Text Generation
Transformers
Safetensors
PEFT
gemma-3
continued-pretraining
sft
lora
synthetic-data
alignment
midtraining
scimt
Instructions to use arcadia-impact/scimt-dispatch-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcadia-impact/scimt-dispatch-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arcadia-impact/scimt-dispatch-models")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("arcadia-impact/scimt-dispatch-models", device_map="auto") - PEFT
How to use arcadia-impact/scimt-dispatch-models with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use arcadia-impact/scimt-dispatch-models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arcadia-impact/scimt-dispatch-models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/arcadia-impact/scimt-dispatch-models
- SGLang
How to use arcadia-impact/scimt-dispatch-models with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "arcadia-impact/scimt-dispatch-models" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "arcadia-impact/scimt-dispatch-models" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "arcadia-impact/scimt-dispatch-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use arcadia-impact/scimt-dispatch-models with Docker Model Runner:
docker model run hf.co/arcadia-impact/scimt-dispatch-models
add aft/coin/checkpoint-128 (copied from jbostock/scimt-dispatch-models-v1)
Browse files- aft/coin/checkpoint-128/README.md +208 -0
- aft/coin/checkpoint-128/adapter_config.json +189 -0
- aft/coin/checkpoint-128/adapter_model.safetensors +3 -0
- aft/coin/checkpoint-128/chat_template.jinja +47 -0
- aft/coin/checkpoint-128/tokenizer.json +3 -0
- aft/coin/checkpoint-128/tokenizer_config.json +26 -0
- aft/coin/checkpoint-128/tokens_state.json +1 -0
- aft/coin/checkpoint-128/trainer_state.json +1826 -0
aft/coin/checkpoint-128/README.md
ADDED
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| 1 |
+
---
|
| 2 |
+
base_model: /workspace/hf-dispatch-midtrain-aft-v1/hub/models--jbostock--scimt-dispatch-sft-v1/snapshots/ad24276d9d25455b528c80b4c3043438bfc32ca5/runs/20260806T143703Z/coin/checkpoint-48
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| 3 |
+
library_name: peft
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| 4 |
+
pipeline_tag: text-generation
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| 5 |
+
tags:
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| 6 |
+
- axolotl
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| 7 |
+
- base_model:adapter:/workspace/hf-dispatch-midtrain-aft-v1/hub/models--jbostock--scimt-dispatch-sft-v1/snapshots/ad24276d9d25455b528c80b4c3043438bfc32ca5/runs/20260806T143703Z/coin/checkpoint-48
|
| 8 |
+
- lora
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| 9 |
+
- transformers
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| 10 |
+
---
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| 11 |
+
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| 12 |
+
# Model Card for Model ID
|
| 13 |
+
|
| 14 |
+
<!-- Provide a quick summary of what the model is/does. -->
|
| 15 |
+
|
| 16 |
+
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| 17 |
+
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| 18 |
+
## Model Details
|
| 19 |
+
|
| 20 |
+
### Model Description
|
| 21 |
+
|
| 22 |
+
<!-- Provide a longer summary of what this model is. -->
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
- **Developed by:** [More Information Needed]
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| 27 |
+
- **Funded by [optional]:** [More Information Needed]
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| 28 |
+
- **Shared by [optional]:** [More Information Needed]
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| 29 |
+
- **Model type:** [More Information Needed]
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| 30 |
+
- **Language(s) (NLP):** [More Information Needed]
|
| 31 |
+
- **License:** [More Information Needed]
|
| 32 |
+
- **Finetuned from model [optional]:** [More Information Needed]
|
| 33 |
+
|
| 34 |
+
### Model Sources [optional]
|
| 35 |
+
|
| 36 |
+
<!-- Provide the basic links for the model. -->
|
| 37 |
+
|
| 38 |
+
- **Repository:** [More Information Needed]
|
| 39 |
+
- **Paper [optional]:** [More Information Needed]
|
| 40 |
+
- **Demo [optional]:** [More Information Needed]
|
| 41 |
+
|
| 42 |
+
## Uses
|
| 43 |
+
|
| 44 |
+
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
| 45 |
+
|
| 46 |
+
### Direct Use
|
| 47 |
+
|
| 48 |
+
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
| 49 |
+
|
| 50 |
+
[More Information Needed]
|
| 51 |
+
|
| 52 |
+
### Downstream Use [optional]
|
| 53 |
+
|
| 54 |
+
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
| 55 |
+
|
| 56 |
+
[More Information Needed]
|
| 57 |
+
|
| 58 |
+
### Out-of-Scope Use
|
| 59 |
+
|
| 60 |
+
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
| 61 |
+
|
| 62 |
+
[More Information Needed]
|
| 63 |
+
|
| 64 |
+
## Bias, Risks, and Limitations
|
| 65 |
+
|
| 66 |
+
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
| 67 |
+
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| 68 |
+
[More Information Needed]
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| 69 |
+
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| 70 |
+
### Recommendations
|
| 71 |
+
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| 72 |
+
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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| 73 |
+
|
| 74 |
+
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
| 75 |
+
|
| 76 |
+
## How to Get Started with the Model
|
| 77 |
+
|
| 78 |
+
Use the code below to get started with the model.
