README.md

## **UPDATE: Official version is out, use it instead: [https://huggingface.co/mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)**

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# mistral-7B-v0.1-hf

Huggingface compatible version of Mistral's 7B model: https://twitter.com/MistralAI/status/1706877320844509405

## Usage

### Load in bfloat16 (16GB VRAM or higher)

```python
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer

tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf")
model = LlamaForCausalLM.from_pretrained(
    "kittn/mistral-7B-v0.1-hf",
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128)
```

### Load in bitsandbytes nf4 (6GB VRAM or higher, maybe less with double_quant)

```python
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer, BitsAndBytesConfig

tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf")
model = LlamaForCausalLM.from_pretrained(
    "kittn/mistral-7B-v0.1-hf",
    device_map={"": 0},
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float16,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_use_double_quant=False, # set to True to save more VRAM at the cost of some speed/accuracy
    ),
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128)
```

### Load in bitsandbytes int8 (8GB VRAM or higher). Quite slow; not recommended.

```python
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer, BitsAndBytesConfig

tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf")
model = LlamaForCausalLM.from_pretrained(
    "kittn/mistral-7B-v0.1-hf",
    device_map={"": 0},
    quantization_config=BitsAndBytesConfig(
        load_in_8bit=True,
    ),
)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128)
```


## Notes

* The original huggingface conversion script converts the model from bf16 to fp16 before saving it. This script doesn't
* The tokenizer is created with `legacy=False`, [more about this here](https://github.com/huggingface/transformers/pull/24565)
* Saved in safetensors format

## Conversion script [[link]](https://gist.github.com/sekstini/151d6946df1f6aa997b7cb15ee6f3be1)

Unlike [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b), this model uses GQA. This breaks some assumptions in the original conversion script, requiring a few changes.

Conversion script: [link](https://gist.github.com/sekstini/151d6946df1f6aa997b7cb15ee6f3be1)

Original conversion script: [link](https://github.com/huggingface/transformers/blob/946bac798caefada3f5f1c9fecdcfd587ed24ac7/src/transformers/models/llama/convert_llama_weights_to_hf.py)