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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tekken.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,260 @@
+---
+language:
+  - en
+  - fr
+  - de
+  - es
+  - it
+  - pt
+  - zh
+  - ja
+  - ru
+  - ko
+license: apache-2.0
+library_name: vllm
+base_model:
+  - mistralai/Mistral-Small-24B-Base-2501
+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 Card for Mistral-Small-24B-Instruct-2501
+
+Mistral Small 3 ( 2501 ) sets a new benchmark in the "small" Large Language Models category below 70B, boasting 24B parameters and achieving state-of-the-art capabilities comparable to larger models!  
+This model is an instruction-fine-tuned version of the base model: [Mistral-Small-24B-Base-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Base-2501).
+
+Mistral Small can be deployed locally and is exceptionally "knowledge-dense", fitting in a single RTX 4090 or a 32GB RAM MacBook once quantized.  
+Perfect for:
+- Fast response conversational agents.
+- Low latency function calling.
+- Subject matter experts via fine-tuning.
+- Local inference for hobbyists and organizations handling sensitive data.
+
+For enterprises that need specialized capabilities (increased context, particular modalities, domain specific knowledge, etc.), we will be releasing commercial models beyond what Mistral AI contributes to the community.
+
+This release demonstrates our commitment to open source, serving as a strong base model. 
+
+Learn more about Mistral Small in our [blog post](https://mistral.ai/news/mistral-small-3/).
+
+Model developper: Mistral AI Team
+
+## Key Features
+- **Multilingual:** Supports dozens of languages, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch, and Polish.
+- **Agent-Centric:** Offers best-in-class agentic capabilities with native function calling and JSON outputting.
+- **Advanced Reasoning:** State-of-the-art conversational and reasoning capabilities.
+- **Apache 2.0 License:** Open license allowing usage and modification for both commercial and non-commercial purposes.
+- **Context Window:** A 32k context window.
+- **System Prompt:** Maintains strong adherence and support for system prompts.
+- **Tokenizer:** Utilizes a Tekken tokenizer with a 131k vocabulary size.
+
+## Benchmark results
+
+
+### Human evaluated benchmarks
+
+| Category | Gemma-2-27B | Qwen-2.5-32B | Llama-3.3-70B | Gpt4o-mini |
+|----------|-------------|--------------|---------------|------------|
+| Mistral is better | 0.536 | 0.496 | 0.192 | 0.200 |
+| Mistral is slightly better | 0.196 | 0.184 | 0.164 | 0.204 |
+| Ties | 0.052 | 0.060 | 0.236 | 0.160 |
+| Other is slightly better | 0.060 | 0.088 | 0.112 | 0.124 |
+| Other is better | 0.156 | 0.172 | 0.296 | 0.312 |
+
+**Note**:
+
+- We conducted side by side evaluations with an external third-party vendor, on a set of over 1k proprietary coding and generalist prompts.
+- Evaluators were tasked with selecting their preferred model response from anonymized generations produced by Mistral Small 3 vs another model.
+- We are aware that in some cases the benchmarks on human judgement starkly differ from publicly available benchmarks, but have taken extra caution in verifying a fair evaluation. We are confident that the above benchmarks are valid.
+
+### Publicly accesible benchmarks
+
+**Reasoning & Knowledge**
+
+| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
+|------------|---------------|--------------|---------------|---------------|-------------|
+| mmlu_pro_5shot_cot_instruct | 0.663 | 0.536 | 0.666 | 0.683 | 0.617 |
+| gpqa_main_cot_5shot_instruct | 0.453 | 0.344 | 0.531 | 0.404 | 0.377 |
+
+**Math & Coding**
+
+| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
+|------------|---------------|--------------|---------------|---------------|-------------|
+| humaneval_instruct_pass@1 | 0.848 | 0.732 | 0.854 | 0.909 | 0.890 |
+| math_instruct | 0.706 | 0.535 | 0.743 | 0.819 | 0.761 |
+
+**Instruction following**
+
+| Evaluation | mistral-small-24B-instruct-2501 | gemma-2b-27b | llama-3.3-70b | qwen2.5-32b | gpt-4o-mini-2024-07-18 |
+|------------|---------------|--------------|---------------|---------------|-------------|
+| mtbench_dev | 8.35 | 7.86 | 7.96 | 8.26 | 8.33 |
+| wildbench | 52.27 | 48.21 | 50.04 | 52.73 | 56.13 |
+| arena_hard | 0.873 | 0.788 | 0.840 | 0.860 | 0.897 |
+| ifeval | 0.829 | 0.8065 | 0.8835 | 0.8401 | 0.8499 |
+
+**Note**:
+
+- Performance accuracy on all benchmarks were obtained through the same internal evaluation pipeline - as such, numbers may vary slightly from previously reported performance