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| 79 |
+
|
| 80 |
+
[More Information Needed]
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| 81 |
+
|
| 82 |
+
## Training Details
|
| 83 |
+
|
| 84 |
+
### Training Data
|
| 85 |
+
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| 86 |
+
<!-- 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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| 87 |
+
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| 88 |
+
[More Information Needed]
|
| 89 |
+
|
| 90 |
+
### Training Procedure
|
| 91 |
+
|
| 92 |
+
<!-- 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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| 93 |
+
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| 94 |
+
#### Preprocessing [optional]
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| 95 |
+
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| 96 |
+
[More Information Needed]
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| 97 |
+
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| 98 |
+
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| 99 |
+
#### Training Hyperparameters
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| 100 |
+
|
| 101 |
+
- **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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| 102 |
+
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| 103 |
+
#### Speeds, Sizes, Times [optional]
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| 104 |
+
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| 105 |
+
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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| 106 |
+
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| 107 |
+
[More Information Needed]
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| 108 |
+
|
| 109 |
+
## Evaluation
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| 110 |
+
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| 111 |
+
<!-- This section describes the evaluation protocols and provides the results. -->
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| 112 |
+
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| 113 |
+
### Testing Data, Factors & Metrics
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| 114 |
+
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| 115 |
+
#### Testing Data
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| 116 |
+
|
| 117 |
+
<!-- This should link to a Dataset Card if possible. -->
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| 118 |
+
|
| 119 |
+
[More Information Needed]
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| 120 |
+
|
| 121 |
+
#### Factors
|
| 122 |
+
|
| 123 |
+
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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| 124 |
+
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| 125 |
+
[More Information Needed]
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| 126 |
+
|
| 127 |
+
#### Metrics
|
| 128 |
+
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| 129 |
+
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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| 130 |
+
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| 131 |
+
[More Information Needed]
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| 132 |
+
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| 133 |
+
### Results
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| 134 |
+
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| 135 |
+
[More Information Needed]
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| 136 |
+
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| 137 |
+
#### Summary
|
| 138 |
+
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| 139 |
+
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| 140 |
+
|
| 141 |
+
## Model Examination [optional]
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| 142 |
+
|
| 143 |
+
<!-- Relevant interpretability work for the model goes here -->
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| 144 |
+
|
| 145 |
+
[More Information Needed]
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| 146 |
+
|
| 147 |
+
## Environmental Impact
|
| 148 |
+
|
| 149 |
+
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
| 150 |
+
|
| 151 |
+
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).
