---
license: apache-2.0
library_name: peft
tags:
- mistral
datasets:
- jondurbin/airoboros-2.2.1
- Open-Orca/SlimOrca
- garage-bAInd/Open-Platypus
inference: false
pipeline_tag: text-generation
base_model: mistralai/Mixtral-8x7B-v0.1
---

<div align="center">

<img src="./logo.png" width="110px">

</div>


# dfurman/Mixtral-8x7B-peft-v0.1

A pretrained generative language model with ~47 billion parameters geared towards instruction-following capabilities.

## Model Details

This model was built via parameter-efficient finetuning of the [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) base model on the first 40k rows in each of the [jondurbin/airoboros-2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1), [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca), and [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) datasets.

- **Developed by:** Daniel Furman
- **Model type:** Causal language model (clm)
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)

## Model Sources 

- **Repository:** [here](https://github.com/daniel-furman/sft-demos/blob/main/src/sft/mixtral/sft_Mixtral_8x7B_Instruct_v0_1_peft.py)

## 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

<details>

<summary>Setup</summary>

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

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

```python
peft_model_id = "dfurman/dfurman/Mixtral-8x7B-peft-v0.1"
config = PeftConfig.from_pretrained(peft_model_id)

tokenizer = AutoTokenizer.from_pretrained(
    peft_model_id,
    use_fast=True,
    trust_remote_code=True,
)

model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(
    model, 
    peft_model_id
)
```

</details>


```python
messages = [
    {"role": "user", "content": "Tell me a recipe for a mai tai."},
]

print("\n\n*** Prompt:")
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    return_tensors="pt",
)
print(tokenizer.decode(input_ids[0]))

print("\n\n*** Generate:")
with torch.autocast("cuda", dtype=torch.bfloat16):
    output = model.generate(
        input_ids=input_ids.to("cuda"),
        max_new_tokens=1024,
        return_dict_in_generate=True,
    )

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

**Outputs**

```python
*** Prompt:
<s> [INST] Tell me a recipe for a mai tai. [/INST] 

*** Generate:
1.5 oz light rum
2 oz dark rum
1 oz lime juice
0.5 oz orange curaçao
0.5 oz orgeat syrup

In a shaker filled with ice, combine the light rum, dark rum, lime juice, orange curaçao, and orgeat syrup. Shake well.

Strain the mixture into a chilled glass filled with fresh ice.

Garnish with a lime wedge and a cherry.
```


## Speeds, Sizes, Times 

| runtime / 50 tokens (sec) | GPU  | dtype | VRAM (GB) |
|:-----------------------------:|:---------------------:|:-------------:|:-----------------------:|
| 3.21                          | 1x A100 (40 GB SXM)                | torch.bfloat16    | 16                    |

## Training

It took ~24 hours to train 2 epochs on 4x A6000s.

### Prompt Format

This model was finetuned with the following format:

```python
tokenizer.chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST] ' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
```

This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method. Here's an illustrative example:

```python
messages = [
    {"role": "user", "content": "Tell me a recipe for a mai tai."},
    {"role": "assistant", "content": "1 oz light rum\n½ oz dark rum\n¼ oz orange curaçao\n2 oz pineapple juice\n¾ oz lime juice\nDash of orgeat syrup (optional)\nSplash of grenadine (for garnish, optional)\nLime wheel and cherry garnishes (optional)\n\nShake all ingredients except the splash of grenadine in a cocktail shaker over ice. Strain into an old-fashioned glass filled with fresh ice cubes. Gently pour the splash of grenadine down the side of the glass so that it sinks to the bottom. Add garnishes as desired."},
    {"role": "user", "content": "How can I make it more upscale and luxurious?"},
]

print("\n\n*** Prompt:")
input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    return_tensors="pt",
)
print(tokenizer.decode(input_ids[0]))
```

<details>

<summary>Output</summary>

```python
"""<s> [INST] Tell me a recipe for a mai tai. [/INST] 1 oz light rum\n½ oz dark rum\n (...) Add garnishes as desired.</s>  [INST] How can I make it more upscale and luxurious? [/INST]"""
```
</details>

### 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.

See [here](https://github.com/daniel-furman/sft-demos/blob/main/src/sft/mistral/sft_Mistral_7B_Instruct_v0_1_peft.ipynb) for the finetuning code, which contains an exhaustive view of the hyperparameters employed.

The following `TrainingArguments` config was used:

- output_dir = "./results"
- num_train_epochs = 2
- auto_find_batch_size = True
- gradient_accumulation_steps = 2
- 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
- evaluation_strategy = "no"
- 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

## Mistral Research Citation

```
@misc{jiang2023mistral,
      title={Mistral 7B}, 
      author={Albert Q. Jiang and Alexandre Sablayrolles and Arthur Mensch and Chris Bamford and Devendra Singh Chaplot and Diego de las Casas and Florian Bressand and Gianna Lengyel and Guillaume Lample and Lucile Saulnier and Lélio Renard Lavaud and Marie-Anne Lachaux and Pierre Stock and Teven Le Scao and Thibaut Lavril and Thomas Wang and Timothée Lacroix and William El Sayed},
      year={2023},
      eprint={2310.06825},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

### Framework versions

- PEFT 0.7.2.dev0