初始化项目，由ModelHub XC社区提供模型
Model: RL-gang/vocence-orpheus-amir Source: Original Platform
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ModelHub XC
36
.gitattributes
vendored
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vendored
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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21
README.md
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README.md
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---
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base_model: unsloth/orpheus-3b-0.1-ft
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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license: apache-2.0
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language:
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- en
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---
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# Uploaded finetuned model
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- **Developed by:** xtz999
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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93
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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21
chute_config.yml
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21
chute_config.yml
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Image:
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from_base: parachutes/base-python:3.12.9
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run_command:
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- pip install torch torchaudio transformers accelerate huggingface_hub pyyaml soundfile snac
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set_workdir: /app
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NodeSelector:
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gpu_count: 1
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min_vram_gb_per_gpu: 16
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include:
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- pro_6000
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exclude: []
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Chute:
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tagline: vocence Orpheus Amir TTS
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readme: Vocence PromptTTS — American English Amir (Orpheus 3B)
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shutdown_after_seconds: 86400
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concurrency: 1
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max_instances: 1
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scaling_threshold: 0.5
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tee: true
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38
config.json
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38
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"torch_dtype": "float16",
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"eos_token_id": 128009,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 24,
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"num_hidden_layers": 28,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"transformers_version": "4.56.2",
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"unsloth_fixed": true,
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"unsloth_version": "2025.11.4",
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"use_cache": true,
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"vocab_size": 156940
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}
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232
miner.py
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232
miner.py
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from __future__ import annotations
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import threading
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from functools import cached_property
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from pathlib import Path
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from types import SimpleNamespace
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from typing import Any, Dict, List, Tuple
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import numpy as np
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SOH_ID = 128259
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EOH_ID = 128260
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CODE_START_TOKEN_ID = 128257
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CODE_END_TOKEN_ID = 128258
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TEXT_EOT_ID = 128009
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CODE_TOKEN_OFFSET = 128266
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SNAC_MIN_ID = 128266
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SNAC_MAX_ID = 156937
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SNAC_TOKENS_PER_FRAME = 7
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SNAC_MODEL_NAME = "hubertsiuzdak/snac_24khz"
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VOCENCE_EMOTION_HINTS = {
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"happy": "cheerfully",
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"sad": "sadly",
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"angry": "firmly",
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"excited": "excitedly",
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"calm": "calmly",
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"neutral": "",
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}
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def _parse_vocence_instruction(instruction: str) -> Dict[str, str]:
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parts: Dict[str, str] = {}
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for segment in instruction.split("|"):
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segment = segment.strip()
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if ":" in segment:
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key, val = segment.split(":", 1)
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parts[key.strip().lower()] = val.strip().lower()
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return parts
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def _build_orpheus_prompt(voice: str, instruction: str, text: str) -> str:
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traits = _parse_vocence_instruction(instruction)
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hints: List[str] = []
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emotion = traits.get("emotion", "")
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if emotion in VOCENCE_EMOTION_HINTS and VOCENCE_EMOTION_HINTS[emotion]:
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hints.append(VOCENCE_EMOTION_HINTS[emotion])
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speed = traits.get("speed", "")
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if speed in ("slow", "very_slow"):
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hints.append("slowly")
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elif speed in ("fast", "very_fast"):
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hints.append("quickly")
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if instruction.strip() and not traits:
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hints.append(instruction.strip()[:120])
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body = text.strip() or "Hello."