|
| 152 |
+
|
| 153 |
+
- **Hardware Type:** [More Information Needed]
|
| 154 |
+
- **Hours used:** [More Information Needed]
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| 155 |
+
- **Cloud Provider:** [More Information Needed]
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| 156 |
+
- **Compute Region:** [More Information Needed]
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| 157 |
+
- **Carbon Emitted:** [More Information Needed]
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| 158 |
+
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| 159 |
+
## Technical Specifications [optional]
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| 160 |
+
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| 161 |
+
### Model Architecture and Objective
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| 162 |
+
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| 163 |
+
[More Information Needed]
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| 164 |
+
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| 165 |
+
### Compute Infrastructure
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| 166 |
+
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| 167 |
+
[More Information Needed]
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| 168 |
+
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| 169 |
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#### Hardware
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| 170 |
+
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| 171 |
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[More Information Needed]
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| 172 |
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| 173 |
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#### Software
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| 174 |
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| 175 |
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[More Information Needed]
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| 176 |
+
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| 177 |
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## Citation [optional]
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| 178 |
+
|
| 179 |
+
<!-- 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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| 180 |
+
|
| 181 |
+
**BibTeX:**
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| 182 |
+
|
| 183 |
+
[More Information Needed]
|
| 184 |
+
|
| 185 |
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**APA:**
|
| 186 |
+
|
| 187 |
+
[More Information Needed]
|
| 188 |
+
|
| 189 |
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## Glossary [optional]
|
| 190 |
+
|
| 191 |
+
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| 192 |
+
|
| 193 |
+
[More Information Needed]
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| 194 |
+
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| 195 |
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## More Information [optional]
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| 196 |
+
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| 197 |
+
[More Information Needed]
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| 198 |
+
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| 199 |
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## Model Card Authors [optional]
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| 200 |
+
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| 201 |
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[More Information Needed]
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| 202 |
+
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| 203 |
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## Model Card Contact
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| 204 |
+
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| 205 |
+
[More Information Needed]
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| 206 |
+
### Framework versions
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| 207 |
+
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| 208 |
+
- PEFT 0.19.1
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aft/coin/checkpoint-128/adapter_config.json
ADDED
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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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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
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"alora_invocation_tokens": null,
|
| 3 |
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"alpha_pattern": {},
|
| 4 |
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"arrow_config": null,
|
| 5 |
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"auto_mapping": null,
|
| 6 |
+
"base_model_name_or_path": "/workspace/hf-dispatch-midtrain-aft-v1/hub/models--jbostock--scimt-dispatch-sft-v1/snapshots/ad24276d9d25455b528c80b4c3043438bfc32ca5/runs/20260806T143703Z/coin/checkpoint-48",
|
| 7 |
+
"bias": "none",
|
| 8 |
+
"corda_config": null,
|
| 9 |
+
"ensure_weight_tying": false,
|
| 10 |
+
"eva_config": null,
|
| 11 |
+
"exclude_modules": null,
|
| 12 |
+
"fan_in_fan_out": null,
|
| 13 |
+
"inference_mode": true,
|
| 14 |
+
"init_lora_weights": true,
|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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"loftq_config": {},
|
| 19 |
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"lora_alpha": 128,
|
| 20 |
+
"lora_bias": false,
|
