,11 @@ def load_system_prompt(repo_id: str, filename: str) -> str:
         system_prompt = file.read()
     return system_prompt
 
-model_id = "mistralai/Devstral-Small-2505"
-tekken_file = hf_hub_download(repo_id=model_id, filename="tekken.json")
+model_id = "mistralai/Devstral-Small-2507"
 SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
 
-tokenizer = MistralTokenizer.from_file(tekken_file)
 
+tokenizer = MistralTokenizer.from_hf_hub(model_id)
 model = AutoModelForCausalLM.from_pretrained(model_id)
 
 tokenized = tokenizer.encode_chat_completion(
@@ -376,73 +361,139 @@ decoded_output = tokenizer.decode(output[len(tokenized.tokens):])
 print(decoded_output)
 ```
 
-### LMStudio
-Download the weights from huggingface:
+</details>
+
+
+#### LM Studio
+
+<details>
+<summary>Expand</summary
+
+Download the weights from either:
+- LM Studio GGUF repository (recommended): https://huggingface.co/lmstudio-community/Devstral-Small-2507-GGUF
+- our GGUF repository: https://huggingface.co/mistralai/Devstral-Small-2507_gguf
 
 ```
 pip install -U "huggingface_hub[cli]"
 huggingface-cli download \
-"mistralai/Devstral-Small-2505_gguf" \
---include "devstralQ4_K_M.gguf" \
---local-dir "mistralai/Devstral-Small-2505_gguf/"
+"lmstudio-community/Devstral-Small-2507-GGUF" \ # or mistralai/Devstral-Small-2507_gguf
+--include "Devstral-Small-2507-Q4_K_M.gguf" \
+--local-dir "Devstral-Small-2507_gguf/"
 ```
 
 You can serve the model locally with [LMStudio](https://lmstudio.ai/).
 * Download [LM Studio](https://lmstudio.ai/) and install it
 * Install `lms cli ~/.lmstudio/bin/lms bootstrap`
-* In a bash terminal, run `lms import devstralQ4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `mistralai/Devstral-Small-2505_gguf`)
-* Open the LMStudio application, click the terminal icon to get into the developer tab. Click select a model to load and select Devstral Q4 K M. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on.
-* On the right tab, you will see an API identifier which should be devstralq4_k_m and an api address under API Usage. Keep note of this address, we will use it in the next step.
+* In a bash terminal, run `lms import Devstral-Small-2507-Q4_K_M.gguf` in the directory where you've downloaded the model checkpoint (e.g. `Devstral-Small-2507_gguf`)
+* Open the LM Studio application, click the terminal icon to get into the developer tab. Click select a model to load and select `Devstral Small 2507`. Toggle the status button to start the model, in setting toggle Serve on Local Network to be on.
+* On the right tab, you will see an API identifier which should be `devstral-small-2507` and an api address under API Usage. Keep note of this address, this is used for OpenHands or Cline.
 
-Launch Openhands
-You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker
+</details>
 
-```bash
-docker pull docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik
-docker run -it --rm --pull=always \
-	-e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.38-nikolaik \
-	-e LOG_ALL_EVENTS=true \
-	-v /var/run/docker.sock:/var/run/docker.sock \
-	-v ~/.openhands-state:/.openhands-state \
-	-p 3000:3000 \
-	--add-host host.docker.internal:host-gateway \
-	--name openhands-app \
-	docker.all-hands.dev/all-hands-ai/openhands:0.38
-```
 
-Click “see advanced setting” on the second line. 
-In the new tab, toggle advanced to on. Set the custom model to be mistral/devstralq4_k_m and Base URL the api address we get from the last step in LM Studio. Set API Key to dummy. Click save changes.
+#### llama.cpp
 
-### llama.cpp
+<details>
+<summary>Expand</summary
 
 Download the weights from huggingface:
 
 ```
 pip install -U "huggingface_hub[cli]"
 huggingface-cli download \
-"mistralai/Devstral-Small-2505_gguf" \
---include "devstralQ4_K_M.gguf" \
---local-dir "mistralai/Devstral-Small-2505_gguf/"
+"mistralai/Devstral-Small-2507_gguf" \
+--include "Devstral-Small-2507-Q4_K_M.gguf" \
+--local-dir "mistralai/Devstral-Small-2507_gguf/"
 ```
 
-Then run Devstral using the llama.cpp CLI.
+Then run Devstral using the llama.cpp server.
 
