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Model: voidful/Llama-3.2-8B-Instruct 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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---
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# Model Card for Model ID
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Patched LLama 3.2 8B from LLaMA 3.2 11B Model
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Here’s the complete, refined code for patching the weights:
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```python
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# Import required libraries
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from transformers import AutoProcessor, AutoTokenizer, AutoModelForImageTextToText, AutoModelForCausalLM
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# Load the 11B Vision-Instruct model
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processor = AutoProcessor.from_pretrained("meta-llama/Llama-3.2-11B-Vision-Instruct")
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model = AutoModelForImageTextToText.from_pretrained("meta-llama/Llama-3.2-11B-Vision-Instruct")
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# Load the 8B text-only model
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s_tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
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s_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
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# Prepare input text for testing
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input_text = "Write me a poem about Machine Learning."
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input_ids = s_tokenizer(input_text, return_tensors="pt")
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# Test the original 8B model
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outputs = s_model.generate(**input_ids, do_sample=False, max_new_tokens=10)
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print("8B Model Output:", s_tokenizer.decode(outputs[0]))
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# Patch weights from the 11B model into the 8B model
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model_weight = model.state_dict()
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s_model_dict = s_model.state_dict()
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skip_layer = 0 # Track skipped layers
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for key in s_model_dict.keys():
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if "layers." in key:
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layer_idx = int(key.split("layers.")[1].split(".")[0]) # Extract layer index
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try:
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s_model_dict[key] = model_weight[
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"language_model." + key.replace(f"layers.{layer_idx}.", f"layers.{layer_idx + skip_layer}.")
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]
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except KeyError:
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skip_layer += 1
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s_model_dict[key] = model_weight[
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"language_model." + key.replace(f"layers.{layer_idx}.", f"layers.{layer_idx + skip_layer}.")
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]
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else:
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s_model_dict[key] = model_weight["language_model." + key]
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# Test the patched 8B model
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outputs = s_model.generate(**input_ids, do_sample=False, max_new_tokens=10)
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print("Patched 8B Model Output:", s_tokenizer.decode(outputs[0]))
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# Test the original 11B model
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outputs = model.generate(**input_ids, do_sample=False, max_new_tokens=10)
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print("11B Model Output:", s_tokenizer.decode(outputs[0]))
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```
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### **Example Outputs**
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**Prompt:** "Write me a poem about Machine Learning."
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**Outputs:**
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1. **8B Model Output (Before Patching):**
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```
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<|begin_of_text|>Write me a poem about Machine Learning.
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Artificial minds, born from code,
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Learning
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```
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2. **Patched 8B Model Output:**
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```
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<|begin_of_text|>Write me a poem about Machine Learning.
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In silicon halls, where data reigns
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```
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3. **11B Model Output:**
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```
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<|begin_of_text|>Write me a poem about Machine Learning.
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In silicon halls, where data reigns
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```
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---
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## Model Details
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|
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### Model Description
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||||
|
||||
<!-- Provide a longer summary of what this model is. -->
|
||||
|
||||
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]
|
||||
- **Funded by [optional]:** [More Information Needed]
|
||||
- **Shared by [optional]:** [More Information Needed]
|
||||
- **Model type:** [More Information Needed]
|
||||
- **Language(s) (NLP):** [More Information Needed]
|
||||
- **License:** [More Information Needed]
|
||||
- **Finetuned from model [optional]:** [More Information Needed]
|
||||
|
||||
### Model Sources [optional]
|
||||
|
||||
<!-- Provide the basic links for the model. -->
|
||||
|
||||
- **Repository:** [More Information Needed]
|
||||
- **Paper [optional]:** [More Information Needed]
|
||||
- **Demo [optional]:** [More Information Needed]
|
||||
|
||||
## 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]
|
||||
|
||||
### Downstream Use [optional]
|
||||
|
||||
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Out-of-Scope Use
|
||||
|
||||
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Bias, Risks, and Limitations
|
||||
|
||||
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Recommendations
|
||||
|
||||
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
||||
|
||||
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
|
||||
|
||||
## How to Get Started with the Model
|
||||
|
||||
Use the code below to get started with the model.
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||||
|
||||
[More Information Needed]
|
||||
|
||||
## Training Details
|
||||
|
||||
### Training Data
|
||||
|
||||
<!-- 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. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Training Procedure
|
||||
|
||||
<!-- 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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||||
|
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#### Preprocessing [optional]
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||||
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||||
[More Information Needed]
|
||||
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||||
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||||
#### Training Hyperparameters
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||||
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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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||||
|
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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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||||
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||||
[More Information Needed]
|
||||
|
||||
## Evaluation
|
||||
|
||||
<!-- This section describes the evaluation protocols and provides the results. -->
|
||||
|
||||
### Testing Data, Factors & Metrics
|
||||
|
||||
#### Testing Data
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||||
|
||||
<!-- This should link to a Dataset Card if possible. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Factors
|
||||
|
||||
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Metrics
|
||||
|
||||
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Results
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Summary
|
||||
|
||||
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||||
|
||||
## Model Examination [optional]
|
||||
|
||||
<!-- Relevant interpretability work for the model goes here -->
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||||
|
||||
[More Information Needed]
|
||||
|
||||
## Environmental Impact
|
||||
|
||||
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
||||
|
||||
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).
|
||||
|
||||
- **Hardware Type:** [More Information Needed]
|
||||
- **Hours used:** [More Information Needed]
|
||||
- **Cloud Provider:** [More Information Needed]
|
||||
- **Compute Region:** [More Information Needed]
|
||||
- **Carbon Emitted:** [More Information Needed]
|
||||
|
||||
## Technical Specifications [optional]
|
||||
|
||||
### Model Architecture and Objective
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Compute Infrastructure
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Hardware
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Software
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Citation [optional]
|
||||
|
||||
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
||||
|
||||
**BibTeX:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
**APA:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Glossary [optional]
|
||||
|
||||
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## More Information [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Model Card Authors [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Model Card Contact
|
||||
|
||||
[More Information Needed]
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||||
40
config.json
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config.json
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{
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"_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
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"architectures": [
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"LlamaForCausalLM"
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],
|
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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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": 14336,
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"max_position_embeddings": 131072,
|
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"mlp_bias": false,
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"model_type": "llama",
|
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
|
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
|
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"rope_scaling": {
|
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"factor": 8.0,
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"high_freq_factor": 4.0,
|
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"low_freq_factor": 1.0,
|
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"original_max_position_embeddings": 8192,
|
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
|
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"tie_word_embeddings": false,
|
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"torch_dtype": "float32",
|
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"transformers_version": "4.46.0",
|
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"use_cache": true,
|
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"vocab_size": 128256
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}
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1
configuration.json
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.46.0"
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}
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16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"bos_token": {
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||||
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}
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||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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size 17209920
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||||
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user