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if hints:
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body = f"{' '.join(hints)} {body}".strip()
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return f"{voice}: {body}"
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def _redistribute_codes(code_list: List[int]) -> List[Any]:
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import torch
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layer_1: List[int] = []
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layer_2: List[int] = []
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layer_3: List[int] = []
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frames = len(code_list) // 7
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for i in range(frames):
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layer_1.append(code_list[7 * i])
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layer_2.append(code_list[7 * i + 1] - 4096)
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layer_3.append(code_list[7 * i + 2] - (2 * 4096))
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layer_3.append(code_list[7 * i + 3] - (3 * 4096))
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layer_2.append(code_list[7 * i + 4] - (4 * 4096))
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layer_3.append(code_list[7 * i + 5] - (5 * 4096))
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layer_3.append(code_list[7 * i + 6] - (6 * 4096))
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return [
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torch.tensor(layer_1).unsqueeze(0),
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torch.tensor(layer_2).unsqueeze(0),
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torch.tensor(layer_3).unsqueeze(0),
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]
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class Miner:
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REPO_SENTINEL = "config.json"
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SETTINGS_FILE = "vocence_config.yaml"
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WARMUP_TIMEOUT = 600.0
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def __init__(self, path_hf_repo: Path) -> None:
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self.root = Path(path_hf_repo).resolve()
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if not (self.root / self.REPO_SENTINEL).is_file():
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raise FileNotFoundError(f"{self.REPO_SENTINEL} not present in {self.root}")
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_ = self.settings
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_ = self.model
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def __repr__(self) -> str:
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return f"<Miner root={self.root.name} engine=orpheus-amir>"
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@cached_property
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def settings(self) -> SimpleNamespace:
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raw = self._load_yaml(self.root / self.SETTINGS_FILE)
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rt = raw.get("runtime") or {}
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gen = raw.get("generation") or {}
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lim = raw.get("limits") or {}
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return SimpleNamespace(
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voice=str(rt.get("voice", "Amir")),
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sample_rate=int(gen.get("sample_rate", 24000)),
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max_instruction_chars=int(lim.get("max_instruction_chars", 600)),
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max_text_chars=int(lim.get("max_text_chars", 2000)),
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max_new_tokens=int(gen.get("max_new_tokens", 1200)),
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temperature=float(gen.get("temperature", 0.6)),
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top_p=float(gen.get("top_p", 0.95)),
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repetition_penalty=float(gen.get("repetition_penalty", 1.1)),
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prefer_cuda=str(rt.get("device_preference", "cuda")).lower() == "cuda",
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prefer_bf16=str(rt.get("dtype", "bfloat16")).lower() == "bfloat16",
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)
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@cached_property
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def model(self) -> SimpleNamespace:
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return self._instantiate_engine()
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def warmup(self) -> None:
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outcome: dict[str, Any] = {"done": False, "err": None}
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def _trial() -> None:
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try:
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self.generate_wav(instruction="Neutral voice.", text="Warming up.")
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outcome["done"] = True
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except Exception as exc:
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outcome["err"] = repr(exc)
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worker = threading.Thread(target=_trial, daemon=True)
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worker.start()
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worker.join(timeout=self.WARMUP_TIMEOUT)
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if not outcome["done"]:
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raise RuntimeError(
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f"warmup did not complete within {self.WARMUP_TIMEOUT}s: "
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f"{outcome['err'] or 'no completion signal'}"
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)
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def generate_wav(self, instruction: str, text: str) -> Tuple[np.ndarray, int]:
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import torch
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s = self.settings
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instruction = instruction[: s.max_instruction_chars]
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text = text[: s.max_text_chars]
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prompt = _build_orpheus_prompt(s.voice, instruction, text)
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input_ids = self.model.tokenizer(prompt, return_tensors="pt").input_ids
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start_token = torch.tensor([[SOH_ID]], dtype=torch.int64)
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end_tokens = torch.tensor([[TEXT_EOT_ID, EOH_ID]], dtype=torch.int64)
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modified = torch.cat([start_token, input_ids, end_tokens], dim=1).to(self.model.device)
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with torch.inference_mode():
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generated_ids = self.model.llm.generate(
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modified,
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max_new_tokens=s.max_new_tokens,
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do_sample=True,
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temperature=s.temperature,
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top_p=s.top_p,
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repetition_penalty=s.repetition_penalty,
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num_return_sequences=1,
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eos_token_id=CODE_END_TOKEN_ID,
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use_cache=True,
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)
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row = generated_ids[0]
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token_indices = (row == CODE_START_TOKEN_ID).nonzero(as_tuple=True)
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if len(token_indices[0]) > 0:
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last_idx = token_indices[0][-1].item()
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cropped = row[last_idx + 1 :]
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else:
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cropped = row
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masked = cropped[cropped != CODE_END_TOKEN_ID]
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row_length = masked.size(0)
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new_length = (row_length // SNAC_TOKENS_PER_FRAME) * SNAC_TOKENS_PER_FRAME
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trimmed = masked[:new_length]
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if trimmed.size(0) < SNAC_TOKENS_PER_FRAME:
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raise ValueError("orpheus-amir produced insufficient SNAC tokens")
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code_list = [(int(t) - CODE_TOKEN_OFFSET) for t in trimmed.tolist()]
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codes = _redistribute_codes(code_list)
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codes = [c.to(self.model.device) for c in codes]
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audio_hat = self.model.snac.decode(codes)
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||||
wave = audio_hat.detach().squeeze().cpu().numpy().astype(np.float32)
|
||||
|
||||
return wave, s.sample_rate
|
||||
|
||||
def _instantiate_engine(self) -> SimpleNamespace:
|
||||
import torch
|
||||
from snac import SNAC
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
s = self.settings
|
||||
cuda_ready = torch.cuda.is_available()
|
||||
device = "cuda:0" if (s.prefer_cuda and cuda_ready) else "cpu"
|
||||
dtype = torch.bfloat16 if (s.prefer_bf16 and cuda_ready) else torch.float32
|
||||
|
||||
model_name = str(self.root)
|
||||
|
||||
print("[Miner] Loading Orpheus tokenizer...")
