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.gitattributes

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README.md

@@ -25,43 +25,17 @@ language:
 - hi
 - bn
 license: apache-2.0
+library_name: vllm
 inference: false
 base_model:
-- mistralai/Devstral-Small-2505
+- mistralai/Devstrall-Small-2505
 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>.
 pipeline_tag: text2text-generation
 ---
-<div>
-  <p style="margin-bottom: 0; margin-top: 0;">
-    <strong>See <a href="https://huggingface.co/collections/unsloth/mistral-small-3-all-versions-679fe9a4722f40d61cfe627c">our collection</a> for all versions of Mistral 3.1 including GGUF, 4-bit & 16-bit formats.</strong>
-  </p>
-  <p style="margin-bottom: 0;">
-    <em>Learn to run Devstral correctly - <a href="https://docs.unsloth.ai/basics/devstral">Read our Guide</a>.</em>
-  </p>
-<p style="margin-top: 0;margin-bottom: 0;">
-    <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
-  </p>
-  <div style="display: flex; gap: 5px; align-items: center; ">
-    <a href="https://github.com/unslothai/unsloth/">
-      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
-    </a>
-    <a href="https://discord.gg/unsloth">
-      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
-    </a>
-    <a href="https://docs.unsloth.ai/basics/devstral">
-      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
-    </a>
-  </div>
-<h1 style="margin-top: 0rem;">✨ Run & Fine-tune Devstral with Unsloth!</h1>
-</div>
-
-- Fine-tune Mistral v0.3 (7B)) for free using our Google [Colab notebook here](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Mistral_v0.3_(7B)-Conversational.ipynb)!
-- Read our Blog about Devstral support: [docs.unsloth.ai/basics/devstral](https://docs.unsloth.ai/basics/devstral)
-- View the rest of our notebooks in our [docs here](https://docs.unsloth.ai/get-started/unsloth-notebooks).
-
-# Devstrall-Small-2505
+
+# Model Card for mistralai/Devstrall-Small-2505
 
 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). 
 
@@ -80,6 +54,7 @@ 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
@@ -96,7 +71,7 @@ Devstral achieves a score of 46.8% on SWE-Bench Verified, outperforming prior op
 
  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](https://huggingface.co/mistralai/Devstral-Small-2505/resolve/main/assets/swe_bench.png)
+![SWE Benchmark](assets/swe_bench.png)
 
 ## Usage
 
@@ -127,13 +102,34 @@ docker run -it --rm --pull=always \
 
 ### Local inference 
 
+You can also run the model locally. It can be done with LMStudio or other providers listed below.
+
+Launch Openhands
+You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker
+
+```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
+```
+
+The server will start at http://0.0.0.0:3000. Open it in your browser and you will see a tab AI Provider Configuration. 
+Now you can start a new conversation with the agent by clicking on the plus sign on the left bar.
+
+
 The model can also be deployed with the following libraries:
-- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
+- [`LMStudio (recommended for quantized model)`](https://lmstudio.ai/): See [here](#lmstudio)
+- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm)
+- [`ollama`](https://github.com/ollama/ollama): See [here](#ollama)
 - [`mistral-inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference)
 - [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
-- [`LMStudio`](https://lmstudio.ai/): See [here](#lmstudio)
-- [`ollama`](https://github.com/ollama/ollama): See [here](#ollama)
-
 
 ### OpenHands (recommended)
 
@@ -221,6 +217,43 @@ Enjoy building with Devstral Small and OpenHands!
 </details>
 
 
+### LMStudio (recommended for quantized model)
+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/"
+```
+
+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.ggu` 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 oggle 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.
+
+Launch Openhands
+You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker
+
+```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.
+
 ### vLLM (recommended)
 
 We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
@@ -234,7 +267,7 @@ Make sure you install [`vLLM >= 0.8.5`](https://github.com/vllm-project/vllm/rel
 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).
+Doing so should automatically install [`mistral_common >= 1.5.4`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.4).
 
 To check:
 ```
@@ -282,7 +315,7 @@ messages = [
         "content": [
             {
                 "type": "text",
-                "text": "<your-command>",
+                "text": "Write a function that computes fibonacci in Python.",
             },
         ],
     },
@@ -294,6 +327,97 @@ response = requests.post(url, headers=headers, data=json.dumps(data))
 print(response.json()["choices"][0]["message"]["content"])
 ```
 
