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README.md

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+---
+license: other
+license_name: mrl
+license_link: https://mistral.ai/licenses/MRL-0.1.md
+language:
+  - en
+  - fr
+  - de
+  - es
+  - it
+  - pt
+  - zh
+  - ja
+  - ru 
+  - ko
+
+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>.
+---
+# BigStorm - ExLLamaV2 (Exl2) Quantization
+- 4.5 bpw target
+- 8 head bits
+
+Enjoy! Raise an issue if you'd like other BPW levels.
+**Base Model Card Follows:**
+---
+
+# Model Card for Mistral-Large-Instruct-2407
+
+Mistral-Large-Instruct-2407 is an advanced dense Large Language Model (LLM) of 123B parameters with state-of-the-art reasoning, knowledge and coding capabilities.
+
+For more details about this model please refer to our release [blog post](https://mistral.ai/news/mistral-large-2407/).
+
+## Key features
+- **Multi-lingual by design:** Dozens of languages supported, including English, French, German, Spanish, Italian, Chinese, Japanese, Korean, Portuguese, Dutch and Polish.
+- **Proficient in coding:** Trained on 80+ coding languages such as Python, Java, C, C++, Javacsript, and Bash. Also trained on more specific languages such as Swift and Fortran.
+- **Agentic-centric:** Best-in-class agentic capabilities with native function calling and JSON outputting.
+- **Advanced Reasoning:** State-of-the-art mathematical and reasoning capabilities.
+- **Mistral Research License:** Allows usage and modification for research and non-commercial usages.
+- **Large Context:** A large 128k context window.
+
+## Metrics
+
+### Base Pretrained Benchmarks
+
+| Benchmark | Score |
+| --- | --- |
+| MMLU | 84.0% |
+
+
+### Base Pretrained Multilingual Benchmarks (MMLU)
+| Benchmark | Score |
+| --- | --- |
+| French | 82.8% |
+| German | 81.6% |
+| Spanish | 82.7% |
+| Italian | 82.7% |
+| Dutch | 80.7% |
+| Portuguese | 81.6% |
+| Russian | 79.0% |
+| Korean | 60.1% |
+| Japanese | 78.8% |
+| Chinese | 74.8% |
+
+
+### Instruction Benchmarks
+
+| Benchmark | Score |
+| --- | --- |
+| MT Bench | 8.63 |
+| Wild Bench | 56.3 |
+| Arena Hard| 73.2 |
+
+### Code & Reasoning Benchmarks
+| Benchmark | Score |
+| --- | --- |
+| Human Eval | 92% |
+| Human Eval Plus| 87% |
+| MBPP Base| 80% |
+| MBPP Plus| 69% |
+
+### Math Benchmarks
+
+| Benchmark | Score |
+| --- | --- |
+| GSM8K | 93% |
+| Math Instruct (0-shot, no CoT) | 70% |
+| Math Instruct (0-shot, CoT)| 71.5% |
+
+## Usage
+
+The model can be used with two different frameworks
+
+- [`mistral_inference`](https://github.com/mistralai/mistral-inference): See [here](#mistral-inference)
+- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
+
+### Mistral Inference
+
+#### Install
+
+It is recommended to use `mistralai/Mistral-Large-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', 'Large')
+mistral_models_path.mkdir(parents=True, exist_ok=True)
+
+snapshot_download(repo_id="mistralai/Mistral-Large-Instruct-2407", allow_patterns=["params.json", "consolidated-*.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
+```
+
+#### Chat
+
+After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment.
+Given the size of this model, you will need a node with several GPUs (more than 300GB cumulated vRAM).
+If you have 8 GPUs on your machine, you can chat with the model using
+
+```
+torchrun --nproc-per-node 8 --no-python mistral-chat $HOME/mistral_models/Large --instruct --max_tokens 256 --temperature 0.7
+```
+
+*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}/tokenizer.model.v3")
+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=0.7, 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}/tokenizer.model.v3")
+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=0.7, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.decode(out_tokens[0])
+
+print(result)
+```
+
+### 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 pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+chatbot = pipeline("text-generation", model="mistralai/Mistral-Large-Instruct-2407")
+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-Large-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.
+
+## Limitations
+
+The Mistral Large 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, 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,
+    "hidden_act": "silu",
+    "hidden_size": 12288,
+    "initializer_range": 0.02,
+    "intermediate_size": 28672,
+    "max_position_embeddings": 131072,
+    "model_type": "mistral",
+    "num_attention_heads": 96,
+    "num_hidden_layers": 88,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.42.3",
+    "use_cache": true,
+    "vocab_size": 32768,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.1.9",
+        "bits": 4.5,
+        "head_bits": 8,
+        "calibration": {
+            "rows": 115,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

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params.json

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+{
+    "dim": 12288,
+    "n_layers": 88,
+    "head_dim": 128,
+    "hidden_dim": 28672,
+    "n_heads": 96,
+    "n_kv_heads": 8,
+    "norm_eps": 1e-05,
+    "vocab_size": 32768,
+    "rope_theta": 1000000.0
+}

test.py

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+import json
+from typing import Dict
+
+from safetensors.torch import load_file, save_file
+from huggingface_hub import split_torch_state_dict_into_shards
+import torch
+import os
+
+def save_state_dict(state_dict: Dict[str, torch.Tensor], save_directory: str):
+    state_dict_split = split_torch_state_dict_into_shards(state_dict, filename_pattern='consolidated{suffix}.safetensors')
+    for filename, tensors in state_dict_split.filename_to_tensors.items():
+        shard = {tensor: state_dict[tensor] for tensor in tensors}
+        print("Saving", save_directory, filename)
+        save_file(shard, os.path.join(save_directory, filename))
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        with open(os.path.join(save_directory, "consolidated.safetensors.index.json"), "w") as f:
+            f.write(json.dumps(index, indent=2))
+
+big_file = 'consolidated.safetensors'
+loaded = load_file(big_file)
+
+save_state_dict(loaded, save_directory=f'.')

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