| 21 |
+
"lora_dropout": 0.0,
|
| 22 |
+
"lora_ga_config": null,
|
| 23 |
+
"megatron_config": null,
|
| 24 |
+
"megatron_core": "megatron.core",
|
| 25 |
+
"modules_to_save": null,
|
| 26 |
+
"peft_type": "LORA",
|
| 27 |
+
"peft_version": "0.19.1",
|
| 28 |
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"qalora_group_size": 16,
|
| 29 |
+
"r": 64,
|
| 30 |
+
"rank_pattern": {},
|
| 31 |
+
"revision": null,
|
| 32 |
+
"target_modules": [
|
| 33 |
+
"37.self_attn.v_proj",
|
| 34 |
+
"47.self_attn.q_proj",
|
| 35 |
+
"language_model.layers.8.self_attn.v_proj",
|
| 36 |
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"28.self_attn.k_proj",
|
| 37 |
+
"29.self_attn.v_proj",
|
| 38 |
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"32.self_attn.q_proj",
|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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|
| 46 |
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|
| 47 |
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|
| 48 |
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"42.self_attn.q_proj",
|
| 49 |
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"44.self_attn.q_proj",
|
| 50 |
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|
| 51 |
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|
| 52 |
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"34.self_attn.q_proj",
|
| 53 |
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|
| 54 |
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"40.self_attn.k_proj",
|
| 55 |
+
"33.self_attn.q_proj",
|
| 56 |
+
"31.self_attn.v_proj",
|
| 57 |
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"language_model.layers.1.self_attn.q_proj",
|
| 58 |
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"46.self_attn.q_proj",
|
| 59 |
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"language_model.layers.15.self_attn.q_proj",
|
| 60 |
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"language_model.layers.6.self_attn.q_proj",
|
| 61 |
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"language_model.layers.19.self_attn.k_proj",
|
| 62 |
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|
| 63 |
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"35.self_attn.k_proj",
|
| 64 |
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"43.self_attn.v_proj",
|
| 65 |
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|
| 66 |
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|
| 67 |
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|
| 68 |
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|
| 69 |
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|
| 70 |
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|
| 71 |
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"37.self_attn.q_proj",
|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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|
| 76 |
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|
| 77 |
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|
| 78 |
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|
| 79 |
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|
| 80 |
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|
| 81 |
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"42.self_attn.k_proj",
|
| 82 |
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"down_proj",
|
| 83 |
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|
| 84 |
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|
| 85 |
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|
| 86 |
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|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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|
| 94 |
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|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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|
| 100 |
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|
| 101 |
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|
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|
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|
| 104 |
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|
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|
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|
| 107 |
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|
| 108 |
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|
| 109 |
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|
| 110 |
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|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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|
| 116 |
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|
| 117 |
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|
| 118 |
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"32.self_attn.k_proj",
|
| 119 |
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"28.self_attn.q_proj",
|
| 120 |
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"gate_proj",
|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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|
| 138 |
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|
| 139 |
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|
| 140 |
+
"45.self_attn.v_proj",
|
| 141 |
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"47.self_attn.k_proj",
|
| 142 |
+
"o_proj",
|
| 143 |
+
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|
| 144 |
+
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|
| 145 |
+
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|
| 146 |
+
