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README.md

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+---
+license: other
+license_name: mrl
+language:
+- en
+tags:
+- chat
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+Quantized model => https://huggingface.co/anthracite-org/magnum-v4-123b
+
+**Quantization Details:**
+Quantization is done using turboderp's ExLlamaV2 v0.2.3.
+
+I use the default calibration datasets and arguments. The repo also includes a "measurement.json" file, which was used during the quantization process.
+
+For models with bits per weight (BPW) over 6.0, I default to quantizing the `lm_head` layer at 8 bits instead of the standard 6 bits.
+
+
+
+---
+
+**Who are you? What's with these weird BPWs on [insert model here]?**
+I specialize in optimized EXL2 quantization for models in the 70B to 100B+ range, specifically tailored for 48GB VRAM setups. My rig is built using 2 x 3090s with a Ryzen APU (APU used solely for desktop output—no VRAM wasted on the 3090s). I use TabbyAPI for inference, targeting context sizes between 32K and 64K.
+
+Every model I upload includes a `config.yml` file with my ideal TabbyAPI settings. If you're using my config, don’t forget to set `PYTORCH_CUDA_ALLOC_CONF=backend:cudaMallocAsync` to save some VRAM.

config.json

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+{
+    "_name_or_path": "mistralai/Mistral-Large-Instruct-2407",
+    "architectures": [
+        "MistralForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 12288,
+    "initializer_range": 0.02,
+    "intermediate_size": 28672,
+    "max_position_embeddings": 131072,
+    "model_type": "mistral",
+    "num_attention_heads": 96,
+    "num_hidden_layers": 88,
+    "num_key_value_heads": 8,
+    "rms_norm_eps": 1e-05,
+    "rope_theta": 1000000.0,
+    "sliding_window": null,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.45.0.dev0",
+    "use_cache": false,
+    "vocab_size": 32768,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.2.3",
+        "bits": 2.85,
+        "head_bits": 6,
+        "calibration": {
+            "rows": 115,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

