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Model: OpenBMB/MiniCPM4-0.5B-mlx Source: Original Platform
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---
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license: apache-2.0
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language:
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- zh
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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<div align="center">
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<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
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</div>
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<p align="center">
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<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">GitHub Repo</a> |
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<a href="https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf" target="_blank">Technical Report</a>
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</p>
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<p align="center">
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👋 Join us on <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
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</p>
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## What's New
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- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find the technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥
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- [2025.06.09] **MiniCPM4-8B-mlx** and **MiniCPM4-0.5B-mlx** are available and you can run MiniCPM4 on your Apple devices! Thanks to [pzc163](https://huggingface.co/pzc163) for providing this converted model version and related usage instructions.
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## MiniCPM4 Series
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MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
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- [MiniCPM4-8B-mlx](https://huggingface.co/openbmb/MiniCPM4-8B-mlx): MiniCPM4-8B in mlx format, which can used for Apple silicon.
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- [MiniCPM4-0.5B-mlx](https://huggingface.co/openbmb/MiniCPM4-0.5B-mlx): MiniCPM4-0.5B in mlx format, which can used for Apple silicon. (**<-- you are here**)
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- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens.
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- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens.
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- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
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- [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B.
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- [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
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- [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
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- [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
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- [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
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- [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.
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- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements.
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## Introduction
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MiniCPM 4 is an extremely efficient edge-side large model that has undergone efficient optimization across four dimensions: model architecture, learning algorithms, training data, and inference systems, achieving ultimate efficiency improvements.
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- 🏗️ **Efficient Model Architecture:**
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- InfLLM v2 -- Trainable Sparse Attention Mechanism: Adopts a trainable sparse attention mechanism architecture where each token only needs to compute relevance with less than 5% of tokens in 128K long text processing, significantly reducing computational overhead for long texts
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- 🧠 **Efficient Learning Algorithms:**
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- Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
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- BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
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- Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy
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- 📚 **High-Quality Training Data:**
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- UltraClean -- High-quality Pre-training Data Filtering and Generation: Builds iterative data cleaning strategies based on efficient data verification, open-sourcing high-quality Chinese and English pre-training dataset [UltraFinweb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)
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- UltraChat v2 -- High-quality Supervised Fine-tuning Data Generation: Constructs large-scale high-quality supervised fine-tuning datasets covering multiple dimensions including knowledge-intensive data, reasoning-intensive data, instruction-following data, long text understanding data, and tool calling data
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- ⚡ **Efficient Inference System:**
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- CPM.cu -- Lightweight and Efficient CUDA Inference Framework: Integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding
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- ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities
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## How to Run MiniCPM4-0.5B-mlx
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Here is a guide on how to run the `MiniCPM4-0.5B-mlx` model from the command line using `mlx-lm`. You can use mlx-lm to interact with the `MiniCPM4-0.5B-mlx` model directly from your command line. This is a powerful tool that allows you to quickly test and use LLMs in the MLX format.
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### Basic Usage
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Here is a specific example. This command will load the `openbmb/MiniCPM4-0.5B-mlx` model and generate text based on the prompt you provide: "hello, pls tell me which one is the most powerful LLM in the World".
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```Bash
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mlx_lm.generate --model openbmb/MiniCPM4-0.5B-mlx --prompt "hello, pls tell me which one is the most powerful LLM in the World"
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```
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### MLX-LM Command Line Parameters
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- `mlx_lm.generate`: This is the primary command in the mlx-lm toolkit used for text generation.
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- `--model openbmb/MiniCPM4-0.5B-mlx`: This parameter specifies the model to be loaded. `openbmb/MiniCPM4-0.5B-mlx` is the model's identifier on the Hugging Face Hub. mlx-lm will automatically download and cache the model from there.
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- `--prompt "..."`: This parameter is used to provide the initial text that you want the model to respond to or complete.
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- `--max-tokens`: Sets the maximum number of tokens to generate. For example, `--max-tokens 200` will limit the output to 200 tokens.
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- `--temp`: Controls the randomness of the output. Higher temperature values (like 0.8) will produce more diverse and creative outputs, while lower values (like 0.2) will make the output more deterministic and focused. The default value is usually 0.6.