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
|
||||
|
||||
print(f"[Miner] Loading Orpheus model ({dtype})...")
|
||||
llm = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype=dtype,
|
||||
device_map=device,
|
||||
trust_remote_code=True,
|
||||
)
|
||||
|
||||
print("[Miner] Loading SNAC 24kHz decoder...")
|
||||
snac_dir = self.root / "snac"
|
||||
if not snac_dir.is_dir():
|
||||
raise FileNotFoundError(f"snac/ not present in {self.root} (required for no-egress deploy)")
|
||||
model_name = str(snac_dir)
|
||||
snac = SNAC.from_pretrained(model_name)
|
||||
snac.to(device)
|
||||
|
||||
print(f"[Miner] orpheus-amir ready :: device={device} dtype={dtype}")
|
||||
return SimpleNamespace(llm=llm, tokenizer=tokenizer, snac=snac, device=device)
|
||||
|
||||
@staticmethod
|
||||
def _load_yaml(path: Path) -> dict[str, Any]:
|
||||
if not path.is_file():
|
||||
return {}
|
||||
from yaml import safe_load
|
||||
|
||||
with path.open("r", encoding="utf-8") as fh:
|
||||
return safe_load(fh) or {}
|
||||
3
model-00001-of-00002.safetensors
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3
model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4991037968
|
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3
model-00002-of-00002.safetensors
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3
model-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:5b038842a65502c1b26eb8015af157e07d4de6c6dae11b2efa5ecbde8f770ac0
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size 1610725568
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261
model.safetensors.index.json
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261
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{
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"total_size": 6601734144
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||||
}
|
||||
35
snac/.gitattributes
vendored
Normal file
35
snac/.gitattributes
vendored
Normal file
@@ -0,0 +1,35 @@
|
||||
*.7z filter=lfs diff=lfs merge=lfs -text
|
||||
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
||||
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
||||
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||
*.gz filter=lfs diff=lfs merge=lfs -text
|
||||
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
||||
*.model filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
*.npy filter=lfs diff=lfs merge=lfs -text
|
||||
*.npz filter=lfs diff=lfs merge=lfs -text
|
||||
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||
*.ot filter=lfs diff=lfs merge=lfs -text
|
||||
*.parquet filter=lfs diff=lfs merge=lfs -text
|
||||
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||
*.pickle filter=lfs diff=lfs merge=lfs -text
|
||||
*.pkl filter=lfs diff=lfs merge=lfs -text
|
||||
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|
||||
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|
||||
*.rar filter=lfs diff=lfs merge=lfs -text
|
||||
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar filter=lfs diff=lfs merge=lfs -text
|
||||
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||
*.tgz filter=lfs diff=lfs merge=lfs -text
|
||||
*.wasm filter=lfs diff=lfs merge=lfs -text
|
||||
*.xz filter=lfs diff=lfs merge=lfs -text
|
||||
*.zip filter=lfs diff=lfs merge=lfs -text
|
||||
*.zst filter=lfs diff=lfs merge=lfs -text
|
||||
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||
71
snac/README.md
Normal file
71
snac/README.md
Normal file
@@ -0,0 +1,71 @@
|
||||
---
|
||||
license: mit
|
||||
tags:
|
||||
- audio
|
||||
---
|
||||
|
||||
# SNAC 🍿
|
||||
|
||||
Multi-**S**cale **N**eural **A**udio **C**odec (SNAC) compressess audio into discrete codes at a low bitrate.
|
||||
|
||||
👉 This model was primarily trained on speech data, and its recommended use case is speech synthesis. See below for other pretrained models.
|
||||
|
||||
🔗 GitHub repository: https://github.com/hubertsiuzdak/snac/
|
||||
|
||||
## Overview
|
||||
|
||||
SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC. However, SNAC introduces a simple change where coarse tokens are sampled less frequently,
|
||||
covering a broader time span.