config.yml

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+# Sample YAML file for configuration.
+# Comment and uncomment values as needed.
+# Every value has a default within the application.
+# This file serves to be a drop in for config.yml
+
+# Unless specified in the comments, DO NOT put these options in quotes!
+# You can use https://www.yamllint.com/ if you want to check your YAML formatting.
+
+# Options for networking
+network:
+  # The IP to host on (default: 127.0.0.1).
+  # Use 0.0.0.0 to expose on all network adapters.
+  host: 0.0.0.0
+
+  # The port to host on (default: 5000).
+  port: 5000
+
+  # Disable HTTP token authentication with requests.
+  # WARNING: This will make your instance vulnerable!
+  # Turn on this option if you are ONLY connecting from localhost.
+  disable_auth: false
+
+  # Send tracebacks over the API (default: False).
+  # NOTE: Only enable this for debug purposes.
+  send_tracebacks: false
+
+  # Select API servers to enable (default: ["OAI"]).
+  # Possible values: OAI, Kobold.
+  api_servers: ["oai"]
+
+# Options for logging
+logging:
+  # Enable prompt logging (default: False).
+  log_prompt: false
+
+  # Enable generation parameter logging (default: False).
+  log_generation_params: false
+
+  # Enable request logging (default: False).
+  # NOTE: Only use this for debugging!
+  log_requests: false
+
+# Options for model overrides and loading
+# Please read the comments to understand how arguments are handled
+# between initial and API loads
+model:
+  # Directory to look for models (default: models).
+  # Windows users, do NOT put this path in quotes!
+  model_dir: models
+
+  # Allow direct loading of models from a completion or chat completion request (default: False).
+  inline_model_loading: false
+
+  # Sends dummy model names when the models endpoint is queried.
+  # Enable this if the client is looking for specific OAI models.
+  use_dummy_models: false
+
+  # An initial model to load.
+  # Make sure the model is located in the model directory!
+  # REQUIRED: This must be filled out to load a model on startup.
+  model_name: magnum-v4-123b_exl2_2.85bpw
+
+  # Names of args to use as a fallback for API load requests (default: []).
+  # For example, if you always want cache_mode to be Q4 instead of on the inital model load, add "cache_mode" to this array.
+  # Example: ['max_seq_len', 'cache_mode'].
+  use_as_default: []
+
+  # Max sequence length (default: Empty).
+  # Fetched from the model's base sequence length in config.json by default.
+  max_seq_len: 32768
+
+  # Overrides base model context length (default: Empty).
+  # WARNING: Don't set this unless you know what you're doing!
+  # Again, do NOT use this for configuring context length, use max_seq_len above ^
+  override_base_seq_len:
+
+  # Load model with tensor parallelism.
+  # Falls back to autosplit if GPU split isn't provided.
+  # This ignores the gpu_split_auto value.
+  tensor_parallel: false
+
+  # Automatically allocate resources to GPUs (default: True).
+  # Not parsed for single GPU users.
+  gpu_split_auto: true
+
+  # Reserve VRAM used for autosplit loading (default: 96 MB on GPU 0).
+  # Represented as an array of MB per GPU.
+  autosplit_reserve: [0]
+
+  # An integer array of GBs of VRAM to split between GPUs (default: []).
+  # Used with tensor parallelism.
+  gpu_split: []
+
+  # Rope scale (default: 1.0).
+  # Same as compress_pos_emb.
+  # Use if the model was trained on long context with rope.
+  # Leave blank to pull the value from the model.
+  rope_scale: 1.0
+
+  # Rope alpha (default: None).
+  # Same as alpha_value. Set to "auto" to auto-calculate.
+  # Leaving this value blank will either pull from the model or auto-calculate.
+  rope_alpha:
+
+  # Enable different cache modes for VRAM savings (default: FP16).
+  # Possible values: 'FP16', 'Q8', 'Q6', 'Q4'.
+  cache_mode: Q4
+
+  # Size of the prompt cache to allocate (default: max_seq_len).
+  # Must be a multiple of 256 and can't be less than max_seq_len.
+  # For CFG, set this to 2 * max_seq_len.
+  cache_size:
+
+  # Chunk size for prompt ingestion (default: 2048).
+  # A lower value reduces VRAM usage but decreases ingestion speed.
+  # NOTE: Effects vary depending on the model.
+  # An ideal value is between 512 and 4096.
+  chunk_size: 1024
+
+  # Set the maximum number of prompts to process at one time (default: None/Automatic).
+  # Automatically calculated if left blank.
+  # NOTE: Only available for Nvidia ampere (30 series) and above GPUs.
+  max_batch_size:
+
+  # Set the prompt template for this model. (default: None)
+  # If empty, attempts to look for the model's chat template.
+  # If a model contains multiple templates in its tokenizer_config.json,
+  # set prompt_template to the name of the template you want to use.
+  # NOTE: Only works with chat completion message lists!
+  prompt_template:
+
+  # Number of experts to use per token.
+  # Fetched from the model's config.json if empty.
+  # NOTE: For MoE models only.
+  # WARNING: Don't set this unless you know what you're doing!
+  num_experts_per_token:
+
+  # Enables fasttensors to possibly increase model loading speeds (default: False).
+  fasttensors: true
+
+# Options for draft models (speculative decoding)
+# This will use more VRAM!
+draft_model:
+  # Directory to look for draft models (default: models)
+  draft_model_dir: models
+
+  # An initial draft model to load.
+  # Ensure the model is in the model directory.
+  draft_model_name:
+
+  # Rope scale for draft models (default: 1.0).
+  # Same as compress_pos_emb.
+  # Use if the draft model was trained on long context with rope.
+  draft_rope_scale: 1.0
+
+  # Rope alpha for draft models (default: None).
+  # Same as alpha_value. Set to "auto" to auto-calculate.
+  # Leaving this value blank will either pull from the model or auto-calculate.
+  draft_rope_alpha:
+
+  # Cache mode for draft models to save VRAM (default: FP16).
+  # Possible values: 'FP16', 'Q8', 'Q6', 'Q4'.
+  draft_cache_mode: FP16
+
+# Options for Loras
+lora:
+  # Directory to look for LoRAs (default: loras).
+  lora_dir: loras
+
+  # List of LoRAs to load and associated scaling factors (default scale: 1.0).
+  # For the YAML file, add each entry as a YAML list:
+  # - name: lora1
+  #   scaling: 1.0
+  loras:
+
+# Options for embedding models and loading.
+# NOTE: Embeddings requires the "extras" feature to be installed
+# Install it via "pip install .[extras]"
+embeddings:
+  # Directory to look for embedding models (default: models).
+  embedding_model_dir: models
+
+  # Device to load embedding models on (default: cpu).
+  # Possible values: cpu, auto, cuda.
+  # NOTE: It's recommended to load embedding models on the CPU.
+  # If using an AMD GPU, set this value to 'cuda'.
+  embeddings_device: cpu
+
+  # An initial embedding model to load on the infinity backend.
+  embedding_model_name:
+sampling:
+
+# Options for development and experimentation
+developer:
+  # Skip Exllamav2 version check (default: False).
+  # WARNING: It's highly recommended to update your dependencies rather than enabling this flag.
+  unsafe_launch: false
+
+  # Disable API request streaming (default: False).
+  disable_request_streaming: false
+
+  # Enable the torch CUDA malloc backend (default: False).
+  cuda_malloc_backend: true
+
+  # Run asyncio using Uvloop or Winloop which can improve performance.
+  # NOTE: It's recommended to enable this, but if something breaks turn this off.
+  uvloop: true
+
+  # Set process to use a higher priority.
+  # For realtime process priority, run as administrator or sudo.
+  # Otherwise, the priority will be set to high.
+  realtime_process_priority: true

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "transformers_version": "4.45.0.dev0"
+}

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+from huggingface_hub import HfApi
+from pathlib import Path
+
+# Define the parameters for uploading
+repo_id = "DBMe/magnum-v4-123b-2.85bpw-h6-exl2"  # Replace with your actual repo ID
+folder_path = "/home/asusws-x570-ace/programs/tabbyAPI/models/magnum-v4-123b_exl2_2.85bpw/"  # Replace with your folder path
+repo_type = "model"  # Change to "model" or "space" if applicable
+revision = "main"  # Optional: specify the branch or use "main"
+private = False  # Set to True if the repository should be private
+allow_patterns = None  # Optional: specify patterns of files to include
+ignore_patterns = None  # Optional: specify patterns of files to exclude
+num_workers = 1  # Set based on your system; lower if your internet is unstable
+print_report = True  # Enable progress reporting
+print_report_every = 60  # Report frequency in seconds
+
+# Initialize the Hugging Face API client
+api = HfApi()
+
+# Function to upload the folder in a resumable manner
+def upload_resumable():
+    try:
+        print("Starting upload process...")
+
+        # Perform the upload with the provided parameters
+        api.upload_large_folder(
+            repo_id=repo_id,
+            folder_path=Path(folder_path),
+            repo_type=repo_type,
+            revision=revision,
+            private=private,
+            allow_patterns=allow_patterns,
+            ignore_patterns=ignore_patterns,
+            num_workers=num_workers,
+            print_report=print_report,
+            print_report_every=print_report_every,
+        )
+
+        print("Upload completed successfully!")
+
+    except Exception as e:
+        print(f"Upload interrupted due to error: {e}")
+        print("You can resume the upload by running the script again.")
+
+# Call the function to start the upload
+upload_resumable()