 ```bash
-./llama-cli -m Devstral-Small-2505_gguf/devstralQ4_K_M.gguf -cnv
+./llama-server -m mistralai/Devstral-Small-2507_gguf/Devstral-Small-2507-Q4_K_M.gguf -c 0 # -c configure the context size, 0 means model's default, here 128k.
 ```
 
-### Ollama
+</details>
+
+
+### OpenHands (recommended)
+
+#### Launch a server to deploy Devstral Small 1.1
 
-You can run Devstral using the [Ollama](https://ollama.ai/) CLI.
+Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral Small 1.1`.
 
+In the case of the tutorial we spineed up a vLLM server running the command:
 ```bash
-ollama run devstral
+vllm serve mistralai/Devstral-Small-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
 ```
 
+The server address should be in the following format: `http://<your-server-url>:8000/v1`
+
+#### Launch OpenHands
+
+You can follow installation of OpenHands [here](https://docs.all-hands.dev/modules/usage/installation).
+
+The easiest way to launch OpenHands is to use the Docker image:
+```bash
+docker pull docker.all-hands.dev/all-hands-ai/runtime:0.48-nikolaik
+
+docker run -it --rm --pull=always \
+    -e SANDBOX_RUNTIME_CONTAINER_IMAGE=docker.all-hands.dev/all-hands-ai/runtime:0.48-nikolaik \
+    -e LOG_ALL_EVENTS=true \
+    -v /var/run/docker.sock:/var/run/docker.sock \
+    -v ~/.openhands:/.openhands \
+    -p 3000:3000 \
+    --add-host host.docker.internal:host-gateway \
+    --name openhands-app \
+    docker.all-hands.dev/all-hands-ai/openhands:0.48
+```
+
+Then, you can access the OpenHands UI at `http://localhost:3000`.
+
+#### Connect to the server
+
+When accessing the OpenHands UI, you will be prompted to connect to a server. You can use the advanced mode to connect to the server you launched earlier.
+
+Fill the following fields:
+- **Custom Model**: `openai/mistralai/Devstral-Small-2507`
+- **Base URL**: `http://<your-server-url>:8000/v1`
+- **API Key**: `token` (or any other token you used to launch the server if any)
+
+<details>
+<summary>See settings</summary>
+                 
+![OpenHands Settings](assets/open_hands_config.png)
+
+</details>
+
+
+### Cline
+
+#### Launch a server to deploy Devstral Small 1.1
+
+Make sure you launched an OpenAI-compatible server such as vLLM or Ollama as described above. Then, you can use OpenHands to interact with `Devstral Small 1.1`.
+
+In the case of the tutorial we spineed up a vLLM server running the command:
+```bash
+vllm serve mistralai/Devstral-Small-2507 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
+```
+
+The server address should be in the following format: `http://<your-server-url>:8000/v1`
+
+#### Launch Cline
+
+You can follow installation of Cline [here](https://docs.cline.bot/getting-started/installing-cline). Then you can configure the server address in the settings.
+
+<details>
+<summary>See settings</summary>
+                  
+![Cline Settings](assets/cline_config.png)
+
+</details>
+
 
 See more here:
 
-https://huggingface.co/mistralai/Devstral-Small-2505
+https://huggingface.co/mistralai/Devstral-Small-2507
 
 ---
 
@@ -450,7 +501,7 @@ https://huggingface.co/mistralai/Devstral-Small-2505
 
 ---
 
-<B>Brainstorm 40x</B>
+<B>Brainstorm 20x</B>
 
 The BRAINSTORM process was developed by David_AU.
 
@@ -463,7 +514,7 @@ What is "Brainstorm" ?
 
 The reasoning center of an LLM is taken apart, reassembled, and expanded.
 
-In this case for this model: 40 times
+In this case for this model: 20 times
 
 Then these centers are individually calibrated. These "centers" also interact with each other. 
 This introduces subtle changes into the reasoning process.