Hastagaras/Anjir-8B-L3

ANJIRRR

This model aims to achieve the human-like responses of the Halu Blackroot, the no refusal tendencies of the Halu OAS, and the smartness of the Standard Halu.

GGUF: STATIC/IMATRIX made available by mradermacher

Model Details:

• Anjrit: This model is similar to my Halu Blackroot model, but instead of using the standard version, this model uses the OAS version.

• Anying: This model is also similar to the Halu Blackroot, but instead of using the model stock, I merged the Blackroot lora manually with a very low alpha.

Both models have downsides. The Anjrit model lacks coherency, while the Anying model lacks a human-like responses.

I decided to merge both models with the following method:

1. First, I compared the response from each layer of both models using the baukit notebook.

2. After comparing both, it seems that around the bottom layer, the Anjrit model is better, perhaps because it is unhinged.

3. From the bottom to the middle layer, the Anjrit is still better, but the Anying seems smarter.

4. At the middle layer, both seem equal, but again, the Anjrit is unhinged, so I prefer this one.

5. From the middle to the top layer, the Anying is better. It is smarter, and the response is more structured.

6. The top layer of the Anjrit model is better since the model itself is orthogonalized, so I prefer this one.

7. Then I performed slerp with the following configuration. I don't know if this is really how the slerp merge works, so let's just say this is an experimental merge. Maybe I will try the other merge methods for future experiments

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Hastagaras/anjrit
  - model: Hastagaras/anying
merge_method: slerp
base_model: Hastagaras/anjrit
dtype: bfloat16
parameters:
  t: [0.12, 0.17, 0.29, 0.44, 0.26]

SAMPLER:

You can start with this and tweak it

• TEMP: 1.0
• TOP_P: 0.95
• TOP_K: 100
• MIN_P: 0.05

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Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	69.07
AI2 Reasoning Challenge (25-Shot)	63.57
HellaSwag (10-Shot)	84.15
MMLU (5-Shot)	67.67
TruthfulQA (0-shot)	52.67
Winogrande (5-shot)	78.61
GSM8k (5-shot)	67.78