Update README.md

README.md

@@ -1,199 +1,394 @@
 ---
-library_name: transformers
-tags: []
+license: apache-2.0
+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>.
+tags:
+- gptq
+- quantized
+- auto-gptq
+- 4bit
 ---
 
-# Model Card for Model ID
+## Mistral 7B Instruct v0.3 GPTQ 4bit
+This is a 4bit GPTQ Quantization of the Mistral 7B Instruct v0.3 model
+The model has been quantized using auto-gptq with a custom dataset.
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for Mistral-7B-Instruct-v0.3
 
+The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
 
+Mistral-7B-v0.3 has the following changes compared to [Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/edit/main/README.md)
+- Extended vocabulary to 32768
+- Supports v3 Tokenizer
+- Supports function calling
 
-## Model Details
+## Installation
 
-### Model Description
+It is recommended to use `mistralai/Mistral-7B-Instruct-v0.3` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.
 
-<!-- Provide a longer summary of what this model is. -->
+```
+pip install mistral_inference
+```
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+## Download
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+```py
+from huggingface_hub import snapshot_download
+from pathlib import Path
 
-### Model Sources [optional]
+mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.3')
+mistral_models_path.mkdir(parents=True, exist_ok=True)
 
-<!-- Provide the basic links for the model. -->
+snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
+```
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+### Chat
 
-## Uses
+After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+```
+mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256
+```
 
-### Direct Use
+### Instruct following
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+```py
+from mistral_inference.model import Transformer
+from mistral_inference.generate import generate
 
-[More Information Needed]
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
 
-### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
+model = Transformer.from_folder(mistral_models_path)
 
-[More Information Needed]
+completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
 
-### Out-of-Scope Use
+tokens = tokenizer.encode_chat_completion(completion_request).tokens
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
 
-[More Information Needed]
+print(result)
+```
 
-## Bias, Risks, and Limitations
+### Function calling
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```py
+from mistral_common.protocol.instruct.tool_calls import Function, Tool
+from mistral_inference.model import Transformer
+from mistral_inference.generate import generate
 
-[More Information Needed]
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
 
-### Recommendations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
+model = Transformer.from_folder(mistral_models_path)
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+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?"),
+        ],
+)
 
-## How to Get Started with the Model
+tokens = tokenizer.encode_chat_completion(completion_request).tokens
 
-Use the code below to get started with the model.
+out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
 
-[More Information Needed]
+print(result)
+```
 
-## Training Details
+## Generate with `transformers`
 
-### Training Data
+If you want to use Hugging Face `transformers` to generate text, you can do something like this.
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+```py
+from transformers import pipeline
 
-[More Information Needed]
+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-7B-Instruct-v0.3")
+chatbot(messages)
+```
 
-### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+## Function calling with `transformers`
 
-#### Preprocessing [optional]
+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.
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
+model_id = "mistralai/Mistral-7B-Instruct-v0.3"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-#### Training Hyperparameters
+def get_current_weather(location: str, format: str):
+    """
+    Get the current weather
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+    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
 
-#### Speeds, Sizes, Times [optional]
+conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]
+tools = [get_current_weather]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+# render the tool use prompt as a string:
+tool_use_prompt = tokenizer.apply_chat_template(
+            conversation,
+            tools=tools,
+            tokenize=False,
+            add_generation_prompt=True,
+)
 
-[More Information Needed]
+inputs = tokenizer(tool_use_prompt, return_tensors="pt")
+
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
 
-## Evaluation
+outputs = model.generate(**inputs, max_new_tokens=1000)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+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 7B 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.
 
-### Testing Data, Factors & Metrics
+## The Mistral AI Team
 
-#### Testing Data
+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
 
-<!-- This should link to a Dataset Card if possible. -->
+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-7B-Instruct-v0.3
+
+The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
 
-[More Information Needed]
+Mistral-7B-v0.3 has the following changes compared to [Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/edit/main/README.md)
+- Extended vocabulary to 32768
+- Supports v3 Tokenizer
+- Supports function calling
 
-#### Factors
+## Installation
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+It is recommended to use `mistralai/Mistral-7B-Instruct-v0.3` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.
 
-[More Information Needed]
+```
+pip install mistral_inference
+```
 
-#### Metrics
+## Download
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+```py
+from huggingface_hub import snapshot_download
+from pathlib import Path
 
-[More Information Needed]
+mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.3')
+mistral_models_path.mkdir(parents=True, exist_ok=True)
 
-### Results
+snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
+```
 
-[More Information Needed]
+### Chat
 
-#### Summary
+After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
 
+```
+mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256
+```
 
+### Instruct following
 
-## Model Examination [optional]
+```py
+from mistral_inference.model import Transformer
+from mistral_inference.generate import generate
 
-<!-- Relevant interpretability work for the model goes here -->
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
 
-[More Information Needed]
 
-## Environmental Impact
+tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
+model = Transformer.from_folder(mistral_models_path)
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+tokens = tokenizer.encode_chat_completion(completion_request).tokens
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
 
-## Technical Specifications [optional]
+print(result)
+```
 
-### Model Architecture and Objective
+### Function calling
 
-[More Information Needed]
+```py
+from mistral_common.protocol.instruct.tool_calls import Function, Tool
+from mistral_inference.model import Transformer
+from mistral_inference.generate import generate
 
-### Compute Infrastructure
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
 
-[More Information Needed]
 
-#### Hardware
+tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
+model = Transformer.from_folder(mistral_models_path)
 
-[More Information Needed]
+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?"),
+        ],
+)
 
-#### Software
+tokens = tokenizer.encode_chat_completion(completion_request).tokens
 
-[More Information Needed]
+out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
 
-## Citation [optional]
+print(result)
+```
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+## Generate with `transformers`
 
-**BibTeX:**
+If you want to use Hugging Face `transformers` to generate text, you can do something like this.
 
-[More Information Needed]
+```py
+from transformers import pipeline
 
-**APA:**
+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-7B-Instruct-v0.3")
+chatbot(messages)
+```
 
-[More Information Needed]
 
-## Glossary [optional]
+## Function calling with `transformers`
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+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.
 
-[More Information Needed]
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-## More Information [optional]
+model_id = "mistralai/Mistral-7B-Instruct-v0.3"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
 
-[More Information Needed]
+def get_current_weather(location: str, format: str):
+    """
+    Get the current weather
 
-## Model Card Authors [optional]
+    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
 
-[More Information Needed]
+conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]
+tools = [get_current_weather]
 
-## Model Card Contact
+# render the tool use prompt as a string:
+tool_use_prompt = tokenizer.apply_chat_template(
+            conversation,
+            tools=tools,
+            tokenize=False,
+            add_generation_prompt=True,
+)
+
+inputs = tokenizer(tool_use_prompt, return_tensors="pt")
+
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
+
+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 7B 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.
+
+## The Mistral AI Team
 
-[More Information Needed]
+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