Update README.md

README.md

@@ -13,7 +13,7 @@ The Mixtral-8x7B Large Language Model (LLM) is a pretrained generative Sparse Mi
 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. 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.
+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
 
@@ -40,6 +40,93 @@ 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. 
 
+## 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)
+
+text = "Hello my name is"
+inputs = tokenizer(text, return_tensors="pt")
+
+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).to(0)
+
+text = "Hello my name is"
++ inputs = tokenizer(text, return_tensors="pt").to(0)
+
+outputs = model.generate(**inputs, 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)
+
+text = "Hello my name is"
++ inputs = tokenizer(text, return_tensors="pt").to(0)
+
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+</details>
+
+### Load the model with Flash Attention 2
+
+<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, use_flash_attention_2=True)
+
+text = "Hello my name is"
++ inputs = tokenizer(text, return_tensors="pt").to(0)
+
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+</details>
+
 ## Limitations
 
 The Mixtral-8x7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.