+([Qwen2.5-32B-Instruct](https://qwenlm.github.io/blog/qwen2.5/), [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), [Gemma-2-27B-IT](https://huggingface.co/google/gemma-2-27b-it)). 
+- Judge based evals such as Wildbench, Arena hard and MTBench were based on gpt-4o-2024-05-13.
+
+### Basic Instruct Template (V7-Tekken)
+
+```
+<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]
+```
+*`<system_prompt>`, `<user message>` and `<assistant response>` are placeholders.*
+
+***Please make sure to use [mistral-common](https://github.com/mistralai/mistral-common) as the source of truth***
+
+## Usage
+
+The model can be used with the following frameworks;
+- [`vllm`](https://github.com/vllm-project/vllm): See [here](#vLLM)
+- [`transformers`](https://github.com/huggingface/transformers): See [here](#Transformers)
+
+### vLLM
+
+We recommend using this model with the [vLLM library](https://github.com/vllm-project/vllm)
+to implement production-ready inference pipelines.
+
+**Note**: We recommond using a relatively low temperature, such as `temperature=0.15`.
+
+**_Installation_**
+
+Make sure you install [`vLLM >= 0.6.4`](https://github.com/vllm-project/vllm/releases/tag/v0.6.4):
+
+```
+pip install --upgrade vllm
+```
+
+Also make sure you have [`mistral_common >= 1.5.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.5.2) installed:
+
+```
+pip install --upgrade mistral_common
+```
+
+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
+
+We recommand that you use Mistral-Small-Instruct-2501 in a server/client setting. 
+
+1. Spin up a server:
+
+```
+vllm serve mistralai/Mistral-Small-24B-Instruct-2501 --tokenizer_mode mistral --config_format mistral --load_format mistral --enable-auto-tool-choice
+```
+
+**Note:** Running Mistral-Small-Instruct-2501 on GPU requires 60 GB of GPU RAM. 
+
+
+2. To ping the client you can use a simple Python snippet.
+
+```py
+import requests
+import json
+from datetime import datetime, timedelta
+
+url = "http://<your-server>:8000/v1/chat/completions"
+headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}
+
+model = "mistralai/Mistral-Small-24B-Instruct-2501"
+
+messages = [
+    {
+        "role": "system",
+        "content": "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
+    },
+    {
+        "role": "user",
+        "content": "Give me 5 non-formal ways to say 'See you later' in French."
+    },
+]
+
+data = {"model": model, "messages": messages}
+
+response = requests.post(url, headers=headers, data=json.dumps(data))
+print(response.json()["choices"][0]["message"]["content"])
+
+# Sure, here are five non-formal ways to say "See you later" in French:
+#
+# 1. À plus tard
+# 2. À plus
+# 3. Salut
+# 4. À toute
+# 5. Bisous
+#
+# ```
+#  /\_/\
+# ( o.o )
+#  > ^ <
+# ```
+```
+
+#### Offline
+
+```py
+from vllm import LLM
+from vllm.sampling_params import SamplingParams
+from datetime import datetime, timedelta
+
+SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."
+
+user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."
+
+messages = [
+    {
+        "role": "system",
+        "content": SYSTEM_PROMPT
+    },
+    {
+        "role": "user",
+        "content": user_prompt
+    },
+]
+
+# note that running this model on GPU requires over 60 GB of GPU RAM
+llm = LLM(model=model_name, tokenizer_mode="mistral", tensor_parallel_size=8)
+
+sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
+outputs = llm.chat(messages, sampling_params=sampling_params)
+
+print(outputs[0].outputs[0].text)
+# Sure, here are five non-formal ways to say "See you later" in French:
+#
+# 1. À plus tard
+# 2. À plus
+# 3. Salut
+# 4. À toute
+# 5. Bisous
+#
+# ```
+#  /\_/\
+# ( o.o )
+#  > ^ <
+# ```
+```
+
+
+### Ollama
+
+[Ollama](https://github.com/ollama/ollama) can run this model locally on MacOS, Windows and Linux. 
+
+```
+ollama run mistral-small
+```
+
+4-bit quantization (aliased to default): 
+```
+ollama run mistral-small:24b-instruct-2501-q4_K_M
+```
+
+8-bit quantization:
+```
+ollama run mistral-small:24b-instruct-2501-q8_0
+```
+
+FP16:
+```
+ollama run mistral-small:24b-instruct-2501-fp16
+```

config.json

@@ -0,0 +1,37 @@
+{
+    "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": 32768,
+    "model_type": "mistral",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 40,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 100000000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.49.0.dev0",
+    "use_cache": true,
+    "vocab_size": 131072,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.2.7",
+        "bits": 8.0,
+        "head_bits": 8,
+        "calibration": {
+            "rows": 115,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.49.0.dev0"
+}

huggingface-metadata.txt

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