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base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: peft
license: other
tags:
- lora
- peft
- adapter
- adaption
---
# adaption_diverse_ai_task_samples
## Model Training
A LORA adapter for `mistralai/Mixtral-8x7B-Instruct-v0.1`. This model was trained with SFT using [Adaption](https://adaptionlabs.ai)'s AutoScientist on the diverse_ai_task_samples dataset.
### AutoScientist Config
```json
{
"job_id": "34447162-a1f5-49ba-ab92-d9ad159c85a0",
"training_experiment_id": "77bacf88-1a66-492e-9e7d-2e5d1cb4fab5",
"original_model_name": "mistralai/Mixtral-8x7B-Instruct-v0.1",
"trained_model_name": "adaption_diverse_ai_task_samples",
"training_method": "sft",
"training_type": "lora",
"data_format": "chat",
"hyperparams": {
"lora": "true",
"lora_r": 16,
"n_evals": 5,
"n_epochs": 4,
"batch_size": "max",
"lora_alpha": 32,
"lora_dropout": 0.1,
"min_lr_ratio": 0.1,
"warmup_ratio": 0.05,
"weight_decay": 0.01,
"learning_rate": 0.00004,
"max_grad_norm": 1,
"base_model_size": "46.7B",
"train_on_inputs": "false",
"training_method": "sft",
"lr_scheduler_type": "cosine",
"scheduler_num_cycles": 0.5,
"lora_trainable_modules": "all-linear"
}
}
```
## Training Data
The model was trained on 795 rows of adapted data with the following domain distribution: entertainment (12%), language (11%), academic-education (9%), news (8%), history (8%), science (8%), writing-editing-communication (7%), math (6%), sports (6%), other (4%), data-analysis-visualization (3%), social (2%), geography (2%), games (2%), how-to (2%), medical (2%), religion (2%), governance (2%), personal-growth (2%), art (1%), code (1%), cooking (1%), culture (1%), animal-nature (1%), product-advice (1%), logic (1%), music (1%), dating (1%), roleplay (1%), technology (1%).
## Model Evaluation
The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.
## How to use
```bash
pip install torch transformers peft
```
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
BASE = "mistralai/Mixtral-8x7B-Instruct-v0.1"
ADAPTER = "<this-repo-id>"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float32 if device == "cpu" else torch.bfloat16
base = AutoModelForCausalLM.from_pretrained(BASE, dtype=dtype).to(device)
model = PeftModel.from_pretrained(base, ADAPTER)
# Optional: merge the LoRA weights into the base for faster inference
model = model.merge_and_unload()
model.eval()
tokenizer = AutoTokenizer.from_pretrained(BASE)
messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(device)
with torch.inference_mode():
out = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```
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