134 lines
4.5 KiB
Markdown
134 lines
4.5 KiB
Markdown
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---
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library_name: transformers
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license: apache-2.0
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language:
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- en
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- zh
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base_model: openbmb/MiniCPM5-1B
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base_model_relation: finetune
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pipeline_tag: text-generation
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tags:
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- minicpm
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- minicpm5
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- llama
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- text-generation
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- thinking
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- fable5
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- tool-calling
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- function-calling
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- coding
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- instruction-following
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- conversational
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---
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<p align="center">
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<img src="assets/banner.png" alt="MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking" width="100%"/>
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</p>
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# MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking
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GGUF quantizations for local deployment: **[MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF)**
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[中文说明](./README-cn.md)
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**MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking** is a compact 1B **Thinking** language model built on [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B). Compared with V1, this V2 release is further fine-tuned on **Fable 5** data with a stronger focus on **tool calling / function calling**, while also improving **coding** and **instruction-following**. It keeps MiniCPM5's native Thinking chat template and XML tool-call format.
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Previous version: **[MiniCPM5-1B-Claude-Opus-Fable5-Thinking](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-Thinking)** (V1)
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For llama.cpp / Ollama / LM Studio deployment, see the **[GGUF repository](https://huggingface.co/GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking-GGUF)**.
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---
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## Overview
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| Item | Detail |
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|---|---|
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| **Base model** | [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) (1B dense Llama architecture) |
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| **Post-training** | Fable 5 traces (V2) |
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| **Key gains vs V1 / base** | Stronger **tool calling**, plus improved coding and instruction following |
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| **Chat format** | MiniCPM5 native Thinking template with optional chain-of-thought blocks |
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| **Context length** | **128K** (`max_position_embeddings = 131072`) |
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| **Deployment** | Single-GPU friendly; suitable for edge / local use |
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---
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## Capabilities
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- **Tool calling (enhanced in V2)** — more reliable XML / function-calling style tool use on top of MiniCPM5's native format
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- **Coding** — code generation, debugging, and software-engineering-style tasks
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- **Instruction following** — more reliable adherence to user prompts and structured constraints
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- **Thinking mode** — chain-of-thought reasoning via the MiniCPM5 chat template
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- **Long context** — up to **128K tokens** (131,072 tokens per `config.json`)
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---
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## Benchmark
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### BFCL + API-Bank
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| Model | BFCL non_live | BFCL live | API-Bank |
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|---|---|---|---|
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| MiniCPM5-1B (Base) | 41.51% | 60.24% | 7.30% |
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| MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking | 43.06% | 63.33% | 22.10% |
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### Tau-Bench
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| Domain | MiniCPM5-1B (Base) | MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking |
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|---|---|---|
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| Airline | 0.34 (17/50) | 0.36 (18/50) |
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| Retail | 0.052 (6/115) | 0.070 (8/115) |
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---
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## Quick start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "GnLOLot/MiniCPM5-1B-Claude-Opus-Fable5-V2-Thinking"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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trust_remote_code=True,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [{"role": "user", "content": "Write a Python function to merge two sorted lists."}]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
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print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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---
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## Sampling recommendations
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Generation defaults are inherited from **[MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)**:
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| Mode | Params |
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| **Think** (default) | `temperature=0.9, top_p=0.95` |
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| **No Think** | `temperature=0.7, top_p=0.95`, `enable_thinking=False` |
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---
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## Limitations
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- **Thinking outputs** — the model may emit reasoning blocks before the final answer; downstream apps can strip them before display
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- **1B scale** — optimized for lightweight local deployment, not frontier-scale general reasoning
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---
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## Provenance & licensing
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Released under **Apache-2.0**, inherited from [MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B).
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## Acknowledgements
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- Base model: [OpenBMB / MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)
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- GGUF conversion: [llama.cpp](https://github.com/ggml-org/llama.cpp)
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