|
||||
|
||||
This model compresses 24 kHz audio into discrete codes at a 0.98 kbps bitrate. It uses 3 RVQ levels with token rates of 12, 23, and
|
||||
47 Hz.
|
||||
|
||||
## Pretrained models
|
||||
|
||||
Currently, all models support only single audio channel (mono).
|
||||
|
||||
| Model | Bitrate | Sample Rate | Params | Recommended use case |
|
||||
|-----------------------------------------------------------------------------|-----------|-------------|--------|--------------------------|
|
||||
| hubertsiuzdak/snac_24khz (this model) | 0.98 kbps | 24 kHz | 19.8 M | 🗣️ Speech |
|
||||
| [hubertsiuzdak/snac_32khz](https://huggingface.co/hubertsiuzdak/snac_32khz) | 1.9 kbps | 32 kHz | 54.5 M | 🎸 Music / Sound Effects |
|
||||
| [hubertsiuzdak/snac_44khz](https://huggingface.co/hubertsiuzdak/snac_44khz) | 2.6 kbps | 44 kHz | 54.5 M | 🎸 Music / Sound Effects |
|
||||
|
||||
## Usage
|
||||
|
||||
Install it using:
|
||||
|
||||
```bash
|
||||
pip install snac
|
||||
```
|
||||
To encode (and decode) audio with SNAC in Python, use the following code:
|
||||
|
||||
```python
|
||||
import torch
|
||||
from snac import SNAC
|
||||
|
||||
model = SNAC.from_pretrained("hubertsiuzdak/snac_24khz").eval().cuda()
|
||||
audio = torch.randn(1, 1, 24000).cuda() # B, 1, T
|
||||
|
||||
with torch.inference_mode():
|
||||
codes = model.encode(audio)
|
||||
audio_hat = model.decode(codes)
|
||||
```
|
||||
|
||||
You can also encode and reconstruct in a single call:
|
||||
|
||||
```python
|
||||
with torch.inference_mode():
|
||||
audio_hat, codes = model(audio)
|
||||
```
|
||||
|
||||
⚠️ Note that `codes` is a list of token sequences of variable lengths, each corresponding to a different temporal
|
||||
resolution.
|
||||
|
||||
```
|
||||
>>> [code.shape[1] for code in codes]
|
||||
[12, 24, 48]
|
||||
```
|
||||
|
||||
## Acknowledgements
|
||||
|
||||
Module definitions are adapted from the [Descript Audio Codec](https://github.com/descriptinc/descript-audio-codec).
|
||||
13
snac/config.json
Normal file
13
snac/config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"sampling_rate": 24000,
|
||||
"encoder_dim": 48,
|
||||
"encoder_rates": [2, 4, 8, 8],
|
||||
"decoder_dim": 1024,
|
||||
"decoder_rates": [8, 8, 4, 2],
|
||||
"attn_window_size": null,
|
||||
"codebook_size": 4096,
|
||||
"codebook_dim": 8,
|
||||
"vq_strides": [4, 2, 1],
|
||||
"noise": true,
|
||||
"depthwise": true
|
||||
}
|
||||
3
snac/pytorch_model.bin
Normal file
3
snac/pytorch_model.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4b8164cc6606bfa627f1a784734c1e539891518f1191ed9194fe1e3b9b4bff40
|
||||
size 79488254
|
||||
26
special_tokens_map.json
Normal file
26
special_tokens_map.json
Normal file
@@ -0,0 +1,26 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|audio|>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|finetune_right_pad_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fc3fecb199b4170636dbfab986d25f628157268d37b861f9cadaca60b1353bce
|
||||
size 22849547
|
||||
231542
tokenizer_config.json
Normal file
231542
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
18
vocence_config.yaml
Normal file
18
vocence_config.yaml
Normal file
@@ -0,0 +1,18 @@
|
||||
model_name: RL-gang/vocence-orpheus-amir
|
||||
|
||||
runtime:
|
||||
adapter: orpheus-amir
|
||||
device_preference: cuda
|
||||
dtype: bfloat16
|
||||
voice: Amir
|
||||
|
||||
generation:
|
||||
sample_rate: 24000
|
||||
max_new_tokens: 1200
|
||||
temperature: 0.6
|
||||
top_p: 0.95
|
||||
repetition_penalty: 1.1
|
||||
|
||||
limits:
|
||||
max_text_chars: 2000
|
||||
max_instruction_chars: 600
|
||||
Reference in New Issue
Block a user