---
license: apache-2.0
library_name: peft
tags:
- mistral
datasets:
- ehartford/dolphin
- garage-bAInd/Open-Platypus
inference: false
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-v0.1
---

<div align="center">

<img src="./logo.png" width="150px">

</div>

# Mistral-7B-Instruct-v0.1

The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. It is geared towards generalist instruction-following capabilities.

## Model Details

This model was built via parameter-efficient finetuning of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the first 5k rows of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin). Finetuning was executed on 1x A100 (40 GB SXM) for roughly 1 hour on Google Colab.

- **Developed by:** Daniel Furman
- **Model type:** Decoder-only
- **Language(s) (NLP):** English
- **License:** Yi model license
- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)

## Model Sources 

- **Repository:** [github.com/daniel-furman/sft-demos](https://github.com/daniel-furman/sft-demos/blob/main/src/sft/one_gpu/mistral/sft-mistral-7b-instruct-peft.ipynb)

## Evaluation Results

| Metric                | Value |
|-----------------------|-------|
| MMLU (5-shot)         | Coming |
| ARC (25-shot)         | Coming |
| HellaSwag (10-shot)   | Coming |
| TruthfulQA (0-shot)   | Coming |
| Avg.                  | Coming |

We use Eleuther.AI's [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).

## Basic Usage

```python
!pip install -q -U transformers peft torch accelerate bitsandbytes einops sentencepiece

import torch
from peft import PeftModel, PeftConfig
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)
```

```python
peft_model_id = "dfurman/Yi-6B-instruct-v0.1"
config = PeftConfig.from_pretrained(peft_model_id)

tokenizer = AutoTokenizer.from_pretrained(
    peft_model_id,
    use_fast=True,
    trust_remote_code=True,
)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(
    model, 
    peft_model_id
)
```

```python
messages = [
    {"role": "system", "content": "You are a helpful assistant. Respond as briefly as possible."},    
    {"role": "user", "content": "Tell me a recipe for a mai tai."},
]

print("\n\n*** Prompt:")
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(prompt)

print("\n\n*** Generate:")
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
with torch.autocast("cuda", dtype=torch.bfloat16):
    output = model.generate(
        input_ids=input_ids,
        max_new_tokens=1024,
        do_sample=True,
        temperature=0.7,
        return_dict_in_generate=True,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.pad_token_id,
        repetition_penalty=1.2,
        no_repeat_ngram_size=5,
    )

response = tokenizer.decode(
    output["sequences"][0][len(input_ids[0]):], 
    skip_special_tokens=True
)
print(response)
```

<details>

<summary>Output</summary>

**Prompt**: <|im_start|>system
You are a helpful assistant. Respond as briefly as possible.<|im_end|>
<|im_start|>user
Tell me a recipe for a mai tai.<|im_end|>
<|im_start|>assistant

**Generation**: Here's one simple version of the classic Mai Tai cocktail:

1 oz White Rum (Bacardi, Don Papa, etc.) ➕ ½ oz Coconut Cream Liqueur (Malibu or Coco Lopez)
2 tsp Simple Syrup ➕ Dash Orange Bitters
3-4 Ice Cubes

Shake all ingredients in a shaker filled with ice until well chilled and strain into an old fashioned glass over fresh crushed ice. Garnish with mint leaves if desired. Enjoy!

</details>


## Speeds, Sizes, Times 

| runtime / 50 tokens (sec) | GPU             | attn | torch dtype | VRAM (GB) |
|:-----------------------------:|:----------------------:|:---------------------:|:-------------:|:-----------------------:|
| 3.1                        | 1x A100 (40 GB SXM)  | torch               | fp16    | 13                    |

## Training

It took ~3 hours to train 3 epochs on 1x A100 (40 GB SXM).

Prompt format:
This model uses the same prompt format as [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). This model does **not** expect a system prompt.

```
[INST] {prompt} [/INST]
```

## Training Hyperparameters


We use the [SFTTrainer](https://huggingface.co/docs/trl/main/en/sft_trainer) from `trl` to fine-tune LLMs on instruction-following datasets.

The following `TrainingArguments` config was used:

- num_train_epochs = 1
- auto_find_batch_size = True
- gradient_accumulation_steps = 1
- optim = "paged_adamw_32bit"
- save_strategy = "epoch"
- learning_rate = 3e-4
- lr_scheduler_type = "cosine"
- warmup_ratio = 0.03
- logging_strategy = "steps"
- logging_steps = 25
- bf16 = True

The following `bitsandbytes` quantization config was used:

- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16


## Model Card Contact

dryanfurman at gmail


## Framework versions

- PEFT 0.6.0.dev0