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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

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+---
+language:
+  - en
+  - fr
+  - de
+  - es
+  - it
+  - pt
+  - zh
+  - ja
+  - ru
+  - ko
+license: apache-2.0
+base_model:
+  - mistralai/Mistral-Small-24B-Instruct-2501
+quantized_by: DeusImperator
+---
+
+# Mistral-Small-24B-Instruct-2501 - EXL2 6.5bpw L
+
+This is a 6.5bpw EXL2 quant of [mistralai/Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501)
+
+This quant was made using exllamav2-0.2.7 with default dataset and extended quantization sample length (8k instead of default 2k). It also uses -head_bits=8 and max accuracy quant for first and last layer (8bpw), all other layers of the model use normally chosen methods (method and name (6.5bpw_L) inspired by quants like Q4_K_L and Q6_K_L made by [bartowski](https://huggingface.co/bartowski))
+
+It fits nicely in 24GB VRAM on Windows with 20k fp16 context (should fit all 32k that with q8 cache in exl2).
+
+## Prompt Templates
+
+Uses Mistral V7-tekken:
+```
+<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]
+```
+
+### Original readme below
+
+---
+
+# 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/).
+
+## 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.
+
+### 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.
+
+**_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)
+
+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 )
+#  > ^ <
+# ```
+```
+
+### Transformers
+
+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 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."},
+]
+chatbot = pipeline("text-generation", model="mistralai/Mistral-Small-24B-Instruct-2501", max_new_tokens=256)
+chatbot(messages)
+```
+
+## 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, Diogo Costa, 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

config.json

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+{
+    "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": 6.5,
+        "head_bits": 8,
+        "calibration": {
+            "rows": 115,
+            "length": 8192,
+            "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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+download date: 2025-01-30 16:28:49
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