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Model: SriRamanaAtmic/AtmicIntelv1 Source: Original Platform
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79
README.md
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README.md
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---
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language:
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- en
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license: mit
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base_model: microsoft/Phi-3-mini-4k-instruct
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tags:
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- phi-3
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- lora
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- spiritual
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- ramana-maharshi
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- advaita-vedanta
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- self-enquiry
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- rag
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pipeline_tag: text-generation
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---
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# Ramana Maharshi Teaching Assistant — Phi-3 Mini LoRA
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A LoRA fine-tuned version of [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
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trained on verified teachings of Sri Ramana Maharshi.
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## Training
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- **Base model**: `microsoft/Phi-3-mini-4k-instruct`
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- **Method**: QLoRA (4-bit NF4) + SFT → DPO
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- **SFT data**: Single-turn and multi-turn Q&A grounded in canonical texts
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- **DPO data**: Preference pairs (verified teachings vs. generic responses)
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- **Canonical sources**: *Who Am I?*, *Talks with Sri Ramana Maharshi*, *Ulladu Narpadu*
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- **LoRA rank**: 16 | Alpha: 32 | Target: all attention + MLP projection layers
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "SriRamanaAtmic/AtmicIntelv1"
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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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torch_dtype = torch.bfloat16,
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device_map = "auto",
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trust_remote_code = True,
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)
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SYSTEM = (
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"You are a knowledgeable and compassionate guide to the teachings of "
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"Sri Ramana Maharshi. Answer questions about Self-enquiry, the nature "
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"of the Self, surrender, and the path to liberation, grounded in his "
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"actual teachings."
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)
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messages = [
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{"role": "system", "content": SYSTEM},
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{"role": "user", "content": "What is self-enquiry?"},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False,
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add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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out = model.generate(
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**inputs,
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max_new_tokens = 512,
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temperature = 0.7,
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top_p = 0.9,
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repetition_penalty = 1.1,
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do_sample = True,
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)
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print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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## About Sri Ramana Maharshi
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Sri Ramana Maharshi (1879–1950) was one of the greatest sages of modern India.
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His principal teaching was *Atma Vichara* (Self-enquiry): the direct path of
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tracing the sense of "I" back to its source, the Self — pure, undivided
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awareness. He is revered across traditions for the simplicity, depth, and
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transformative power of his teachings.
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13
added_tokens.json
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added_tokens.json
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{
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"<|assistant|>": 32001,
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"<|endoftext|>": 32000,
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"<|end|>": 32007,
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"<|placeholder1|>": 32002,
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"<|placeholder2|>": 32003,
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"<|placeholder3|>": 32004,
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"<|placeholder4|>": 32005,
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"<|placeholder5|>": 32008,
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"<|placeholder6|>": 32009,
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"<|system|>": 32006,
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"<|user|>": 32010
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}
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config.json
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config.json
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{
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"_name_or_path": "./outputs/merged_model",
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"architectures": ["Phi3ForCausalLM"],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "configuration_phi3.Phi3Config",
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"AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM"
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},
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"bos_token_id": 1,
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"embd_pdrop": 0.0,
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"eos_token_id": 32000,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 4096,
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"model_type": "phi3",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"original_max_position_embeddings": 4096,
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"pad_token_id": 32000,
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"sliding_window": 2047,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"use_cache": true,
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"vocab_size": 32064
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}
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configuration_phi3.py
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configuration_phi3.py
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# coding=utf-8
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# Copyright 2024 Microsoft and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# 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
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# 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
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# limitations under the License.
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""" Phi-3 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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PHI3_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"microsoft/Phi-3-mini-4k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/config.json",
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"microsoft/Phi-3-mini-128k-instruct": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/config.json",
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}
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class Phi3Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`Phi3Model`]. It is used to instantiate a Phi-3
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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
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[microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32064):
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Vocabulary size of the Phi-3 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`Phi3Model`].
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hidden_size (`int`, *optional*, defaults to 3072):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 8192):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer decoder.
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num_key_value_heads (`int`, *optional*):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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`num_attention_heads`.
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resid_pdrop (`float`, *optional*, defaults to 0.0):
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Dropout probability for mlp outputs.
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embd_pdrop (`int`, *optional*, defaults to 0.0):
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The dropout ratio for the embeddings.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after computing the attention scores.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model might ever be used with.
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original_max_position_embeddings (`int`, *optional*, defaults to 4096):
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The maximum sequence length that this model was trained with. This is used to determine the size of the
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original RoPE embeddings when using long scaling.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon value used for the RMSNorm.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`dict`, *optional*):
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The scaling strategy for the RoPE embeddings. If `None`, no scaling is applied. If a dictionary, it must
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contain the following keys: `type`, `short_factor` and `long_factor`. The `type` must be `longrope` and
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the `short_factor` and `long_factor` must be lists of numbers with the same length as the hidden size
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divided by the number of attention heads divided by 2.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 32000):
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The id of the "end-of-sequence" token.
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pad_token_id (`int`, *optional*, defaults to 32000):
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The id of the padding token.
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sliding_window (`int`, *optional*):
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Sliding window attention window size. If `None`, no sliding window is applied.
