4.0 KiB
4.0 KiB
base_model, library_name, tags
| base_model | library_name | tags | |||||||
|---|---|---|---|---|---|---|---|---|---|
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transformers |
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Group Model
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the TIES merge method using Qwen/Qwen3-1.7B as a base.
Models Merged
The following models were included in the merge:
- cs-552-2026-claude-bots/general_knowledge_model
- cs-552-2026-claude-bots/safety_model
- cs-552-2026-claude-bots/math_model
- cs-552-2026-claude-bots/multilingual_model
Configuration
The following YAML configuration was used to produce this model:
# TIES merge configuration for 4 specialized Qwen fine-tunes
# Goal:
# - Preserve strong domain expertise from each model
# - Reduce destructive interference between skills
# - Keep general reasoning stability from the base model
#
# Recommended for:
# - Qwen2 / Qwen2.5 instruction-tuned variants
# - Same architecture + same parameter count
# - Same tokenizer + same base checkpoint lineage
models:
# ---------------------------
# Math specialist
# ---------------------------
- model: cs-552-2026-claude-bots/math_model
parameters:
# High density because math capabilities are usually sparse
# and easily lost during merging
density:
- filter: self_attn
value: 0.72
- filter: mlp
value: 0.82
# Strong contribution in reasoning-heavy blocks
weight:
- filter: self_attn
value: 1.25
- filter: mlp
value: 1.15
- value: 1.10
# ---------------------------
# Knowledge / factual model
# ---------------------------
- model: cs-552-2026-claude-bots/general_knowledge_model
parameters:
# Moderate density:
# factual tuning tends to be more distributed
density: 0.58
# Slightly lower than math to avoid overwriting reasoning
weight:
- filter: self_attn
value: 1.00
- filter: mlp
value: 0.95
- value: 0.95
# ---------------------------
# Multilingual specialist
# ---------------------------
- model: cs-552-2026-claude-bots/multilingual_model
parameters:
# Language capabilities are often spread broadly,
# so use reasonably high density
density:
- filter: embed_tokens
value: 0.90
- filter: self_attn
value: 0.68
- filter: mlp
value: 0.62
# Stronger influence on embeddings and attention
weight:
- filter: embed_tokens
value: 1.30
- filter: self_attn
value: 1.10
- value: 1.00
# ---------------------------
# Safety / alignment model
# ---------------------------
- model: cs-552-2026-claude-bots/safety_model
parameters:
# Lower density prevents excessive refusals
# while still preserving alignment behavior
density: 0.34
# Important but intentionally constrained
weight:
- filter: self_attn
value: 0.82
- filter: mlp
value: 0.72
- value: 0.75
merge_method: ties
# IMPORTANT:
# Use the ORIGINAL shared pretrained base model
# from which all four fine-tunes were derived.
base_model: Qwen/Qwen3-1.7B
parameters:
# Critical for TIES stability
normalize: true
# Helps reduce memory usage and improves masking behavior
int8_mask: true
# Trim very small parameter deltas
# Good default for 4-way merges
prune_threshold: 0.015
dtype: bfloat16
# Optional:
# tokenizer_source: base
# chat_template: auto