L3-Umbral-Storm-8B-t0.0001

Llama-3-Umbral-Storm-8B (8K)

RP model, "L3-Umbral-Mind-v2.0" as a base, nearswapped with one of the smartest L3.1 models "Storm".

• Warning: Based on Mopey-Mule so it should be negative, don't use this model for any truthful information or advice.

• <b>----></b> GGUF Q8 static

Thank you mradermacher for the quants!

• GGUFs
• GGUFs imatrix

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merge

This is a merge of pre-trained language models.

Merge Details

This model is on the Llama-3 arch with Llama-3.1 merged in, so it has 8k context length. But could possibly be extended slightly with RoPE due to the L3.1 layers.

Merge Method

This model was merged using the <b>NEARSWAP t0.0001</b> merge algorithm.

Models Merged

The following models were included in the merge:

• Base Model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
• akjindal53244/Llama-3.1-Storm-8B

Configuration

slices:
  - sources:
      - model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
        layer_range: [0, 32]
      - model: akjindal53244/Llama-3.1-Storm-8B
        layer_range: [0, 32]
merge_method: nearswap
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v2.0-8B
parameters:
  t:
    - value: 0.0001
dtype: bfloat16

Prompt Template:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

{output}<|eot_id|>

Credit to Alchemonaut:

def lerp(a, b, t):
    return a * (1 - t) + b * t

def nearswap(v0, v1, t):
    lweight = np.abs(v0 - v1)
    with np.errstate(divide='ignore', invalid='ignore'):
        lweight = np.where(lweight != 0, t / lweight, 1.0)
    lweight = np.nan_to_num(lweight, nan=1.0, posinf=1.0, neginf=1.0)
    np.clip(lweight, a_min=0.0, a_max=1.0, out=lweight)
    return lerp(v0, v1, lweight)

Credit to Numbra for idea.