---
license: apache-2.0
language:
- en
- fr
pipeline_tag: text-generation
tags:
- lora
- peft
- multi-task
- sentiment-analysis
- translation
- tinyllama
- adapter-fusion
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T

---

- # LoRA Fusion: IMDB Sentiment + EN-FR Translation on TinyLlama-1.1B

  This repository contains a **fully merged multi-task model** created by **sequentially fusing two independently trained LoRA adapters** into a single TinyLlama-1.1B base model.

  The final model supports **multiple tasks within one unified set of weights**, without requiring PEFT or LoRA adapters at inference time.

  ------

  ## 🧠 Tasks Supported

  The model is capable of performing the following tasks via prompt-based inference:

  - 🧠 **Sentiment Analysis**
     Binary sentiment classification (positive / negative) trained on the **IMDB movie review dataset**.
  - 🌍 **English → French Translation**
     Neural machine translation trained on **OPUS-100 (EN-FR)** data.

  ------

  ## 🔧 How This Model Was Built

  ### Base Model

  - **TinyLlama-1.1B**

    ```
    TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
    ```

  ### Independent LoRA Adapters

  Two LoRA adapters were trained **independently**, each specializing in a single task:

  1. **IMDB Sentiment Analysis LoRA**

     ```
     BEncoderRT/IMDB-Sentiment-LoRA-TinyLlama-1.1B
     ```

  2. **English → French Translation LoRA**

     ```
     BEncoderRT/EN-FR-Translation-LoRA-TinyLlama-1.1B
     ```

  ### Fusion Method: Sequential LoRA Merge

  The final model was created using **sequential LoRA fusion**:

  1. Load the frozen TinyLlama base model
  2. Merge the sentiment analysis LoRA into the base model
  3. Treat the merged model as a new base
  4. Merge the translation LoRA into the updated base
  5. Export the final merged weights

  This process uses `merge_and_unload()` from **PEFT**, resulting in a **standard `LlamaForCausalLM` model**.

  > ⚠️ **Important**
  >  This repository does **NOT** contain LoRA adapters.
  >  It contains a **fully merged model** and should **NOT** be loaded with `PeftModel`.

---

## 🧠 Architecture

                  ┌──────────────────────────┐
                  │  TinyLlama-1.1B Base LM│
                  │   (Frozen Parameters)  │
                  └────────────┬─────────────┘
                              │
             ┌─────────────────┴─────────────────┐
             │                                │
        ┌──────────────────────────┐ ┌──────────────────────────┐
        │ Sentiment LoRA Adapter │ │Translation LoRA Adapter│
        │ (IMDB)                 │ │          (EN → FR)     │
        └──────────────────────────┘ └──────────────────────────┘
    set_adapter("sentiment") set_adapter("translation")



---

# Usage Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "BEncoderRT/LoRA_Fusion_IMDB-Sentiment_EN-FR"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    device_map="auto",
    torch_dtype="auto"
)

model.eval()

```

```
LlamaForCausalLM(
  (model): LlamaModel(
    (embed_tokens): Embedding(32000, 2048)
    (layers): ModuleList(
      (0-21): 22 x LlamaDecoderLayer(
        (self_attn): LlamaAttention(
          (q_proj): Linear(in_features=2048, out_features=2048, bias=False)
          (k_proj): Linear(in_features=2048, out_features=256, bias=False)
          (v_proj): Linear(in_features=2048, out_features=256, bias=False)
          (o_proj): Linear(in_features=2048, out_features=2048, bias=False)
        )
        (mlp): LlamaMLP(
          (gate_proj): Linear(in_features=2048, out_features=5632, bias=False)
          (up_proj): Linear(in_features=2048, out_features=5632, bias=False)
          (down_proj): Linear(in_features=5632, out_features=2048, bias=False)
          (act_fn): SiLUActivation()
        )
        (input_layernorm): LlamaRMSNorm((2048,), eps=1e-05)
        (post_attention_layernorm): LlamaRMSNorm((2048,), eps=1e-05)
      )
    )
    (norm): LlamaRMSNorm((2048,), eps=1e-05)
    (rotary_emb): LlamaRotaryEmbedding()
  )
  
```

```python
def generate(prompt, max_new_tokens=32):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    output = model.generate(
        **inputs,
        max_new_tokens=max_new_tokens,
        temperature=0.3,
        pad_token_id=tokenizer.eos_token_id
    )
    return tokenizer.decode(output[0], skip_special_tokens=True)
```

```python
print(generate(
    "### Task: Sentiment Analysis\n### Review:\nThis movie was amazing.\n### Answer:\n",
    8
))

```

```
The following generation flags are not valid and may be ignored: ['temperature']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
### Task: Sentiment Analysis
### Review:
This movie was amazing.
### Answer:
positive
```

```python
print(generate(
    "### Task: Translation (English to French)\n### English:\nI love deep learning.\n### French:\n",
    32
))

```

```
### Task: Translation (English to French)
### English:
I love deep learning.
### French:
Je tiens à la deep learning.
```