MiniMax M2.7 JANG_3L (3-bit, 89 GB)

README.md

@@ -0,0 +1,118 @@
+---
+license: other
+license_name: minimax-open
+library_name: mlx
+tags:
+- mlx
+- jang
+- minimax
+- moe
+- apple-silicon
+pipeline_tag: text-generation
+---
+
+<p align="center">
+  <img src="mlx-studio-logo.png" alt="MLX Studio" width="400"/>
+</p>
+
+<p align="center">
+  <img src="jangq-logo.png" alt="JANGQ" width="200"/>
+</p>
+
+<div align="center">
+
+# MiniMax-M2.7 JANG_3L
+
+**MiniMax M2.7 456B MoE — 3-bit mixed precision, 89 GB**
+
+Best balance of quality and size for Apple Silicon. Fits on 128 GB+ Macs.
+</div>
+
+> **Recommended: Run in [MLX Studio](https://mlxstudio.com)** for best experience including thinking mode support and optimized MoE inference.
+
+## Important Settings
+
+MiniMax M2.7 requires specific inference settings:
+
+| Setting | Value | Notes |
+|---------|-------|-------|
+| Temperature | **1.0** | REQUIRED — greedy/temp=0 causes infinite thinking loops |
+| Top P | 0.95 | |
+| Top K | 40 | |
+| Repetition Penalty | 1.1 | Optional, helps prevent loops |
+
+## Model Details
+
+| Metric | Value |
+|--------|-------|
+| Source | `MiniMaxAI/MiniMax-M2.7` (FP8 E4M3) |
+| Architecture | MoE (256 experts, top-8 active), GQA, partial RoPE |
+| Profile | JANG_3L (CRITICAL=8-bit, IMPORTANT=4-bit, COMPRESS=3-bit) |
+| Actual avg bits | 3.08 |
+| Model size | 89 GB |
+| Parameters | 456B total, ~46B active per token |
+| Format | JANG v2 (MLX-native safetensors, instant load) |
+| group_size | 128 (speed-optimized for 256 experts) |
+| Routing | Sigmoid + bias correction |
+| Context | 192K tokens |
+
+## JANG_3L Bit Allocation
+
+| Tier | Components | Bits |
+|------|-----------|------|
+| CRITICAL | Attention (Q/K/V/O), lm_head | 8 |
+| IMPORTANT | Embeddings | 4 |
+| COMPRESS | Expert MLP (w1/w2/w3) — 98.2% of params | 3 |
+| Passthrough | MoE router/gate (float16), norms | 16 |
+
+## MMLU Benchmarks
+
+*Coming soon — benchmarks will be added after all profiles are converted and tested.*
+
+## Why JANG
+
+Standard MLX quantization on MiniMax M2.5 produced **completely broken output at ALL bit levels** (~25% MMLU = random guessing). JANG's mixed-precision approach is the **only working quantized MiniMax on Apple Silicon**, achieving 74% MMLU on M2.5. M2.7 results pending.
+
+## Other Quantizations
+
+| Model | Profile | Size | Avg Bits | Status |
+|-------|---------|------|----------|--------|
+| JANG_2L | (8, 6, 2) | 63 GB | 2.10 | Ready |
+| **JANG_3L** (this) | **(8, 4, 3)** | **89 GB** | **3.08** | **Ready** |
+| JANG_4M | (8, 4, 4) | ~120 GB | ~4.1 | Converting |
+
+## Requirements
+
+- Apple Silicon Mac with 128+ GB unified memory
+- MLX framework
+- [MLX Studio](https://mlxstudio.com) recommended
+
+## Usage
+
+```python
+from jang_tools.loader import load_jang_model
+from mlx_lm import generate
+from mlx_lm.sample_utils import make_sampler
+
+model, tokenizer = load_jang_model("JANGQ-AI/MiniMax-M2.7-JANG_3L")
+sampler = make_sampler(temp=1.0, top_p=0.95)
+
+prompt = tokenizer.apply_chat_template(
+    [{"role": "user", "content": "What is photosynthesis?"}],
+    tokenize=False, add_generation_prompt=True
+)
+output = generate(model, tokenizer, prompt=prompt, max_tokens=500, sampler=sampler)
+print(output)
+```
+
+---
+
+## Support
+
+[MLX Studio](https://mlxstudio.com) | [JANGQ](https://jangq.ai) | [X @dealignai](https://x.com/dealignai)
+
+Quantized by Jinho Jang (eric@jangq.ai) using JANG Tools v2.4.1.