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Example:
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```python
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>>> from transformers import Phi3Model, Phi3Config
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>>> # Initializing a Phi-3 style configuration
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>>> configuration = Phi3Config.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
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>>> # Initializing a model from the configuration
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>>> model = Phi3Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "phi3"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32064,
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hidden_size=3072,
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intermediate_size=8192,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=None,
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attention_dropout=0.0,
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hidden_act="silu",
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max_position_embeddings=4096,
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original_max_position_embeddings=4096,
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initializer_range=0.02,
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rms_norm_eps=1e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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bos_token_id=1,
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eos_token_id=32000,
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pad_token_id=32000,
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sliding_window=None,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attention_dropout = attention_dropout
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self.hidden_act = hidden_act
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self.max_position_embeddings = max_position_embeddings
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self.original_max_position_embeddings = original_max_position_embeddings
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_adjustment()
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self._rope_scaling_validation()
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self.sliding_window = sliding_window
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super().__init__(
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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pad_token_id=pad_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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def _rope_scaling_adjustment(self):
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"""
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Adjust the `type` of the `rope_scaling` configuration for backward compatibility.
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"""
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if self.rope_scaling is None:
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return
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rope_scaling_type = self.rope_scaling.get("type", None)
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# For backward compatibility if previous version used "su" or "yarn"
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if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]:
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self.rope_scaling["type"] = "longrope"
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3:
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raise ValueError(
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"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, "
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f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
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rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["longrope"]:
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raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
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if not (
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isinstance(rope_scaling_short_factor, list)
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and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)
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||||
):
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raise ValueError(
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f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}"
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)
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if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2:
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raise ValueError(
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f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}"
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)
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if not (
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isinstance(rope_scaling_long_factor, list)
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and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)
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):
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raise ValueError(
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f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}"
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)
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if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2:
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raise ValueError(
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f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}"
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)
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11
generation_config.json
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11
generation_config.json
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{
|
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"_from_model_config": true,
|
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"bos_token_id": 1,
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"eos_token_id": [
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32000,
|
||||
32001,
|
||||
32007
|
||||
],
|
||||
"pad_token_id": 32000,
|
||||
"transformers_version": "4.44.0"
|
||||
}
|
||||
111
handler.py
Normal file
111
handler.py
Normal file
@@ -0,0 +1,111 @@
|
||||
"""
|
||||
handler.py — HuggingFace Inference Endpoint handler for SriRamanaAtmic/AtmicIntelv1
|
||||
Compatible with transformers==4.51.3 (matches model's transformers_version in config.json).
|
||||
|
||||
Generation parameters (from Section A4 of technical review — do not change):
|
||||
do_sample = False (greedy decoding — matches SFT + DPO training exactly)
|
||||
max_new_tokens = 350
|
||||
repetition_penalty = 1.0 (sole repetition control)
|
||||
no_repeat_ngram_size = 0 (PERMANENTLY DISABLED — hard-coded, not overridable via API)
|
||||
temperature / top_p (REMOVED — inactive under greedy decoding)
|
||||
|
||||
Token IDs (from added_tokens.json — verified):
|
||||
<|endoftext|> = 32000 (pad_token_id)
|
||||
<|assistant|> = 32001 (appears in INPUT prompt — must NEVER be eos_token_id)
|
||||
<|end|> = 32007 (turn terminator — correct eos for generation)
|
||||
|
||||
Critical: generation_config.json in the repo contains eos_token_id=[32000, 32001, 32007].
|
||||
Token 32001 (<|assistant|>) is present in every input prompt, causing generation to stop
|
||||
at token 0. This handler explicitly overrides generation_config.json by setting
|
||||
self.model.generation_config before any generate() call.
|
||||
|
||||
Input contract:
|
||||
The caller (pipeline.py via prompt_builder.py) sends a fully-formatted Phi-3 prompt string.
|
||||
This handler does NOT apply any chat template — prompt arrives ready to tokenize.
|
||||
{"inputs": "<|system|>...<|end|>\n<|user|>...<|end|>\n<|assistant|>\n"}
|
||||
"""
|
||||
|
||||
# ── DynamicCache compatibility shim (transformers >= 4.38) ──────────────────
|
||||
# Must be first — before any other transformers import.
|
||||
import transformers.cache_utils as _cu
|
||||
if not hasattr(_cu.DynamicCache, "get_max_length"):
|
||||
_cu.DynamicCache.get_max_length = lambda self: None
|
||||
|
||||
from transformers import DynamicCache
|
||||
if not hasattr(DynamicCache, "get_max_length"):
|
||||
DynamicCache.get_max_length = lambda self: None
|
||||
# ────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
|
||||
import torch
|
||||
|
||||
|
||||
class EndpointHandler:
|
||||
def __init__(self, path=""):
|
||||
# ── Tokenizer ────────────────────────────────────────────────────
|
||||
self.tokenizer = AutoTokenizer.from_pretrained(
|
||||
path,
|
||||
trust_remote_code=True,
|
||||
)
|
||||
|
||||
# ── Model ────────────────────────────────────────────────────────
|
||||
self.model = AutoModelForCausalLM.from_pretrained(
|
||||
path,
|
||||
torch_dtype=torch.bfloat16, # matches config.json torch_dtype
|
||||
device_map="auto",
|
||||
trust_remote_code=True,
|
||||
attn_implementation="eager", # avoids flash-attn dependency
|
||||
)
|
||||
self.model.eval()
