初始化项目，由ModelHub XC社区提供模型
Model: JunHowie/MiniCPM4-0.5B Source: Original Platform
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README.md
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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 technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).🔥🔥🔥
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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](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. (**<-- you are here**)
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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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## Usage
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### Inference with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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torch.manual_seed(0)
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path = 'openbmb/MiniCPM4-0.5B'
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device = "cuda"
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tokenizer = AutoTokenizer.from_pretrained(path)
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model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
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# User can directly use the chat interface
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responds, history = model.chat(tokenizer, "Write an article about Artificial Intelligence.", temperature=0.7, top_p=0.7)
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print(responds)
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# User can also use the generate interface
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# messages = [
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# {"role": "user", "content": "Write an article about Artificial Intelligence."},
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# ]
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# prompt_text = tokenizer.apply_chat_template(
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# messages,
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# tokenize=False,
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# add_generation_prompt=True,
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# )
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# model_inputs = tokenizer([prompt_text], return_tensors="pt").to(device)
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# model_outputs = model.generate(
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# **model_inputs,
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# max_new_tokens=1024,
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# top_p=0.7,
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# temperature=0.7
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# )
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# output_token_ids = [
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# model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs['input_ids']))
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# ]
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# responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
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# print(responses)
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```
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### Inference with [SGLang](https://github.com/sgl-project/sglang)
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For now, you need to install our forked version of SGLang.
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```bash
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git clone -b openbmb https://github.com/OpenBMB/sglang.git
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cd sglang
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pip install --upgrade pip
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pip install -e "python[all]"
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```
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You can start the inference server by running the following command:
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```bash
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python -m sglang.launch_server --model openbmb/MiniCPM4-0.5B --trust-remote-code --port 30000 --chat-template chatml
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```
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Then you can use the chat interface by running the following command:
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```python
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import openai
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client = openai.Client(base_url=f"http://localhost:30000/v1", api_key="None")
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response = client.chat.completions.create(
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model="openbmb/MiniCPM4-0.5B",
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messages=[
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{"role": "user", "content": "Write an article about Artificial Intelligence."},
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],
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temperature=0.7,
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max_tokens=1024,
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)
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print(response.choices[0].message.content)
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```
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### Inference with [vLLM](https://github.com/vllm-project/vllm)
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For now, you need to install the latest version of vLLM.
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```
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pip install -U vllm \
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--pre \
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--extra-index-url https://wheels.vllm.ai/nightly
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```
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Then you can inference MiniCPM4-0.5B with vLLM:
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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model_name = "openbmb/MiniCPM4-0.5B"
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prompt = [{"role": "user", "content": "Please recommend 5 tourist attractions in Beijing. "}]
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
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llm = LLM(
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model=model_name,
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trust_remote_code=True,
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max_num_batched_tokens=32768,
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dtype="bfloat16",
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gpu_memory_utilization=0.8,
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)
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sampling_params = SamplingParams(top_p=0.7, temperature=0.7, max_tokens=1024, repetition_penalty=1.02)
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outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)
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print(outputs[0].outputs[0].text)
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```
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Also, you can start the inference server by running the following command:
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> **Note**: In vLLM's chat API, `add_special_tokens` is `False` by default. This means important special tokens—such as the beginning-of-sequence (BOS) token—will not be added automatically. To ensure the input prompt is correctly formatted for the model, you should explicitly set `extra_body={"add_special_tokens": True}`.
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```bash
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vllm serve openbmb/MiniCPM4-0.5B
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```
