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Magnum-Mistral-Context.json

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+{
+    "story_string": "[INST] {{#if system}} {{system}}\n{{/if}}{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{char}}'s personality: {{personality}}\n{{/if}}{{#if scenario}}Scenario: {{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}\nLet's get started. Please respond based on the information and instructions provided above.[/INST] ",
+    "example_separator": "",
+    "chat_start": "",
+    "use_stop_strings": false,
+    "allow_jailbreak": false,
+    "always_force_name2": true,
+    "trim_sentences": false,
+    "include_newline": false,
+    "single_line": false,
+    "name": "Mistral"
+}

Magnum-Mistral-Instruct.json

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+{
+    "system_prompt": "Write {{char}}'s next reply in this fictional roleplay with {{user}}.",
+    "input_sequence": "[INST] ",
+    "output_sequence": "",
+    "last_output_sequence": "",
+    "system_sequence": "",
+    "stop_sequence": "</s>",
+    "wrap": false,
+    "macro": true,
+    "names": true,
+    "names_force_groups": true,
+    "activation_regex": "",
+    "system_sequence_prefix": "",
+    "system_sequence_suffix": "",
+    "first_output_sequence": "",
+    "skip_examples": false,
+    "output_suffix": "</s>",
+    "input_suffix": "[/INST] ",
+    "system_suffix": "",
+    "user_alignment_message": "",
+    "system_same_as_user": true,
+    "last_system_sequence": "",
+    "name": "Mistral"
+}

README.md

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+---
+tags:
+- chat
+license: other
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- ru
+- zh
+- ja
+pipeline_tag: text-generation
+license_name: mrl
+license_link: https://mistral.ai/licenses/MRL-0.1.md
+base_model: mistralai/Mistral-Large-Instruct-2407
+datasets:
+- Doctor-Shotgun/C2-Stheno
+- anthracite-org/kalo-opus-instruct-22k-no-refusal
+- anthracite-org/nopm_claude_writing_fixed
+library_name: transformers
+---
+
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/hkPzhL-xYPeGGKCyAf3Qd.png)
+This is the sixth in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407).
+
+## Prompting
+Model has been Instruct tuned with the Mistral formatting. A typical input would look like this:
+
+```py
+<s>[INST] SYSTEM MESSAGE\nUSER MESSAGE[/INST] ASSISTANT MESSAGE</s>[INST] USER MESSAGE[/INST]
+```
+
+We also provide SillyTavern presets for [Context](https://huggingface.co/anthracite-org/Magnum-123b-v1/resolve/main/Magnum-Mistral-Context.json) and [Instruct](https://huggingface.co/anthracite-org/Magnum-123b-v1/raw/main/Magnum-Mistral-Instruct.json) respectively.
+
+The Mistral preset included in SillyTavern seems to be misconfigured by default, so we recommend using these as a replacement.
+
+## Credits
+- [anthracite-org/Stheno-Data-Filtered](https://huggingface.co/datasets/anthracite-org/Stheno-Data-Filtered)
+- [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
+- [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed)
+
+This model has been a team effort, and the credits goes to all members of Anthracite.
+
+## Training
+The training was done for 1.5 epochs. We used 8x [AMD Instinct™ MI300X Accelerators](https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html) for the full-parameter fine-tuning of the model.
+
+In addition to this, we noticed that Mistral Large models seemed much more sensitive to learning rate adjustments than other models:
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/xCK3ISKF6pWcMyO7MEzTA.png)
+
+We hypothesize this is primarily due to the particularly narrow and low variance weight distributions typical of Mistral derived models regardless of their scale.
+
+In the end, due to the costs that would be involved in training another full 2 epochs run ($600) on an even lower rate, we settled on our third attempt: 2e-6 with an effective batch size of 64. We chose to publish the 1.5 epoch run after manually testing and comparing it.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6491e00e057b0928b3e07b75/d9_cBy-DuWrdnoVBbAvRV.png)
+Also, we notice a correlation between the significance of the 2nd epoch loss drop and the strength of the learning rate, implying 4e-6 leads to more catastrophic forgetting.
+
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+
+## Safety
+...