+<details>
+    <summary>Output</summary>
+    
+Certainly! The Fibonacci sequence is a series of numbers where each number is the sum of the two preceding ones, usually starting with 0 and 1. Here's a simple Python function to compute the Fibonacci sequence:
+
+### Iterative Approach
+This approach uses a loop to compute the Fibonacci number iteratively.
+
+```python
+def fibonacci(n):
+    if n <= 0:
+        return "Input should be a positive integer."
+    elif n == 1:
+        return 0
+    elif n == 2:
+        return 1
+
+    a, b = 0, 1
+    for _ in range(2, n):
+        a, b = b, a + b
+    return b
+
+# Example usage:
+print(fibonacci(10))  # Output: 34
+```
+
+### Recursive Approach
+This approach uses recursion to compute the Fibonacci number. Note that this is less efficient for large `n` due to repeated calculations.
+
+```python
+def fibonacci_recursive(n):
+    if n <= 0:
+        return "Input should be a positive integer."
+    elif n == 1:
+        return 0
+    elif n == 2:
+        return 1
+    else:
+        return fibonacci_recursive(n - 1) + fibonacci_recursive(n - 2)
+
+# Example usage:
+print(fibonacci_recursive(10))  # Output: 34
+```
+
+\### Memoization Approach
+This approach uses memoization to store previously computed Fibonacci numbers, making it more efficient than the simple recursive approach.
+
+```python
+def fibonacci_memo(n, memo={}):
+    if n <= 0:
+        return "Input should be a positive integer."
+    elif n == 1:
+        return 0
+    elif n == 2:
+        return 1
+    elif n in memo:
+        return memo[n]
+
+    memo[n] = fibonacci_memo(n - 1, memo) + fibonacci_memo(n - 2, memo)
+    return memo[n]
+
+# Example usage:
+print(fibonacci_memo(10))  # Output: 34
+```
+
+\### Dynamic Programming Approach
+This approach uses an array to store the Fibonacci numbers up to `n`.
+
+```python
+def fibonacci_dp(n):
+    if n <= 0:
+        return "Input should be a positive integer."
+    elif n == 1:
+        return 0
+    elif n == 2:
+        return 1
+
+    fib = [0, 1] + [0] * (n - 2)
+    for i in range(2, n):
+        fib[i] = fib[i - 1] + fib[i - 2]
+    return fib[n - 1]
+
+# Example usage:
+print(fibonacci_dp(10))  # Output: 34
+```
+
+You can choose any of these approaches based on your needs. The iterative and dynamic programming approaches are generally more efficient for larger values of `n`.
+
+</details>
+
+
 ### Mistral-inference
 
 We recommend using mistral-inference to quickly try out / "vibe-check" Devstral.
@@ -326,7 +450,47 @@ You can run the model using the following command:
 mistral-chat $HOME/mistral_models/Devstral --instruct --max_tokens 300
 ```
 
-You can then prompt it with anything you'd like.
+If you prompt it with "Write me a unique and efficient function that computes fibonacci in Python", the model should generate something along the following lines:
+
+<details>
+  <summary>Output</summary>
+
+Certainly! A common and efficient way to compute Fibonacci numbers is by using memoization to store previously computed values. This avoids redundant calculations and significantly improves performance. Below is a Python function that uses memoization to compute Fibonacci numbers efficiently:
+
+```python
+def fibonacci(n, memo=None):
+    if memo is None:
+        memo = {}
+
+    if n in memo:
+        return memo[n]
+
+    if n <= 1:
+        return n
+
+    memo[n] = fibonacci(n - 1, memo) + fibonacci(n - 2, memo)
+    return memo[n]
+
+# Example usage:
+n = 10
+print(f"Fibonacci number at position {n} is {fibonacci(n)}")
+```
+
+### Explanation:
+
+1. **Base Case**: If `n` is 0 or 1, the function returns `n` because the Fibonacci sequence starts with 0 and 1.
+2. **Memoization**: The function uses a dictionary `memo` to store the results of previously computed Fibonacci numbers.
+3. **Recursive Case**: For other values of `n`, the function recursively computes the Fibonacci number by summing the results of `fibonacci(n - 1)` and `fibonacci(n)`
+
+</details>
+
+### Ollama
+
+You can run Devstral using the [Ollama](https://ollama.ai/) CLI.
+
+```bash
+ollama run devstral
+```
 
 ### Transformers
 
@@ -368,7 +532,7 @@ tokenized = tokenizer.encode_chat_completion(
     ChatCompletionRequest(
         messages=[
             SystemMessage(content=SYSTEM_PROMPT),
-            UserMessage(content="<your-command>"),
+            UserMessage(content="Write me a function that computes fibonacci in Python."),
         ],
     )
 )
@@ -381,49 +545,3 @@ output = model.generate(
 decoded_output = tokenizer.decode(output[len(tokenized.tokens):])
 print(decoded_output)
 ```
-
-### LMStudio
-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/"
-```
-
-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 oggle 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.
-
-Launch Openhands
-You can now interact with the model served from LM Studio with openhands. Start the openhands server with the docker
-
-```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.
-
-
-### Ollama
-
-You can run Devstral using the [Ollama](https://ollama.ai/) CLI.
-
-```bash
-ollama run devstral
-```

config.json

@@ -0,0 +1,28 @@
+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 32768,
+  "max_position_embeddings": 131072,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": 11,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.1",
+  "unsloth_fixed": true,
+  "use_cache": true,
+  "vocab_size": 131072
+}