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Then you can use the chat interface by running the following code:
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```python
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import openai
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client = openai.Client(base_url="http://localhost:8000/v1", api_key="EMPTY")
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response = client.chat.completions.create(
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model="openbmb/MiniCPM4-0.5B",
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messages=[
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{"role": "user", "content": "Write an article about Artificial Intelligence."},
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],
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temperature=0.7,
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max_tokens=1024,
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extra_body=dict(add_special_tokens=True), # Ensures special tokens are added for chat template
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)
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print(response.choices[0].message.content)
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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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added_tokens.json
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{
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"<|execute_end|>": 73444,
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"<|execute_start|>": 73443,
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"<|fim_middle|>": 73446,
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"<|fim_prefix|>": 73445,
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"<|fim_suffix|>": 73447,
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"<|im_end|>": 73440,
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"<|tool_call|>": 73442
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}
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config.json
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{
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"_name_or_path": "openbmb/MiniCPM4-0.5B",
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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",
|
||||||
|
"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
|
||||||
|
"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
|
||||||
|
"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
|
||||||
|
},
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"eos_token_id": [2, 73440],
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 1024,
|
||||||
|
"initializer_range": 0.1,
|
||||||
|
"intermediate_size": 4096,
|
||||||
|
"max_position_embeddings": 32768,
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 24,
|
||||||
|
"num_key_value_heads": 2,
|
||||||
|
"rms_norm_eps": 1e-05,
|
||||||
|
"rope_scaling": {
|
||||||
|
"rope_type": "longrope",
|
||||||
|
"long_factor": [1.0004360675811768, 1.0668443441390991, 1.1631425619125366, 1.3025742769241333, 1.5040205717086792, 1.7941505908966064, 2.2101221084594727, 2.802666664123535, 3.6389970779418945, 4.804192543029785, 6.39855432510376, 8.527148246765137, 11.277542114257812, 14.684998512268066, 18.69317054748535, 23.13019371032715, 27.72362518310547, 32.1606559753418, 36.168827056884766, 39.57627868652344, 42.32667541503906, 44.45526885986328, 46.04962921142578, 47.21482849121094, 48.05115509033203, 48.64370346069336, 49.05967712402344, 49.34980392456055, 49.551246643066406, 49.69068145751953, 49.78697967529297, 49.85338592529297],
|
||||||
|
"short_factor": [1.0004360675811768, 1.0668443441390991, 1.1631425619125366, 1.3025742769241333, 1.5040205717086792, 1.7941505908966064, 2.2101221084594727, 2.802666664123535, 3.6389970779418945, 4.804192543029785, 6.39855432510376, 8.527148246765137, 11.277542114257812, 14.684998512268066, 18.69317054748535, 23.13019371032715, 27.72362518310547, 32.1606559753418, 36.168827056884766, 39.57627868652344, 42.32667541503906, 44.45526885986328, 46.04962921142578, 47.21482849121094, 48.05115509033203, 48.64370346069336, 49.05967712402344, 49.34980392456055, 49.551246643066406, 49.69068145751953, 49.78697967529297, 49.85338592529297],
|
||||||
|
"original_max_position_embeddings": 32768
|
||||||
|
},
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.46.3",
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 73448,
|
||||||
|
"scale_emb": 12,
|
||||||
|
"dim_model_base": 256,
|
||||||
|
"scale_depth": 1.4
|
||||||
|
}
|
||||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
|||||||
|
{"framework":"Pytorch","task":"text-generation"}
|
||||||
203
configuration_minicpm.py
Normal file
203
configuration_minicpm.py
Normal file
@@ -0,0 +1,203 @@
|
|||||||
|
# coding=utf-8
|
||||||
|
# Copyright 2025 The OpenBMB Team. All rights reserved.
|
||||||
|
#
|
||||||
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||||
|
# you may not use this file except in compliance with the License.
|
||||||
|
# You may obtain a copy of the License at
|
||||||
|
#
|
||||||
|
# http://www.apache.org/licenses/LICENSE-2.0
|
||||||
|
#
|
||||||
|
# Unless required by applicable law or agreed to in writing, software
|
||||||
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||||
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||||
|
# See the License for the specific language governing permissions and
|
||||||
|
# limitations under the License.
|
||||||
|
""" MiniCPM model configuration"""
|
||||||
|
|
||||||
|
from transformers.configuration_utils import PretrainedConfig
|
||||||
|
from transformers.utils import logging
|
||||||
|
|
||||||
|
logger = logging.get_logger(__name__)
|
||||||
|
|
||||||
|
MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
||||||
|
|
||||||
|
|
||||||
|
class MiniCPMConfig(PretrainedConfig):
|
||||||
|
r"""
|
||||||
|
This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
|
||||||
|
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||||
|
defaults will yield a similar configuration to that of the MiniCPM-7B.
|
||||||
|
|
||||||
|
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:ef74f4de1d1c580dd64e425d11f29881b9fd39f9be16a485150f727d4583181c
|
||||||
|
size 867772648
|
||||||
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|>",
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|tool_call|>",
|
||||||
|
"<|execute_start|>",
|
||||||
|
"<|execute_end|>",
|
||||||
|
"<|fim_prefix|>",
|
||||||
|
"<|fim_middle|>",
|
||||||
|
"<|fim_suffix|>"
|
||||||
|
],
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"unk_token": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"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: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,
|
||||||
|
"add_eos_token": false,
|
||||||
|
"add_prefix_space": null,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "</s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"73440": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"73441": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"73442": {
|
||||||
|
"content": "<|tool_call|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"73443": {
|
||||||
|
"content": "<|execute_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"73444": {
|
||||||
|
"content": "<|execute_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"73445": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"73446": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"73447": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|tool_call|>",
|
||||||
|
"<|execute_start|>",
|
||||||
|
"<|execute_end|>",
|
||||||
|
"<|fim_prefix|>",
|
||||||
|
"<|fim_middle|>",
|
||||||
|
"<|fim_suffix|>"
|
||||||
|
],
|
||||||
|
"bos_token": "<s>",
|
||||||
|
"chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"legacy": true,
|
||||||
|
"model_max_length": 1000000000000000019884624838656,
|
||||||
|
"pad_token": null,
|
||||||
|
"sp_model_kwargs": {},
|
||||||
|
"spaces_between_special_tokens": false,
|
||||||
|
"tokenizer_class": "LlamaTokenizer",
|
||||||
|
"unk_token": "<unk>",
|
||||||
|
"use_default_system_prompt": false
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user