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".
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Warning: Based on Mopey-Mule so it should be negative, don't use this model for any truthful information or advice.
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----> GGUF Q8 static
Thank you mradermacher for the quants!
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 NEARSWAP t0.0001 merge algorithm.
Models Merged
The following models were included in the merge:
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.