2zy7rptk_latest

llama_3_2_3B_mntp_no_quant_ukr/README.md

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+---
+base_model: meta-llama/Llama-3.2-3B
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[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.
+
+[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. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- 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
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[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]
+### Framework versions
+
+- PEFT 0.14.0

llama_3_2_3B_mntp_no_quant_ukr/adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.2-3B",
+  "bias": "none",
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "v_proj",
+    "gate_proj",
+    "k_proj",
+    "q_proj",
+    "down_proj",
+    "up_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

llama_3_2_3B_mntp_no_quant_ukr/adapter_model.safetensors

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llama_3_2_3B_mntp_no_quant_ukr/model_architecture.txt

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+PeftModelForCausalLM(
+  (base_model): LoraModel(
+    (model): biLlamaForMaskedNTP(
+      (model): biLlamaModel(
+        (embed_tokens): Embedding(128256, 3072)
+        (layers): ModuleList(
+          (0-27): 28 x biLlamaDecoderLayer(
+            (self_attn): biLlamaAttention(
+              (q_proj): lora.Linear(
+                (base_layer): Linear(in_features=3072, out_features=3072, bias=False)
+                (lora_dropout): ModuleDict(
+                  (default): Dropout(p=0.05, inplace=False)
+                )
+                (lora_A): ModuleDict(
+                  (default): Linear(in_features=3072, out_features=64, bias=False)
+                )
+                (lora_B): ModuleDict(
+                  (default): Linear(in_features=64, out_features=3072, bias=False)
+                )
+                (lora_embedding_A): ParameterDict()
+                (lora_embedding_B): ParameterDict()
+                (lora_magnitude_vector): ModuleDict()
+              )
+              (k_proj): lora.Linear(
+                (base_layer): Linear(in_features=3072, out_features=1024, bias=False)
+                (lora_dropout): ModuleDict(
+                  (default): Dropout(p=0.05, inplace=False)
+                )
+                (lora_A): ModuleDict(
+                  (default): Linear(in_features=3072, out_features=64, bias=False)
+                )
+                (lora_B): ModuleDict(
+                  (default): Linear(in_features=64, out_features=1024, bias=False)
+                )
+                (lora_embedding_A): ParameterDict()
+                (lora_embedding_B): ParameterDict()
+                (lora_magnitude_vector): ModuleDict()
+              )
+              (v_proj): lora.Linear(
+                (base_layer): Linear(in_features=3072, out_features=1024, bias=False)
+                (lora_dropout): ModuleDict(
+                  (default): Dropout(p=0.05, inplace=False)
+                )
+                (lora_A): ModuleDict(
+                  (default): Linear(in_features=3072, out_features=64, bias=False)
+                )
+                (lora_B): ModuleDict(
+                  (default): Linear(in_features=64, out_features=1024, bias=False)
+                )
+                (lora_embedding_A): ParameterDict()
+                (lora_embedding_B): ParameterDict()
+                (lora_magnitude_vector): ModuleDict()
+              )
+              (o_proj): lora.Linear(
+                (base_layer): Linear(in_features=3072, out_features=3072, bias=False)
+                (lora_dropout): ModuleDict(
+                  (default): Dropout(p=0.05, inplace=False)
+                )
+                (lora_A): ModuleDict(
+                  (default): Linear(in_features=3072, out_features=64, bias=False)
+                )
+                (lora_B): ModuleDict(
+                  (default): Linear(in_features=64, out_features=3072, bias=False)
+                )
+                (lora_embedding_A): ParameterDict()
+                (lora_embedding_B): ParameterDict()
+                (lora_magnitude_vector): ModuleDict()
+              )
+            )
+            (mlp): LlamaMLP(
+              (gate_proj): lora.Linear(
+                (base_layer): Linear(in_features=3072, out_features=8192, bias=False)
+                (lora_dropout): ModuleDict(
+                  (default): Dropout(p=0.05, inplace=False)
+                )
+                (lora_A): ModuleDict(
+                  (default): Linear(in_features=3072, out_features=64, bias=False)
+                )
+                (lora_B): ModuleDict(
+                  (default): Linear(in_features=64, out_features=8192, bias=False)
+                )
+                (lora_embedding_A): ParameterDict()
+                (lora_embedding_B): ParameterDict()
+                (lora_magnitude_vector): ModuleDict()
+              )
+              (up_proj): lora.Linear(
+                (base_layer): Linear(in_features=3072, out_features=8192, bias=False)
+                (lora_dropout): ModuleDict(
+                  (default): Dropout(p=0.05, inplace=False)
+                )
+                (lora_A): ModuleDict(
+                  (default): Linear(in_features=3072, out_features=64, bias=False)
+                )
+                (lora_B): ModuleDict(
+                  (default): Linear(in_features=64, out_features=8192, bias=False)
+                )
+                (lora_embedding_A): ParameterDict()
+                (lora_embedding_B): ParameterDict()
+                (lora_magnitude_vector): ModuleDict()
+              )
+              (down_proj): lora.Linear(
+                (base_layer): Linear(in_features=8192, out_features=3072, bias=False)
+                (lora_dropout): ModuleDict(
+                  (default): Dropout(p=0.05, inplace=False)
+                )
+                (lora_A): ModuleDict(
+                  (default): Linear(in_features=8192, out_features=64, bias=False)
+                )
+                (lora_B): ModuleDict(
+                  (default): Linear(in_features=64, out_features=3072, bias=False)
+                )
+                (lora_embedding_A): ParameterDict()
+                (lora_embedding_B): ParameterDict()
+                (lora_magnitude_vector): ModuleDict()
+              )
+              (act_fn): SiLU()
+            )
+            (input_layernorm): LlamaRMSNorm((3072,), eps=1e-05)
+            (post_attention_layernorm): LlamaRMSNorm((3072,), eps=1e-05)
+          )
+        )
+        (norm): LlamaRMSNorm((3072,), eps=1e-05)
+        (rotary_emb): LlamaRotaryEmbedding()
+      )
+      (lm_head): Linear(in_features=3072, out_features=128256, bias=False)
+    )
+  )
+)

llama_3_2_3B_mntp_no_quant_ukr/training_args.bin

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