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README.md

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+
+## My first ever successful merge: QueenLiz 120B
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63cf23cffbd0cc580bc65c73/I2Aee67kS3a7cjQPM_k_k.png)
+
+this is a linear merge of Quartet70B (https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001) and Lzlv 70B (https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
+
+Sharing it here so hopefully someone else with proper machine can try this out.
+
+
+NOTE: Context Window should be 32K
+
+My Q4KM GGUF here :https://huggingface.co/Noodlz/QueenLiz-120B-GGUF
+
+Thanks to the Mad Skills by @mradermacher - a whole set of iMat quantized GGUF files here:
+https://huggingface.co/mradermacher/QueenLiz-120B-i1-GGUF/tree/main
+
+
+
+---
+base_model:
+- alchemonaut/QuartetAnemoi-70B-t0.0001
+- lizpreciatior/lzlv_70b_fp16_hf
+library_name: transformers
+tags:
+- mergekit
+- merge
+
+---
+# output_folder
+
+This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
+
+## Merge Details
+### Merge Method
+
+This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
+
+### Models Merged
+
+The following models were included in the merge:
+* [alchemonaut/QuartetAnemoi-70B-t0.0001](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001)
+* [lizpreciatior/lzlv_70b_fp16_hf](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
+
+---
+license: other
+license_name: non-commercial-research-only
+license_link: LICENSE
+---

config.json

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+{
+    "_name_or_path": "alchemonaut/QuartetAnemoi-70B-t0.0001",
+    "architectures": [
+        "LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "hidden_act": "silu",
+    "hidden_size": 8192,
+    "initializer_range": 0.02,
+    "intermediate_size": 28672,
+    "max_position_embeddings": 32764,
+    "model_type": "llama",
+    "num_attention_heads": 64,
+    "num_hidden_layers": 140,
+    "num_key_value_heads": 8,
+    "pad_token_id": 0,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 1000000,
+    "tie_word_embeddings": false,
+    "torch_dtype": "float16",
+    "transformers_version": "4.39.0",
+    "use_cache": true,
+    "vocab_size": 32000,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.0.16",
+        "bits": 5.5,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

mergekit_config.yml

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+merge_method: linear # use linear so we can include multiple models, albeit at a zero weight
+parameters:
+  weight: 1.0 # weight everything as 1 unless specified otherwise - linear with one model weighted at 1 is a no-op like passthrough
+slices:
+  - sources:
+      - model: alchemonaut/QuartetAnemoi-70B-t0.0001 # embed_tokens comes along with the ride with whatever is the first layer
+        layer_range: [0, 1]
+      - model: lizpreciatior/lzlv_70b_fp16_hf # add dummy second model with 0 weight so tokenizer-based merge routine is invoked for embed_tokens
+        layer_range: [0, 1]
+        parameters:
+          weight: 0
+  - sources:
+      - model: alchemonaut/QuartetAnemoi-70B-t0.0001
+        layer_range: [1, 20]
+  - sources:
+      - model: lizpreciatior/lzlv_70b_fp16_hf
+        layer_range: [10, 30]
+  - sources:
+      - model: alchemonaut/QuartetAnemoi-70B-t0.0001
+        layer_range: [20, 40]
+  - sources:
+      - model: lizpreciatior/lzlv_70b_fp16_hf
+        layer_range: [30, 50]
+  - sources:
+      - model: alchemonaut/QuartetAnemoi-70B-t0.0001
+        layer_range: [40, 60]
+  - sources:
+      - model: lizpreciatior/lzlv_70b_fp16_hf
+        layer_range: [50, 70]
+  - sources:
+      - model: alchemonaut/QuartetAnemoi-70B-t0.0001
+        layer_range: [60, 79]
+  - sources: # same as above, but for lm_head with the last layer
+      - model: alchemonaut/QuartetAnemoi-70B-t0.0001
+        layer_range: [79, 80]
+      - model: lizpreciatior/lzlv_70b_fp16_hf
+        layer_range: [79, 80]
+        parameters:
+          weight: 0
+dtype: float16
+tokenizer_source: model:alchemonaut/QuartetAnemoi-70B-t0.0001 # keep exact tokenizer used by Quartet - or you could use `union` if you add all of the input models to the first/last slice, but they would need to be non-zero weight or you'll get NaNs in your embeddings

model.safetensors.index.json

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