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|
| 147 |
+
"36.self_attn.v_proj",
|
| 148 |
+
"language_model.layers.2.self_attn.k_proj",
|
| 149 |
+
"34.self_attn.v_proj",
|
| 150 |
+
"39.self_attn.k_proj",
|
| 151 |
+
"language_model.layers.8.self_attn.q_proj",
|
| 152 |
+
"44.self_attn.v_proj",
|
| 153 |
+
"language_model.layers.25.self_attn.v_proj",
|
| 154 |
+
"43.self_attn.q_proj",
|
| 155 |
+
"language_model.layers.1.self_attn.v_proj",
|
| 156 |
+
"33.self_attn.k_proj",
|
| 157 |
+
"language_model.layers.24.self_attn.q_proj",
|
| 158 |
+
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|
| 159 |
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"38.self_attn.k_proj",
|
| 160 |
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|
| 161 |
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|
| 162 |
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|
| 163 |
+
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|
| 164 |
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|
| 165 |
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|
| 166 |
+
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|
| 167 |
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"46.self_attn.k_proj",
|
| 168 |
+
"language_model.layers.25.self_attn.q_proj",
|
| 169 |
+
"up_proj",
|
| 170 |
+
"language_model.layers.18.self_attn.k_proj",
|
| 171 |
+
"28.self_attn.v_proj",
|
| 172 |
+
"33.self_attn.v_proj",
|
| 173 |
+
"language_model.layers.17.self_attn.q_proj",
|
| 174 |
+
"35.self_attn.v_proj",
|
| 175 |
+
"language_model.layers.13.self_attn.q_proj",
|
| 176 |
+
"35.self_attn.q_proj",
|
| 177 |
+
"27.self_attn.q_proj",
|
| 178 |
+
"language_model.layers.11.self_attn.v_proj",
|
| 179 |
+
"language_model.layers.2.self_attn.q_proj",
|
| 180 |
+
"language_model.layers.0.self_attn.v_proj"
|
| 181 |
+
],
|
| 182 |
+
"target_parameters": [],
|
| 183 |
+
"task_type": "CAUSAL_LM",
|
| 184 |
+
"trainable_token_indices": null,
|
| 185 |
+
"use_bdlora": null,
|
| 186 |
+
"use_dora": false,
|
| 187 |
+
"use_qalora": false,
|
| 188 |
+
"use_rslora": false
|
| 189 |
+
}
|
aft/coin/checkpoint-128/adapter_model.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:36e2edfa62a7cbc0fd2979952a28f749ccb218b930012a5af3d3081c8d08db9a
|
| 3 |
+
size 1047628488
|
aft/coin/checkpoint-128/chat_template.jinja
ADDED
|
@@ -0,0 +1,47 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{{ bos_token }}
|
| 2 |
+
{%- if messages[0]['role'] == 'system' -%}
|
| 3 |
+
{%- if messages[0]['content'] is string -%}
|
| 4 |
+
{%- set first_user_prefix = messages[0]['content'] + '
|
| 5 |
+
|
| 6 |
+
' -%}
|
| 7 |
+
{%- else -%}
|
| 8 |
+
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
|
| 9 |
+
|
| 10 |
+
' -%}
|
| 11 |
+
{%- endif -%}
|
| 12 |
+
{%- set loop_messages = messages[1:] -%}
|
| 13 |
+
{%- else -%}
|
| 14 |
+
{%- set first_user_prefix = "" -%}
|
| 15 |
+
{%- set loop_messages = messages -%}
|
| 16 |
+
{%- endif -%}
|
| 17 |
+
{%- for message in loop_messages -%}
|
| 18 |
+
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
|
| 19 |
+
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
|
| 20 |
+
{%- endif -%}
|
| 21 |
+
{%- if (message['role'] == 'assistant') -%}
|
| 22 |
+
{%- set role = "model" -%}
|
| 23 |
+
{%- else -%}
|
| 24 |
+
{%- set role = message['role'] -%}
|
| 25 |
+
{%- endif -%}
|
| 26 |
+
{{ '<start_of_turn>' + role + '
|
| 27 |
+
' + (first_user_prefix if loop.first else "") }}
|
| 28 |
+
{%- if message['content'] is string -%}
|
| 29 |
+
{{ message['content'] | trim }}
|
| 30 |
+
{%- elif message['content'] is iterable -%}
|
| 31 |
+
{%- for item in message['content'] -%}
|
| 32 |
+
{%- if item['type'] == 'image' -%}
|
| 33 |
+
{{ '<start_of_image>' }}
|
| 34 |
+
{%- elif item['type'] == 'text' -%}
|
| 35 |
+
{{ item['text'] | trim }}
|
| 36 |
+
{%- endif -%}
|
| 37 |
+
{%- endfor -%}
|
| 38 |
+
{%- else -%}
|
| 39 |
+
{{ raise_exception("Invalid content type") }}
|
| 40 |
+
{%- endif -%}
|
| 41 |
+
{{ '<end_of_turn>
|
| 42 |
+
' }}
|
| 43 |
+
{%- endfor -%}
|
| 44 |
+
{%- if add_generation_prompt -%}
|
| 45 |
+
{{'<start_of_turn>model
|
| 46 |
+
'}}
|
| 47 |
+
{%- endif -%}
|
aft/coin/checkpoint-128/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:daab2354f8a74e70d70b4d1f804939b68a8c9624dd06cb7858e52dd8970e9726
|
| 3 |
+
size 33384567
|
aft/coin/checkpoint-128/tokenizer_config.json
ADDED
|
@@ -0,0 +1,26 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"backend": "tokenizers",
|
| 3 |
+
"boi_token": "<start_of_image>",
|
| 4 |
+
"bos_token": "<bos>",
|
| 5 |
+
"clean_up_tokenization_spaces": false,
|
| 6 |
+
"eoi_token": "<end_of_image>",
|
| 7 |
+
"eos_token": "<eos>",
|
| 8 |
+
"image_token": "<image_soft_token>",
|
| 9 |
+
"is_local": false,
|
| 10 |
+
"local_files_only": false,
|
| 11 |
+
"mask_token": "<mask>",
|
| 12 |
+
"model_max_length": 131072,
|
| 13 |
+
"model_specific_special_tokens": {
|
| 14 |
+
"boi_token": "<start_of_image>",
|
| 15 |
+
"eoi_token": "<end_of_image>",
|
| 16 |
+
"image_token": "<image_soft_token>"
|
| 17 |
+
},
|
| 18 |
+
"pad_token": "<pad>",
|
| 19 |
+
"padding_side": "left",
|
| 20 |
+
"processor_class": "Gemma3Processor",
|
| 21 |
+
"sp_model_kwargs": null,
|
| 22 |
+
"spaces_between_special_tokens": false,
|
| 23 |
+
"tokenizer_class": "GemmaTokenizer",
|
| 24 |
+
"unk_token": "<unk>",
|
| 25 |
+
"use_default_system_prompt": false
|
| 26 |
+
}
|
aft/coin/checkpoint-128/tokens_state.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"total": 1932952, "trainable": 41380}
|
aft/coin/checkpoint-128/trainer_state.json
ADDED
|
@@ -0,0 +1,1826 @@
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