config.json

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+{
+    "_name_or_path": "anthracite-org/magnum-v2-123b",
+    "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": "4.43.4",
+    "use_cache": false,
+    "vocab_size": 32768,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.2.1",
+        "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 authenticaion 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 to clients (default: False)
+  # NOTE: Only enable this for debug purposes
+  send_tracebacks: False
+
+  # Select API servers to enable (default: ["OAI"])
+  # Possible values: OAI
+  api_servers: ["OAI"]
+
+# Options for logging
+logging:
+  # Enable prompt logging (default: False)
+  prompt: False
+
+  # Enable generation parameter logging (default: False)
+  generation_params: False
+
+  # Enable request logging (default: False)
+  # NOTE: Only use this for debugging!
+  requests: False
+
+# Options for sampling
+sampling:
+  # Override preset name. Find this in the sampler-overrides folder (default: None)
+  # This overrides default fallbacks for sampler values that are passed to the API
+  # Server-side overrides are NOT needed by default
+  # WARNING: Using this can result in a generation speed penalty
+  #override_preset: 
+
+# Options for development and experimentation
+developer:
+  # Skips exllamav2 version check (default: False)
+  # It's highly recommended to update your dependencies rather than enabling this flag
+  # WARNING: Don't set this unless you know what you're doing!
+  #unsafe_launch: False
+
+  # Disable all request streaming (default: False)
+  # A kill switch for turning off SSE in the API server
+  #disable_request_streaming: False
+
+  # Enable the torch CUDA malloc backend (default: False)
+  # This can save a few MBs of VRAM, but has a risk of errors. Use at your own risk.
+  cuda_malloc_backend: True
+
+  # Enable Uvloop or Winloop (default: False)
+  # Make the program utilize a faster async event loop 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
+
+# Options for model overrides and loading
+# Please read the comments to understand how arguments are handled between initial and API loads
+model:
+  # Overrides the directory to look for models (default: models)
+  # Windows users, DO NOT put this path in quotes! This directory will be invalid otherwise.
+  model_dir: models
+
+  # Sends dummy model names when the models endpoint is queried
+  # Enable this if the program is looking for a specific OAI model
+  #use_dummy_models: False
+
+  # An initial model to load. Make sure the model is located in the model directory!
+  # A model can be loaded later via the API.
+  # REQUIRED: This must be filled out to load a model on startup!
+  model_name: magnum-v2-123b_exl2_2.85bpw
+
+  # The below parameters only apply for initial loads
+  # All API based loads do NOT inherit these settings unless specified in use_as_default
+
+  # Names of args to use as a default 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
+  # Ex. ["max_seq_len", "cache_mode"]
+  #use_as_default: []
+
+  # The below parameters apply only if model_name is set
+
+  # 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 ^
+  # Only use this if the model's base sequence length in config.json is incorrect (ex. Mistral 7B)
+  #override_base_seq_len:
+
+  # Load model with tensor parallelism
+  # If a GPU split isn't provided, the TP loader will fallback to autosplit
+  # Enabling ignores the gpu_split_auto and autosplit_reserve values
+  #tensor_parallel: True
+
+  # Automatically allocate resources to GPUs (default: True)
+  # NOTE: Not parsed for single GPU users
+  gpu_split_auto: True
+
+  # Reserve VRAM used for autosplit loading (default: 96 MB on GPU 0)
+  # This is represented as an array of MB per GPU used
+  autosplit_reserve: [0]
+
+  # An integer array of GBs of vram to split between GPUs (default: [])
+  # Used with tensor parallelism
+  # NOTE: Not parsed for single GPU users
+  #gpu_split: [20.6, 24]
+
+  # Rope scale (default: 1.0)
+  # Same thing as compress_pos_emb
