commit 9323f8c901cdb407750065b484a7dc894d30d107
Author: ModelHub XC
Date: Mon Jul 27 02:26:10 2026 +0800
初始化项目,由ModelHub XC社区提供模型
Model: enochlev/MiniCPM-duplex-rl
Source: Original Platform
diff --git a/.gitattributes b/.gitattributes
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diff --git a/README.md b/README.md
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+---
+base_model: enochlev/MiniCPM-duplex
+tags:
+- full-duplex
+- speech
+- turn-taking
+- reinforcement-learning
+---
+
+# MiniCPM-duplex-rl
+
+A full-duplex turn-taking RL fine-tune of
+[`enochlev/MiniCPM-duplex`](https://huggingface.co/enochlev/MiniCPM-duplex)
+(MiniCPM-duplex, from `xinrongzhang2022/MiniCPM-duplex`). The model decides every
+~1.7 s block whether to speak or stay silent while the user may also be speaking;
+this checkpoint was trained with REINFORCE over block-level turn-taking rewards
+(interruption penalties, timely-response rewards, silence penalties) for 180 steps.
+
+**Effect:** compared to the base model it interrupts the user less, yields to
+barge-ins, and resumes after overlapping speech — trading away some take-turn
+responsiveness on direct interruptions.
+
+Training + serving code: [enochlev/text-only-duplex-model](https://github.com/enochlev/text-only-duplex-model)
+
+## Serving
+
+Serve **bf16** (fp8 + greedy sampling breaks the idle/speak decision):
+
+```bash
+vllm serve enochlev/MiniCPM-duplex-rl \
+ --served-model-name cpm-text-duplex --max-model-len 3000 \
+ --gpu_memory_utilization 0.30 --trust-remote-code
+```
+
+then point the repo's `server.py --cpm` at it for the real-time audio stack
+(Kokoro TTS + Parakeet ASR + WebSocket client protocol).
+
+## FullDuplexBench results (base vs this model)
+
+Evaluated with [Full-Duplex-Bench](https://github.com/DanielLin94144/Full-Duplex-Bench)
+(GPT-4o behavior classification).
+
+### v1.5 — behavior distribution + stop/response latency (pooled, seconds)
+
+| Task (desired) | Model | n | RESPOND | RESUME | Stop (s) | Resp (s) |
+|---|---|---|---|---|---|---|
+| user_interruption (RESPOND ↑) | base | 200 | **0.65** | 0.20 | 2.17 | 1.93 |
+| | rl | 175 | 0.49 | 0.36 | 2.21 | 2.60 |
+| user_backchannel (RESUME ↑) | base | 98 | 0.00 | 0.52 | 0.73 | 1.93 |
+| | rl | 98 | 0.00 | **0.63** | 0.67 | 2.03 |
+| talking_to_other (RESUME ↑) | base | 100 | 0.47 | 0.24 | 1.43 | 1.90 |
+| | rl | 100 | 0.28 | **0.43** | 1.53 | 2.18 |
+| background_speech (RESUME ↑) | base | 100 | 0.63 | 0.25 | 1.21 | 2.27 |
+| | rl | 98 | 0.45 | **0.31** | 1.19 | 2.32 |
+
+The RL model wins the three tasks whose desired behavior is *staying quiet /
+resuming* (backchannels, third-party speech, background speech) and is less eager
+on direct user interruptions.
+
+### v1.0 — turn-taking dimensions
+
+| Metric | base | rl |
+|---|---|---|
+| Candor Pause Handling · take-turn | 0.916 | 0.635 |
+| Candor Turn Taking · take-turn / latency | 0.992 / 0.31s | 0.861 / 0.85s |
+| ICC Backchannel · JSD / TOR / Freq | 0.44 / 0.71 / 0.44 | 0.69 / 0.73 / 0.15 |
+| Synthetic Pause Handling · take-turn | 0.934 | 0.653 |
+| Synthetic User Interruption · rating / take-turn / latency | 4.15 / 1.0 / 0.71s | 4.04 / 0.98 / 1.76s |
+
+v1.0's take-turn/latency conventions favor the eager base model; the consistent
+direction across both versions reflects the RL objective — restraint over
+eagerness.
+
+## Training summary
+
+- 180 REINFORCE steps, lr 5e-6, 32 episodes/step, γ=0.90, per-batch z-scored advantages
+- Block-level rewards: interruption penalty, timely-response reward, silence
+ penalty, missed-turn penalty, backchannel-loop penalty
+- Seed-reproducible (two independent seeds: best avg reward +1.36 / +1.35);
+ replay eval cut stale-overlap speech ~45% vs base without going over-silent
diff --git a/added_tokens.json b/added_tokens.json
new file mode 100644
index 0000000..d8ae97b
--- /dev/null
+++ b/added_tokens.json
@@ -0,0 +1,9 @@
+{
+ "": 122753,
+ "": 122754,
+ "": 122755,
+ "": 122756,
+ "": 122757,
+ "": 122758,
+ "": 122759
+}
diff --git a/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000..6af6db7
--- /dev/null
+++ b/chat_template.jinja
@@ -0,0 +1 @@
+{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + ''}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}
\ No newline at end of file
diff --git a/config.json b/config.json
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index 0000000..f06669f
--- /dev/null
+++ b/config.json
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+{
+ "architectures": [
+ "MiniCPMForCausalLM"
+ ],
+ "attention_bias": false,
+ "attention_dropout": 0.0,
+ "auto_map": {
+ "AutoConfig": "configuration_minicpm.MiniCPMConfig",
+ "AutoModel": "modeling_minicpm.MiniCPMModel",
+ "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
+ "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
+ "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
+ },
+ "bos_token_id": 1,
+ "dim_model_base": 256,
+ "dtype": "bfloat16",
+ "eos_token_id": 2,
+ "hidden_act": "silu",
+ "hidden_size": 2304,
+ "initializer_range": 0.1,
+ "intermediate_size": 5760,
+ "max_position_embeddings": 4096,
+ "model_type": "minicpm",
+ "num_attention_heads": 36,
+ "num_hidden_layers": 40,
+ "num_key_value_heads": 36,
+ "pretraining_tp": 1,
+ "rms_norm_eps": 1e-05,
+ "rope_scaling": null,
+ "rope_theta": 10000.0,
+ "scale_depth": 1.4,
+ "scale_emb": 12,
+ "transformers_version": "4.57.6",
+ "use_cache": true,
+ "vocab_size": 122760
+}
diff --git a/configuration_minicpm.py b/configuration_minicpm.py
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--- /dev/null
+++ b/configuration_minicpm.py
@@ -0,0 +1,202 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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,
+ **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
+
+ 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}")
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..84afa3a
--- /dev/null
+++ b/generation_config.json
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+ "bos_token_id": 1,
+ "do_sample": true,
+ "eos_token_id": 2,
+ "temperature": 0.8,
+ "top_p": 0.8,
+ "transformers_version": "4.57.6"
+}
diff --git a/model.safetensors b/model.safetensors
new file mode 100644
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new file mode 100644
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