Update README.md

README.md

@@ -1,169 +1,126 @@
 ---
 license: apache-2.0
-language:
-- fr
-- it
-- de
-- es
-- en
-inference:
-  parameters:
-    temperature: 0.5
-widget:
-- messages:
-  - role: user
-    content: What is your favorite condiment?
 ---
-# Model Card for Mixtral-8x22B-Instruct-v0.1-4bit
-The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x7B outperforms Llama 2 70B on most benchmarks we tested.
 
-Model added by [Prince Canuma](https://twitter.com/Prince_Canuma).
-
-For full details of this model please read our [release blog post](https://mistral.ai/news/mixtral-of-experts/).
-
-## Warning
-This repo contains weights that are compatible with [vLLM](https://github.com/vllm-project/vllm) serving of the model as well as Hugging Face [transformers](https://github.com/huggingface/transformers) library. It is based on the original Mixtral [torrent release](magnet:?xt=urn:btih:5546272da9065eddeb6fcd7ffddeef5b75be79a7&dn=mixtral-8x7b-32kseqlen&tr=udp%3A%2F%http://2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=http%3A%2F%http://2Ftracker.openbittorrent.com%3A80%2Fannounce), but the file format and parameter names are different. Please note that model cannot (yet) be instantiated with HF.
-
-## Instruction format
-
-This format must be strictly respected, otherwise the model will generate sub-optimal outputs.
-
-The template used to build a prompt for the Instruct model is defined as follows:
-```
-<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
-```
-Note that `<s>` and `</s>` are special tokens for beginning of string (BOS) and end of string (EOS) while [INST] and [/INST] are regular strings.
-
-As reference, here is the pseudo-code used to tokenize instructions during fine-tuning:
-```python
-def tokenize(text):
-    return tok.encode(text, add_special_tokens=False)
-
-[BOS_ID] + 
-tokenize("[INST]") + tokenize(USER_MESSAGE_1) + tokenize("[/INST]") +
-tokenize(BOT_MESSAGE_1) + [EOS_ID] +
-…
-tokenize("[INST]") + tokenize(USER_MESSAGE_N) + tokenize("[/INST]") +
-tokenize(BOT_MESSAGE_N) + [EOS_ID]
-```
-
-In the pseudo-code above, note that the `tokenize` method should not add a BOS or EOS token automatically, but should add a prefix space. 
-
-In the Transformers library, one can use [chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating) which make sure the right format is applied.
+# 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
+Model added by [Prince Canuma](https://twitter.com/Prince_Canuma).
 
+## Run the model 
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
-
-messages = [
-    {"role": "user", "content": "What is your favourite condiment?"},
-    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
-    {"role": "user", "content": "Do you have mayonnaise recipes?"}
-]
-
-inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
-
-outputs = model.generate(inputs, max_new_tokens=20)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem:
-
-### In half-precision
-
-Note `float16` precision only works on GPU devices
-
-<details>
-<summary> Click to expand </summary>
-
-```diff
-+ import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-+ model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
-
-messages = [
-    {"role": "user", "content": "What is your favourite condiment?"},
-    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
-    {"role": "user", "content": "Do you have mayonnaise recipes?"}
-]
-
-input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
-
-outputs = model.generate(input_ids, max_new_tokens=20)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-</details>
-
-### Lower precision using (8-bit & 4-bit) using `bitsandbytes`
-
-<details>
-<summary> Click to expand </summary>
-
-```diff
-+ import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-+ model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True, device_map="auto")
-
-text = "Hello my name is"
-messages = [
-    {"role": "user", "content": "What is your favourite condiment?"},
-    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
-    {"role": "user", "content": "Do you have mayonnaise recipes?"}
-]
-
-input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
-
-outputs = model.generate(input_ids, max_new_tokens=20)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+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)
 ```
-</details>
 
-### Load the model with Flash Attention 2
+# Instruct tokenizer
+The HuggingFace tokenizer included in this release should match our own. To compare: 
+`pip install mistral-common`
 
-<details>
-<summary> Click to expand </summary>
+```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
 
-```diff
-+ import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer
 
-model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
+tokenizer_v3 = MistralTokenizer.v3()
 
-+ model = AutoModelForCausalLM.from_pretrained(model_id, use_flash_attention_2=True, device_map="auto")
+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']
 
-messages = [
-    {"role": "user", "content": "What is your favourite condiment?"},
-    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
-    {"role": "user", "content": "Do you have mayonnaise recipes?"}
-]
+tokenized_mistral = tokenizer_v3.encode_chat_completion(mistral_query).tokens
 
-input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
+tokenizer_hf = AutoTokenizer.from_pretrained('mistralai/Mixtral-8x22B-Instruct-v0.1')
+tokenized_hf = tokenizer_hf.apply_chat_template(hf_messages, tokenize=True)
 
-outputs = model.generate(input_ids, max_new_tokens=20)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+assert tokenized_hf == tokenized_mistral
 ```
-</details>
 
-## Limitations
+# 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]
 
-The Mixtral-8x7B Instruct 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.
+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, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
+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