Update README.md

README.md

@@ -4,14 +4,14 @@ 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>.
 ---
 
-# Model Card for Mathstral-7B-v0.1
+# Model Card for Mathstral-7b-v0.1
 
 Mathstral 7B is a model specializing in mathematical and scientific tasks, based on Mistral 7B.
 You can read more in the [official blog post](https://mistral.ai/news/mathstral/).
 
 ## Installation
 
-It is recommended to use `mistralai/mathstral-7B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference) 
+It is recommended to use `mistralai/Mathstral-7b-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference) 
 
 
 ```
@@ -25,10 +25,10 @@ pip install mistral_inference>=1.2.0
 from huggingface_hub import snapshot_download
 from pathlib import Path
 
-mistral_models_path = Path.home().joinpath('mistral_models', 'mathstral-7B-v0.1')
+mistral_models_path = Path.home().joinpath('mistral_models', 'Mathstral-7b-v0.1')
 mistral_models_path.mkdir(parents=True, exist_ok=True)
 
-snapshot_download(repo_id="mistralai/mathstral-7B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
+snapshot_download(repo_id="mistralai/Mathstral-7b-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
 ```
 
 ### Chat
@@ -36,7 +36,7 @@ snapshot_download(repo_id="mistralai/mathstral-7B-v0.1", allow_patterns=["params
 After installing `mistral_inference`, a `mistral-demo` CLI command should be available in your environment.
 
 ```
-mistral-chat $HOME/mistral_models/mathstral-7B-v0.1 --instruct --max_tokens 256
+mistral-chat $HOME/mistral_models/Mathstral-7b-v0.1 --instruct --max_tokens 256
 ```
 
 You can then start chatting with the model, *e.g.* prompt it with something like:
@@ -52,7 +52,7 @@ To use this model within the `transformers` library, install the latest release
 from transformers import pipeline
 import torch
 
-checkpoint = "mistralai/mathstral-7B-v0.1"
+checkpoint = "mistralai/Mathstral-7b-v0.1"
 pipe = pipeline("text-generation", checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
 
 prompt = [{"role": "user", "content": "What are the roots of unity?"}]
@@ -68,7 +68,7 @@ You can also manually tokenize the input and generate text from the model, rathe
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-checkpoint = "mistralai/mathstral-7B-v0.1"
+checkpoint = "mistralai/Mathstral-7b-v0.1"
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
 model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)