+  # Only use if your model was trained on long context with rope (check config.json)
+  # Leave blank to pull the value from the model
+  #rope_scale: 1.0
+
+  # Rope alpha (default: 1.0)
+  # Same thing as alpha_value
+  # Leave blank to automatically calculate alpha
+  #rope_alpha: 1.0
+
+  # Enable different cache modes for VRAM savings (slight performance hit).
+  # Possible values FP16, Q8, Q6, Q4. (default: FP16)
+  cache_mode: Q4
+
+  # Size of the prompt cache to allocate (default: max_seq_len)
+  # This must be a multiple of 256. A larger cache uses more VRAM, but allows for more prompts to be processed at once.
+  # NOTE: Cache size should not be less than max_seq_len.
+  # For CFG, set this to 2 * max_seq_len to make room for both positive and negative prompts.
+  # cache_size:
+
+  # Chunk size for prompt ingestion. A lower value reduces VRAM usage at the cost of ingestion speed (default: 2048)
+  # NOTE: Effects vary depending on the model. An ideal value is between 512 and 4096
+  chunk_size: 1024
+
+  # Set the maximum amount of prompts to process at one time (default: None/Automatic)
+  # This will be automatically calculated if left blank.
+  # A max batch size of 1 processes prompts one at a time.
+  # NOTE: Only available for Nvidia ampere (30 series) and above GPUs
+  #max_batch_size:
+
+  # Set the prompt template for this model. If empty, attempts to look for the model's chat template. (default: None)
+  # 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 not specified (default: Empty)
+  # WARNING: Don't set this unless you know what you're doing!
+  # NOTE: For MoE models (ex. Mixtral) only!
+  #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:
+    # Overrides the directory to look for draft (default: models)
+    #draft_model_dir: models
+
+    # An initial draft model to load. Make sure this model is located in the model directory!
+    # A draft model can be loaded later via the API.
+    #draft_model_name: A model name
+  
+    # The below parameters only apply for initial loads
+    # All API based loads do NOT inherit these settings unless specified in use_as_default
+
+    # Rope scale for draft models (default: 1.0)
+    # Same thing as compress_pos_emb
+    # Only use if your draft model was trained on long context with rope (check config.json)
+    #draft_rope_scale: 1.0
+
+    # Rope alpha for draft model (default: 1.0)
+    # Same thing as alpha_value
+    # Leave blank to automatically calculate alpha value
+    #draft_rope_alpha: 1.0
+
+    # Enable different draft model cache modes for VRAM savings (slight performance hit).
+    # Possible values FP16, Q8, Q6, Q4. (default: FP16)
+    #draft_cache_mode: FP16
+  
+  # Options for loras
+  #lora:
+    # Overrides the directory to look for loras (default: loras)
+    #lora_dir: loras
+    
+    # List of loras to load and associated scaling factors (default: 1.0). Comment out unused entries or add more rows as needed.
+    #loras:
+    #- name: lora1
+    #  scaling: 1.0
+
+# Options for embedding models and loading.
+# NOTE: Embeddings requires the "extras" feature to be installed
+# Install it via "pip install .[extras]"
+embeddings:
+  # Overrides 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 you'd like to load on an AMD gpu, set this value to "cuda" as well.
+  embeddings_device: cpu
+
+  # The below parameters only apply for initial loads
+  # All API based loads do NOT inherit these settings unless specified in use_as_default
+
+  # An initial embedding model to load on the infinity backend (default: None)
+  embedding_model_name:

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "transformers_version": "4.43.4"
+}

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+from huggingface_hub import HfApi
+from pathlib import Path
+
+# Define the parameters for uploading
+repo_id = "DBMe/magnum-v2-123b-2.85bpw-h6-exl2"  # Replace with your actual repo ID
+folder_path = "/home/asusws-x570-ace/programs/tabbyAPI/models/magnum-v2-123b_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()