---
license: mit
library_name: transformers
pipeline_tag: text-generation

language:
  - en
  - es
  - fr

tags:
  - long-context
  - multilingual
  - ntk-scaling
  - hybrid-merge
  - uncensored

base_model: mistralai/Mistral-7B-Instruct-v0.3

datasets:
  - allenai/longform
  - EleutherAI/long-range-arena
  - HuggingFaceH4/openhermes-2.5
  - microsoft/orca-math-word-problems-200k
  - laion/laion-coco
  - HuggingFaceH4/multilingual-open-llm-eval

model-index:
  - name: Abigail45/Green
    results:
      - task:
          type: text-generation
        dataset:
          name: long-range-arena
          type: lra
        metrics:
          - name: ROUGE-L (50k context)
            type: rouge-l
            value: 45.67
          - name: Exact Match (50k)
            type: em
            value: 62.34

      - task:
          type: text-generation
        dataset:
          name: cais/mmlu
          type: mmlu
        metrics:
          - name: MMLU (0-shot, 50k context)
            type: mmlu
            value: 72.45
          - name: ARC-Challenge (25-shot)
            type: arc_challenge
            value: 78.92

---
# Green 7B

Green is an open-source long-context model based on Mistral.

## 🔧 Usage Example

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

model_name = "Abigail45/Green"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

prompt = "Write a short poem about green forests."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=150)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```