+
+---
+
+*This model is provided for research and personal use. Users are responsible for ensuring their use complies with applicable laws and the MiniMax license.*

config.json

@@ -0,0 +1,104 @@
+{
+  "architectures": [
+    "MiniMaxM2ForCausalLM"
+  ],
+  "attn_type_list": [
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_minimax_m2.MiniMaxM2Config",
+    "AutoModelForCausalLM": "modeling_minimax_m2.MiniMaxM2ForCausalLM"
+  },
+  "dtype": "bfloat16",
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "intermediate_size": 1536,
+  "max_position_embeddings": 196608,
+  "model_type": "minimax_m2",
+  "mtp_transformer_layers": 1,
+  "num_attention_heads": 48,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 62,
+  "num_key_value_heads": 8,
+  "num_local_experts": 256,
+  "num_mtp_modules": 3,
+  "qk_norm_type": "per_layer",
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 5000000,
+  "rotary_dim": 64,
+  "scoring_func": "sigmoid",
+  "shared_intermediate_size": 0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.46.1",
+  "use_cache": true,
+  "use_mtp": true,
+  "use_qk_norm": true,
+  "use_routing_bias": true,
+  "vocab_size": 200064,
+  "quantization": {
+    "group_size": 128,
+    "bits": 3
+  }
+}

configuration_minimax_m2.py

@@ -0,0 +1,200 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/minimax_m2/modular_minimax_m2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_minimax_m2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# coding=utf-8
+# Copyright 2025 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class MiniMaxM2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MiniMaxM2Model`]. It is used to instantiate an
+    MiniMaxM2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the MiniMaxM2-7B-v0.1 or MiniMaxM2-7B-Instruct-v0.1.
+
+    [minimax_m2ai/MiniMaxM2-8x7B](https://huggingface.co/minimax_m2ai/MiniMaxM2-8x7B)
+    [minimax_m2ai/MiniMaxM2-7B-Instruct-v0.1](https://huggingface.co/minimax_m2ai/MiniMaxM2-7B-Instruct-v0.1)
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the MiniMaxM2 model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MiniMaxM2Model`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to `8`.
+        head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
+            The attention head dimension.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. MiniMaxM2's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to route per-token, can be also interpreted as the `top-k` routing
+            parameter
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Number of experts per Sparse MLP layer.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabling this will also
+            allow the model to output the auxiliary loss. See [here]() for more details
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+        router_jitter_noise (`float`, *optional*, defaults to 0.0):
+            Amount of noise to add to the router.
