Granite-3.3-2B-Slerp

Granite-3.3-2B-Slerp is a merge of the following models using LazyMergekit:

• powermove72/granite-3.3-2b-Hermes3dataset
• ibm-granite/granite-3.3-2b-instruct

🧩 Configuration

# ----------------------------------------------------------------------
# Granite-3.3-2B-Slerp – 40‑layer variant (v1.3‑40L)
# ----------------------------------------------------------------------
# Goal: produce a stronger merged model when the underlying architecture
#       has 40 transformer layers.
# ----------------------------------------------------------------------
slices:
  - sources:
      - model: powermove72/granite-3.3-2b-Hermes3dataset
        layer_range: [0, 40]                     # now 40 layers
      - model: ibm-granite/granite-3.3-2b-instruct
        layer_range: [0, 40]

merge_method: slerp
base_model: powermove72/granite-3.3-2b-Hermes3dataset

parameters:
  t:
    - filter: self_attn
      value: &self_attn_t
        # Cosine‑annealed schedule (40 values)
        - 1.000
        - 0.991
        - 0.967
        - 0.928
        - 0.876
        - 0.812
        - 0.739
        - 0.658
        - 0.572
        - 0.483
        - 0.393
        - 0.304
        - 0.218
        - 0.138
        - 0.067
        - 0.008
        - 0.000
        - 0.008
        - 0.067
        - 0.138
        - 0.218
        - 0.304
        - 0.393
        - 0.483
        - 0.572
        - 0.658
        - 0.739
        - 0.812
        - 0.876
        - 0.928
        - 0.967
        - 0.991
        - 1.000
        - 0.991
        - 0.967
        - 0.928
        - 0.876
        - 0.812
        - 0.739
        - 0.658
        - 0.572
        - 0.483
        - 0.393
        - 0.304
        - 0.218
        - 0.138
        - 0.067
        - 0.008
        - 0.000
    - filter: mlp
      value: &mlp_t
        # Complementary schedule (1 - self_attn)
        - 0.000
        - 0.009
        - 0.033
        - 0.072
        - 0.124
        - 0.188
        - 0.261
        - 0.342
        - 0.428
        - 0.517
        - 0.607
        - 0.696
        - 0.782
        - 0.862
        - 0.933
        - 0.992
        - 1.000
        - 0.992
        - 0.933
        - 0.862
        - 0.782
        - 0.696
        - 0.607
        - 0.517
        - 0.428
        - 0.342
        - 0.261
        - 0.188
        - 0.124
        - 0.072
        - 0.033
        - 0.009
        - 0.000
        - 0.009
        - 0.033
        - 0.072
        - 0.124
        - 0.188
        - 0.261
        - 0.342
        - 0.428
        - 0.517
        - 0.607
        - 0.696
        - 0.782
        - 0.862
        - 0.933
        - 0.992
        - 1.000
    - value: 0.5                # Global fallback (unused when per‑filter defined)

dtype: bfloat16
seed: 42
deterministic: true

metadata:
  model_name: Granite-3.3-2B-Slerp
  version: v1.3-40L
  date: 2025-08-15
  git_hash: c7e9a4f7
  notes: |
    - Updated for 40 transformer layers.
    - Cosine‑annealed per‑layer t vectors (self_attn & mlp) ensure smooth transition.
    - Deterministic SLERP (seed=42) for reproducibility.
    - Evaluation hook runs MMLU & HELM after merge.
    - Optional: uncomment `t_amplitude: 0.6` to increase the contrast between the two models.
    - Optional: add `post_merge: quantize` for inference‑only int8 deployment.

post_merge:
  - name: eval_benchmarks
    command: |
      python -m eval.run         --model Granite-3.3-2B-Slerp         --tasks mmlu,helm         --precision bfloat16         --output results/2025-08-15-40L.json

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "powermove72/Granite-3.3-2B-Slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])