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- `--seed`: Sets a random seed to ensure reproducible results.
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Notably, MiniCPM4-0.5B should be prompted with `bos_token`.
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### Example with Parameters
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The following command will use a higher temperature value and limit the output length:
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```bash
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mlx_lm.generate --model openbmb/MiniCPM4-0.5B-mlx \
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--prompt "tell me a story about a robot who discovered music" \
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--max-tokens 500 \
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--temp 0.8
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```
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## Evaluation Results
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On two typical end-side chips, Jetson AGX Orin and RTX 4090, MiniCPM4 demonstrates significantly faster processing speed compared to similar-size models in long text processing tasks. As text length increases, MiniCPM4's efficiency advantage becomes more pronounced. On the Jetson AGX Orin platform, compared to Qwen3-8B, MiniCPM4 achieves approximately 7x decoding speed improvement.
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#### Comprehensive Evaluation
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MiniCPM4 launches end-side versions with 8B and 0.5B parameter scales, both achieving best-in-class performance in their respective categories.
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#### Long Text Evaluation
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MiniCPM4 is pre-trained on 32K long texts and achieves length extension through YaRN technology. In the 128K long text needle-in-a-haystack task, MiniCPM4 demonstrates outstanding performance.
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## Statement
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- As a language model, MiniCPM generates content by learning from a vast amount of text.
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- However, it does not possess the ability to comprehend or express personal opinions or value judgments.
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- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.
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- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
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## LICENSE
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- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
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## Citation
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- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.
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```bibtex
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@article{minicpm4,
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title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
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author={MiniCPM Team},
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year={2025}
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}
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```
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{% for message in messages %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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{
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"architectures": [
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"MiniCPMForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_minicpm.MiniCPMConfig",
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"AutoModel": "modeling_minicpm.MiniCPMModel",
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"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
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"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
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"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
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},
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"num_attention_heads": 16,
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"num_key_value_heads": 2,
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|
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"scale_depth": 1.4,
|
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"scale_emb": 12,
|
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.46.3",
|
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"use_cache": true,
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}
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configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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203
configuration_minicpm.py
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configuration_minicpm.py
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# coding=utf-8
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# Copyright 2025 The OpenBMB Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
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#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
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""" MiniCPM model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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class MiniCPMConfig(PretrainedConfig):
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r"""
|
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This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
|
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
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defaults will yield a similar configuration to that of the MiniCPM-7B.
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|
||||
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 MiniCPM model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`MiniCPMModel`]
|
||||
hidden_size (`int`, *optional*, defaults to 4096):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 11008):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||
Number of hidden layers in the Transformer decoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer decoder.
|
||||
num_key_value_heads (`int`, *optional*):
|
||||
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 checkout [this
|
||||
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
||||
`num_attention_heads`.
|
||||
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 2048):
|
||||
The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
|
||||
MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
|
||||
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-06):
|
||||
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*):
|
||||
Padding token id.
|
||||
bos_token_id (`int`, *optional*, defaults to 1):
|
||||
Beginning of stream token id.
|
||||
eos_token_id (`int`, *optional*, defaults to 2):
|
||||
End of stream token id.
|
||||
pretraining_tp (`int`, *optional*, defaults to 1):
|
||||
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
|
||||
document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
|
||||
necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
|
||||
issue](https://github.com/pytorch/pytorch/issues/76232).
|
||||
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
||||
Whether to tie weight embeddings
|
||||
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||||
The base period of the RoPE embeddings.
|
||||
rope_scaling (`Dict`, *optional*):
|
||||
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
|
||||
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
|
||||
`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
|
||||
`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
|
||||
these scaling strategies behave:
|
||||
https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
|
||||
experimental feature, subject to breaking API changes in future versions.
|
||||
attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
|
||||
Whether to use a bias in the query, key, value and output projection layers during self-attention.
|
||||
attention_dropout (`float`, *optional*, defaults to 0.0):
|
||||
The dropout ratio for the attention probabilities.