+
+    ```python
+    >>> from transformers import MiniMaxM2Model, MiniMaxM2Config
+
+    >>> # Initializing a MiniMaxM2 7B style configuration
+    >>> configuration = MiniMaxM2Config()
+
+    >>> # Initializing a model from the MiniMaxM2 7B style configuration
+    >>> model = MiniMaxM2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "minimax_m2"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.block_sparse_moe.gate": "colwise_rep",  # we need to replicate here to correctly route experts
+        "layers.*.block_sparse_moe.experts.*.w1": "colwise",
+        "layers.*.block_sparse_moe.experts.*.w2": "rowwise",
+        "layers.*.block_sparse_moe.experts.*.w3": "colwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        head_dim=None,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=2,
+        num_local_experts=8,
+        output_router_logits=False,
+        router_aux_loss_coef=0.001,
+        router_jitter_noise=0.0,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+        self.head_dim = head_dim
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.router_jitter_noise = router_jitter_noise
+
+        self.use_qk_norm = kwargs.pop("use_qk_norm", False)
+        self.rotary_dim = kwargs.pop("rotary_dim", self.head_dim)
+        self.partial_rotary_factor = kwargs.pop("partial_rotary_factor", 1)
+        if self.head_dim is not None:
+            self.partial_rotary_factor = self.rotary_dim / self.head_dim
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+
+__all__ = ["MiniMaxM2Config"]

generation_config.json

@@ -0,0 +1,9 @@
+{
+  "bos_token_id": 200019,
+  "do_sample": true,
+  "eos_token_id": 200020,
+  "temperature": 1.0,
+  "top_p": 0.95,
+  "top_k": 40,
+  "transformers_version": "4.46.1"
+}

jang_config.json

@@ -0,0 +1,38 @@
+{
+  "quantization": {
+    "method": "jang-importance",
+    "profile": "JANG_3L",
+    "target_bits": 3.0,
+    "actual_bits": 3.08,
+    "block_size": 128,
+    "calibration_method": "weights",
+    "quantization_method": "mse",
+    "scoring_method": "weight-magnitude",
+    "bit_widths_used": [
+      3,
+      4,
+      8
+    ],
+    "quantization_scheme": "asymmetric",
+    "quantization_backend": "mx.quantize",
+    "hadamard_rotation": false
+  },
+  "source_model": {
+    "name": "MiniMax-M2.7-FP8",
+    "dtype": "bfloat16",
+    "parameters": "227.6B"
+  },
+  "architecture": {
+    "type": "moe",
+    "attention": "gqa",
+    "has_vision": false,
+    "has_ssm": false,
+    "has_moe": true
+  },
+  "runtime": {
+    "total_weight_bytes": 0,
+    "total_weight_gb": 0.0
+  },
+  "format": "jang",
+  "format_version": "2.0"
+}

model-00001-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49515fae5e40708be7591eaec7458c7b5b7c249baf3d61f193bac23a53f77ef8
+size 817238768

model-00002-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:252e2349d0ab5c43a2653d355708f56f25a755d67a3a4a96d32db615143ae98e
+size 532907440

model-00003-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6fdcf2d5ae8eb8b5f2df65b3cb27323271beebc82737f9e28f0c8f48528baa95
+size 984712368

model-00004-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89d4bcb83169973b7b47a237a0232808088b5058c91e4f8aa01fb05e5ce87e3d
+size 532907440

model-00005-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c69ba92c4de18857ef2540f927f2d5cdac7cc65355b2d6e1898d55b962e166f4
+size 984712368

model-00006-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:56cb008f4ac651d855b8a42741e36768389cda20de2ac5da66e46cf6a93aaf51
+size 532907440

model-00007-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6ca33e6b7fecbcc426ce879048a9c38194e83f15b44a2e331994d4404682df55
+size 984712368

model-00008-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a55a0c435162a9e19ac25520dcc1f5fea6488e9e77731730cdd490319527002d
+size 532907440

model-00009-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2af9f9c8fa8101033fcb2f1cb2acc0d36cd77c8ba6623ee893104cbb1d3ef5b7
+size 984712368

model-00010-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d1317860731583f8a0c61a3118528ea95b2f7ed337c9eb2218c9eab4a3371402
+size 532907440

model-00011-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49f4994d9b3741c3092c084eacc88621afb4c970aa9a71224099e3a53e58426e
+size 984712368

model-00012-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c3e5118af51f351304d348ae57d216acb9a593534bcd6652e7c59d7cc8a4cb7
+size 532907440

