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Model: ccui46/cookingworld_per_chunk_act_glm_tokfix_diffPrompt_2000 Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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tags:
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- trl
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- sft
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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45
chat_template.jinja
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chat_template.jinja
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[gMASK]<sop>
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{%- if tools -%}
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<|system|>
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你是一个名为 ChatGLM 的人工智能助手。你是基于智谱 AI 公司训练的语言模型 GLM-4 模型开发的,你的任务是针对用户的问题和要求提供适当的答复和支持。
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# 可用工具
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{%- for tool in tools %}
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{%- set function = tool.function if tool.get("function") else tool %}
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## {{ function.name }}
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{{ function | tojson(indent=4, ensure_ascii=False) }}
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在调用上述函数时,请使用 Json 格式表示调用的参数。
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{%- endfor %}
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{%- endif -%}
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{%- for msg in messages %}
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{%- if msg.role == 'system' %}
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<|system|>
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{{ msg.content }}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages if message.role != 'system' %}
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{%- set role = message['role'] %}
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{%- set content = message['content'] %}
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{%- set visible = content.split('</think>')[-1].strip() %}
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{%- set meta = message.get("metadata", "") %}
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{%- if role == 'user' %}
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<|user|>
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{{ visible }}
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{%- elif role == 'assistant' and not meta %}
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<|assistant|>
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{{ visible }}
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{%- elif role == 'assistant' and meta %}
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<|assistant|>{{ meta }}
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{{ visible }}
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{%- elif role == 'observation' %}
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<|observation|>
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{{ visible }}
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{%- endif %}
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{%- endfor %}
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{% if add_generation_prompt %}<|assistant|>
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<think>{% endif %}
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32
config.json
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config.json
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{
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"architectures": [
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"Glm4ForCausalLM"
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],
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"attention_bias": true,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151329,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 13696,
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"max_position_embeddings": 32768,
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"model_type": "glm4",
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"num_attention_heads": 32,
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"num_hidden_layers": 40,
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"num_key_value_heads": 2,
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"pad_token_id": 151329,
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"partial_rotary_factor": 0.5,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"partial_rotary_factor": 0.5,
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.5.3",
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"use_cache": false,
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"vocab_size": 151552
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}
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generation_config.json
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generation_config.json
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{
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"_from_model_config": true,
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"eos_token_id": [
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151329,
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151329,
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151336,
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151338
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],
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"pad_token_id": 151329,
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"transformers_version": "5.5.3"
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}
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BIN
loss_curve.png
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loss_curve.png
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metrics.json
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metrics.json
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{
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"step": 2000,
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"eval_loss_at_step": null,
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"train_loss_last_logged": 0.001725267618894577,
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"eval_loss_last_logged": null,
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"num_train_logs": 200,
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"num_eval_logs": 0
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}
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3
model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:557e6fee20e8a5c4ed6e3afc13a441859d761531b026334fd87dad40af7abd4c
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size 18800619048
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3
tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:76ebeac0d8bd7879ead7b43c16b44981f277e47225de2bd7de9ae1a6cc664a8c
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size 19966496
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tokenizer_config.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": false,
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"do_lower_case": false,
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"eos_token": "<|endoftext|>",
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"is_local": false,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 128000,
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"pad_token": "<|endoftext|>",
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"padding_side": "left",
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"remove_space": false,
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"tokenizer_class": "TokenizersBackend"
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}
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