Duplicate from MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-GGUF

Co-authored-by: Maziyar Panahi <MaziyarPanahi@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+base_model: mistralai/Mixtral-8x22B-Instruct-v0.1
+inference: false
+model_creator: MaziyarPanahi
+model_name: Mixtral-8x22B-Instruct-v0.1-GGUF
+pipeline_tag: text-generation
+quantized_by: MaziyarPanahi
+tags:
+- quantized
+- 2-bit
+- 3-bit
+- 4-bit
+- 5-bit
+- 6-bit
+- 8-bit
+- 16-bit
+- GGUF
+- mixtral
+- moe
+language:
+- fr
+- en
+- es
+- it
+- de
+---
+
+# Mixtral-8x22B-Instruct-v0.1-GGUF
+
+The GGUF and quantized models here are based on [mistralai/Mixtral-8x22B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1) model
+
+## How to download
+You can download only the quants you need instead of cloning the entire repository as follows:
+
+```
+huggingface-cli download MaziyarPanahi/Mixtral-8x22B-Instruct-v0.1-GGUF --local-dir . --include '*Q2_K*gguf'
+```
+
+## Load sharded model
+
+`llama_load_model_from_file` will detect the number of files and will load additional tensors from the rest of files.
+
+```sh
+llama.cpp/main -m Mixtral-8x22B-Instruct-v0.1.Q2_K-00001-of-00005.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 1024 -e
+```
+
+
+Original README
+--- 
+
+# Model Card for Mixtral-8x22B-Instruct-v0.1
+The Mixtral-8x22B-Instruct-v0.1 Large Language Model (LLM) is an instruct fine-tuned version of the [Mixtral-8x22B-v0.1](https://huggingface.co/mistralai/Mixtral-8x22B-v0.1).
+
+## Run the model 
+```python
+from transformers import AutoModelForCausalLM
+from mistral_common.protocol.instruct.messages import (
+    AssistantMessage,
+    UserMessage,
+)
+from mistral_common.protocol.instruct.tool_calls import (
+    Tool,
+    Function,
+)
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
+
+device = "cuda" # the device to load the model onto
+
+tokenizer_v3 = MistralTokenizer.v3()
+
+mistral_query = 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"),
+    ],
+    model="test",
+)
+
+encodeds = tokenizer_v3.encode_chat_completion(mistral_query).tokens
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
+model_inputs = encodeds.to(device)
+model.to(device)
+
+generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
+sp_tokenizer = tokenizer_v3.instruct_tokenizer.tokenizer
+decoded = sp_tokenizer.decode(generated_ids[0])
+print(decoded)
+```
+
+# Instruct tokenizer
+The HuggingFace tokenizer included in this release should match our own. To compare: 
+`pip install mistral-common`
+
+```py
+from mistral_common.protocol.instruct.messages import (
+    AssistantMessage,
+    UserMessage,
+)
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.tokens.instruct.normalize import ChatCompletionRequest
+
+from transformers import AutoTokenizer
+
+tokenizer_v3 = MistralTokenizer.v3()
+
+mistral_query = ChatCompletionRequest(
+    messages=[
+        UserMessage(content="How many experts ?"),
+        AssistantMessage(content="8"),
+        UserMessage(content="How big ?"),
+        AssistantMessage(content="22B"),
+        UserMessage(content="Noice 🎉 !"),
+    ],
+    model="test",
+)
+hf_messages = mistral_query.model_dump()['messages']
+
+tokenized_mistral = tokenizer_v3.encode_chat_completion(mistral_query).tokens
+
+tokenizer_hf = AutoTokenizer.from_pretrained('mistralai/Mixtral-8x22B-Instruct-v0.1')
+tokenized_hf = tokenizer_hf.apply_chat_template(hf_messages, tokenize=True)
+
+assert tokenized_hf == tokenized_mistral
+```
+
+# Function calling and special tokens
+This tokenizer includes more special tokens, related to function calling : 
+- [TOOL_CALLS]
+- [AVAILABLE_TOOLS]
+- [/AVAILABLE_TOOLS]
+- [TOOL_RESULT]
+- [/TOOL_RESULTS]
+
+If you want to use this model with function calling, please be sure to apply it similarly to what is done in our [SentencePieceTokenizerV3](https://github.com/mistralai/mistral-common/blob/main/src/mistral_common/tokens/tokenizers/sentencepiece.py#L299).
+
+# The Mistral AI Team
+Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux,
+Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,
+Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot,
+Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger,
+Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona,
+Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon,
+Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat,
+Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen,
+Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao,
+Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang,
+Valera Nemychnikova, William El Sayed, William Marshall
+
+---

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