---
license: apache-2.0
language:
- en
base_model:
- mistralai/Mistral-7B-Instruct-v0.3
pipeline_tag: question-answering
---

# Mistral-7B-Instruct-v0.3-EOSC

Federated fine tuned version using data from the EOSC registry.

Federated training configuration: 

- model.name = "mistralai/Mistral-7B-Instruct-v0.3"
- model.quantization = 4
- model.gradient-checkpointing = true
- model.lora.peft-lora-r = 32
- model.lora.peft-lora-alpha = 64
- train.save-every-round = 5
- train.learning-rate-max = 5e-5
- train.learning-rate-min = 1e-6
- train.seq-length = 512
- train.training-arguments.per-device-train-batch-size = 16
- train.training-arguments.gradient-accumulation-steps = 1
- train.training-arguments.logging-steps = 10
- train.training-arguments.num-train-epochs = 2
- train.training-arguments.max-steps = 10
- train.training-arguments.save-steps = 1000
- train.training-arguments.save-total-limit = 10
- train.training-arguments.gradient-checkpointing = true
- train.training-arguments.lr-scheduler-type = "constant"
- strategy.fraction-fit = 0.1
- strategy.fraction-evaluate = 0.0
- num-server-rounds = 10

The PEFT presented in this model corresponds to 5 rounds of the FL training,

The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- _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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False

### Framework versions


- PEFT 0.6.2


### Try the model!

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = "mistralai/Mistral-7B-Instruct-v0.3"
adapter_model = 'ifca-advanced-computing/Mistral-7B-Instruct-v0.3-EOSC'

model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_model)
tokenizer = AutoTokenizer.from_pretrained(base_model)

model.eval()

query = [
    {"role": "user", "content": "What is the EOSC?"},
]

input_ids = tokenizer.apply_chat_template(
    query,
    tokenize=True,
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        input_ids=input_ids,
        max_new_tokens=500,
        do_sample=True,
        temperature=0.7,
        top_p=0.9
    )

question = query[0]['content']
print(f'QUESTION: {question} \n')

print('ANSWER:\n')
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```