model-00013-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55189b47901e7a1dc8cd2df359aaa413238cd042b4052ae5bcb9284fdc7e763a
+size 984712368

model-00014-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c65ef09c70e285c1e851c929eb28d9d49acd359f9a067cfa7c5711aa590d6993
+size 532907440

model-00015-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:88ccea5edfa2e777023e9da10115c04e021e87f39acc716cfa3f271836d3b8fe
+size 984712368

model-00016-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:956818a18f144970a8dcf9bd60da5054e90cde395030dee28bc04f965e0408aa
+size 532907440

model-00017-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:738866360548333410864b8220df98d28241ba9668d0bc9ee713dbe763ca94c4
+size 984712368

model-00018-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6f31225f229164cead281e09c21c81a1a89cbcdfa8a26d997de8cb38c4c7f43
+size 532907440

model-00019-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9f658342d8681e702039ae55c8db9d09339c4c29782bea7d44c3b5ff37a1ad7
+size 984712368

model-00020-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1767dc8bb870e117f24e45db391eede335a3ef1d95657a088627ace44224fda
+size 532907440

model-00021-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bae4b6f4c61953118eb84a577bcf3df7f982fe1032b5688b5d02bcf2f7b6ef99
+size 984712368

model-00022-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:745ff23176935e747af3f084290702d958b6ae332623a398d2ae9bacc2012630
+size 532907448

model-00023-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67b3249f2cfe2199aaca1b4a444668fd9af31e61d9166c0e7514a1a1b7554774
+size 984712376

model-00024-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eb1a64a83beda4de98c45adba37f55903a73fbd1770f9865ea307b1078ad0361
+size 532907448

model-00025-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a70da93d642db6295b7938f3380ad7eefca56a6f7130fe25302f6f1b65f711b8
+size 984712376

model-00026-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d0cf7fd5d4a85547cc01873887dd05741e70305131e5e29d8acecf0ce603cfb
+size 532907448

model-00027-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96590811020cf2a555b1bc0bc852469212b302b5df13359f69f5e3a2af9fd259
+size 984712376

model-00028-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d5f51d985eee65d51249dc6ef273fd9ccd0026b985232e1d524ed44e57ba5cab
+size 532907448

model-00029-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8504fb71b90302823a3a65c0b84df42e63a848c3c1de1cef8f5866e0533af271
+size 984712376

model-00030-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:beca0b77078936162991fe375714746cdf97469ebc31d96d06c878b10a89cad0
+size 532907448

model-00031-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e38f0fe6a97d149e0570439d494c51897c6c535a11a37d51e8eed5f7e6d728f
+size 984712376

model-00032-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:06bb2fe4ad3936f7b33d4d7c4dda5de59dafc7066240f74a10072f921fa40a8f
+size 532907448

model-00033-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6842408896427b8a5d4d5f30501c2e3a6a8a4dfdb5f05969686f7ecd0059c5a7
+size 984712376

model-00034-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52bf4e8d85f6570b0910d15cd36d7b13a61db35bd143330165cb55edec776e89
+size 532907448

model-00035-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3609e895fba2be837968c81f32cd84381d95d9462eba235b248adfa3009ea9f
+size 984712376

model-00036-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7dee727db7b64a4a3cddff858f3666810e6b190eb567c1b9beba5af08106455
+size 532907448

model-00037-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4de33ea7d595cd4b4b99f7b57f3d883ff846db0d44888e301c9bc4ec71138698
+size 984712376

model-00038-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:203ee3438a29bb08a84c01d745329695e6577724dbbe76a1c34be246eb0316e6
+size 532907448

model-00039-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b83d07a78ccb9198a4c86a888b85fce0554606ac3614562c6c6b10a60310b8c2
+size 984712376

model-00040-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4615bb6ba5f80d7ef5a9770a2289e96749dd5c072f8dbca8f54b5ebc43a58817
+size 532907448

model-00041-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97656ad18f594b979ddb2146f46848c27f73f9815a9dc23efb26d9f5ffd1b3c5
+size 984712376

model-00042-of-00126.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4cf662abbe2e395c3f6ef4a5c0e90899fc3eafce60af3f20d513f5e0badefba
+size 532907448