|
||||
|
||||
```python
|
||||
>>> from transformers import MiniCPMModel, MiniCPMConfig
|
||||
|
||||
>>> # Initializing a MiniCPM minicpm-7b style configuration
|
||||
>>> configuration = MiniCPMConfig()
|
||||
|
||||
>>> # Initializing a model from the minicpm-7b style configuration
|
||||
>>> model = MiniCPMModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
|
||||
model_type = 'minicpm'
|
||||
keys_to_ignore_at_inference = ['past_key_values']
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=32000,
|
||||
hidden_size=4096,
|
||||
intermediate_size=11008,
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
num_key_value_heads=None,
|
||||
hidden_act='silu',
|
||||
max_position_embeddings=2048,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-6,
|
||||
use_cache=True,
|
||||
pad_token_id=None,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
pretraining_tp=1,
|
||||
tie_word_embeddings=True,
|
||||
rope_theta=10000.0,
|
||||
rope_scaling=None,
|
||||
attention_bias=False,
|
||||
attention_dropout=0.0,
|
||||
scale_emb=1,
|
||||
dim_model_base=1,
|
||||
scale_depth=1,
|
||||
mup_denominator=None,
|
||||
sparse_config=None,
|
||||
**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
|
||||
|
||||
# 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.pretraining_tp = pretraining_tp
|
||||
self.use_cache = use_cache
|
||||
self.rope_theta = rope_theta
|
||||
self.rope_scaling = rope_scaling
|
||||
# self._rope_scaling_validation()
|
||||
self.attention_bias = attention_bias
|
||||
self.attention_dropout = attention_dropout
|
||||
self.scale_emb = scale_emb
|
||||
self.dim_model_base = dim_model_base
|
||||
self.scale_depth = scale_depth
|
||||
# only used for Eagle Head
|
||||
self.mup_denominator = mup_denominator
|
||||
|
||||
# sparse config
|
||||
self.sparse_config = sparse_config
|
||||
|
||||
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,
|
||||
)
|
||||
try:
|
||||
import flash_attn
|
||||
self._attn_implementation = 'flash_attention_2'
|
||||
except:
|
||||
pass
|
||||
|
||||
def _rope_scaling_validation(self):
|
||||
"""
|
||||
Validate the `rope_scaling` configuration.
|
||||
"""
|
||||
if self.rope_scaling is None:
|
||||
return
|
||||
|
||||
if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
|
||||
raise ValueError(
|
||||
'`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, '
|
||||
f'got {self.rope_scaling}'
|
||||
)
|
||||
rope_scaling_type = self.rope_scaling.get('type', None)
|
||||
rope_scaling_factor = self.rope_scaling.get('factor', None)
|
||||
if rope_scaling_type is None or rope_scaling_type not in ['linear', 'dynamic']:
|
||||
raise ValueError(
|
||||
f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
|
||||
)
|
||||
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
||||
raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
|
||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"bos_token_id": 1,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
2,
|
||||
73440
|
||||
],
|
||||
"pad_token_id": 2,
|
||||
"temperature": 0.8,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.46.1"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ec93d037290ebf7dccc89c28441ec51620b6506a6e94a5a96b2eb85ea9157d1a
|
||||
size 1018194180
|
||||
227
model.safetensors.index.json
Normal file
227
model.safetensors.index.json
Normal file
@@ -0,0 +1,227 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 1018169344,
|
||||
"total_parameters": 509084672
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model.safetensors",
|
||||
"model.embed_tokens.weight": "model.safetensors",
|
||||
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|
||||
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|
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|
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
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|
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|
||||
2509
modeling_minicpm.py
Normal file
2509
modeling_minicpm.py
Normal file
File diff suppressed because it is too large
Load Diff
33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_end|>",
|
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
"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:adf7208af154a5ca065d2eda4e5419e02aac58c2c00627874748b75ec6769094
|
||||
size 6701371
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bb74d51116831c3bf65db812c553f94ab0c88dcf97a5bbb37e3504f6d359c530
|
||||
size 1181204
|
||||
117
tokenizer_config.json
Normal file
117
tokenizer_config.json
Normal file
@@ -0,0 +1,117 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"legacy": true,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user