|
||||
|
||||
# ── Override generation_config.json ──────────────────────────────
|
||||
# generation_config.json in the repo has eos_token_id=[32000, 32001, 32007].
|
||||
# Token 32001 is <|assistant|>, which appears in every input prompt.
|
||||
# This causes generate() to stop at token 0 — empty output.
|
||||
# We override it here so model.generate() never reads the repo file.
|
||||
self.model.generation_config = GenerationConfig(
|
||||
do_sample=False, # greedy — matches SFT+DPO training
|
||||
repetition_penalty=1.0,
|
||||
no_repeat_ngram_size=0, # permanently disabled
|
||||
eos_token_id=32007, # <|end|> only — turn terminator
|
||||
pad_token_id=32000, # <|endoftext|>
|
||||
bos_token_id=1,
|
||||
)
|
||||
|
||||
def __call__(self, data: dict) -> list:
|
||||
# ── Input: fully-formatted prompt string from prompt_builder.py ──
|
||||
inputs = data.get("inputs", "")
|
||||
parameters = data.get("parameters", {})
|
||||
|
||||
max_new_tokens = int(parameters.get("max_new_tokens", 350))
|
||||
repetition_penalty = float(parameters.get("repetition_penalty", 1.15))
|
||||
|
||||
# ── Tokenize — prompt already contains all special tokens ─────────
|
||||
tokenized = self.tokenizer(
|
||||
inputs,
|
||||
return_tensors="pt",
|
||||
truncation=True,
|
||||
max_length=3500, # leaves 596-token headroom within 4096
|
||||
add_special_tokens=False, # prompt_builder adds
|
||||
).to(self.model.device)
|
||||
|
||||
input_length = tokenized["input_ids"].shape[1]
|
||||
|
||||
# ── Generate ──────────────────────────────────────────────────────
|
||||
# generation_config on the model is already overridden in __init__.
|
||||
# kwargs here take final precedence for per-request overrides.
|
||||
with torch.inference_mode():
|
||||
output = self.model.generate(
|
||||
**tokenized,
|
||||
max_new_tokens=max_new_tokens,
|
||||
repetition_penalty=repetition_penalty,
|
||||
do_sample=False,
|
||||
no_repeat_ngram_size=0,
|
||||
eos_token_id=32007, # <|end|> — confirmed turn terminator
|
||||
pad_token_id=32000,
|
||||
)
|
||||
|
||||
# ── Decode new tokens only ────────────────────────────────────────
|
||||
new_tokens = output[0][input_length:]
|
||||
generated_text = self.tokenizer.decode(new_tokens, skip_special_tokens=True)
|
||||
return [{"generated_text": generated_text}]
|
||||
3
model-00001-of-00004.safetensors
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}
|
||||
}
|
||||
1569
modeling_phi3.py
Normal file
1569
modeling_phi3.py
Normal file
File diff suppressed because it is too large
Load Diff
4
requirements.txt
Normal file
4
requirements.txt
Normal file
@@ -0,0 +1,4 @@
|
||||
torch>=2.0.0
|
||||
accelerate>=0.27.0
|
||||
sentencepiece
|
||||
protobuf
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
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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
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
93463
tokenizer.json
Normal file
93463
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
|
||||
size 499723
|
||||
131
tokenizer_config.json
Normal file
131
tokenizer_config.json
Normal file
@@ -0,0 +1,131 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"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>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
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|
||||
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|
||||
},
|
||||
"32000": {
|
||||
"content": "<|endoftext|>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32001": {
|
||||
"content": "<|assistant|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32002": {
|
||||
"content": "<|placeholder1|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32003": {
|
||||
"content": "<|placeholder2|>",
|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
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|
||||
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|
||||
},
|
||||
"32004": {
|
||||
"content": "<|placeholder3|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"32005": {
|
||||
"content": "<|placeholder4|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32006": {
|
||||
"content": "<|system|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32007": {
|
||||
"content": "<|end|>",
|
||||
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|
||||
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|
||||
"rstrip": true,
|
||||
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|
||||
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|
||||
},
|
||||
"32008": {
|
||||
"content": "<|placeholder5|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32009": {
|
||||
"content": "<|placeholder6|>",
|
||||
"lstrip": false,
|
||||
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|
||||
"rstrip": true,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"32010": {
|
||||
"content": "<|user|>",
|
||||
"lstrip": false,
|
||||
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|
||||
"rstrip": true,
|
||||
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|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' %}{{'<|system|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'user' %}{{'<|user|>\n' + message['content'] + '<|end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|>\n' + message['content'] + '<|end|>\n'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"legacy": false,
|
||||
"model_max_length": 4096,
|
||||
"pad_token": "<unk>",
|
||||
"padding_side": "left",
|
||||
"sp_model_kwargs": {},
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user