Upload 8 files

README.md

@@ -1,61 +1,204 @@
-# Model Card: Model ID
+---
+library_name: peft
+base_model: mistralai/Mistral-7B-Instruct-v0.2
+---
 
-## License
+# Model Card for Model ID
 
-MIT License
+<!-- Provide a quick summary of what the model is/does. -->
 
-## Languages Supported
 
-- English (en)
 
----
+## Model Details
 
-## Overview
+### Model Description
 
-This model is part of the VCC project and has been fine-tuned on the TESTtm7873/ChatCat dataset using the `mistralai/Mistral-7B-Instruct-v0.2` as the base model. The fine-tuning process utilized QLoRA for improved performance.
+<!-- Provide a longer summary of what this model is. -->
 
----
 
-## Getting Started
-
-To use this model, you'll need to set up your environment first:
-
-## Model initialization
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
-from peft import PeftModel
-tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
-model = AutoModelForCausalLM.from_pretrained(
-    "mistralai/Mistral-7B-Instruct-v0.2",
-    load_in_8bit=True,
-    device_map="auto",
-)
-model = PeftModel.from_pretrained(model, "TESTtm7873/MistralCat-1v/MistralCat-1v")
-model.eval()
-```
-
-## Inference
-```python
-def evaluate(question: str) -> str:
-    prompt = f"The conversation between human and Virtual Cat Companion.\n[|Human|] {question}.\n[|AI|] "
-    inputs = tokenizer(prompt, return_tensors="pt")
-    input_ids = inputs["input_ids"].cuda()
-    generation_output = model.generate(
-        input_ids=input_ids,
-        generation_config=generation_config,
-        return_dict_in_generate=True,
-        output_scores=True,
-        max_new_tokens=256
-    )
-    output = tokenizer.decode(generation_output.sequences[0]).split("[|AI|]")[1]
-    return output
-your_question: str = "You have the softest fur."
-print(evaluate(your_question))
-```
-
-
-- **Developed by:** testtm
-- **Funded by:** testtm
-- **Model type:** Mistral
-- **Language:** English
-- **Finetuned from model:** mistralai/Mistral-7B-Instruct-v0.2
+
+- **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 0.9.1.dev0

adapter_config.json

@@ -0,0 +1,34 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "k_proj",
+    "down_proj",
+    "v_proj",
+    "o_proj",
+    "lm_head",
+    "gate_proj",
+    "up_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34cd63704fbe8213790347d35b301577305fb63503c9ef320a1c7701c18a2946
+size 864513616

optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9dfb3c6b07d8345a84b709b20ef39e1c8f6f63ef3624a3c7b4dc3c27f98a713
+size 170955230

rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:772eedd658ea8948df61ea46d64d7bc30273870b0a0abf230bb541b9b7d8846a
+size 14244

scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:489080dbe6e68b07b7d6ebf4e85012047b5378ffaa2895f331193af2731c50a3
+size 1064

trainer_state.json

@@ -0,0 +1,111 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.974025974025974,
+  "eval_steps": 25,
+  "global_step": 150,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.16,
+      "grad_norm": 11.01017951965332,
+      "learning_rate": 2.2361809045226133e-05,
+      "loss": 2.5416,
+      "step": 25
+    },
+    {
+      "epoch": 0.16,
+      "eval_loss": 1.7125414609909058,
+      "eval_runtime": 5.0817,
+      "eval_samples_per_second": 6.887,
+      "eval_steps_per_second": 0.984,
+      "step": 25
+    },
+    {
+      "epoch": 0.32,
+      "grad_norm": 10.009162902832031,
+      "learning_rate": 1.9221105527638193e-05,
+      "loss": 1.6162,
+      "step": 50
+    },
+    {
+      "epoch": 0.32,
+      "eval_loss": 1.5332300662994385,
+      "eval_runtime": 5.2881,
+      "eval_samples_per_second": 6.619,
+      "eval_steps_per_second": 0.946,
+      "step": 50
+    },
+    {
+      "epoch": 0.49,
+      "grad_norm": 9.377760887145996,
+      "learning_rate": 1.608040201005025e-05,
+      "loss": 1.4794,
+      "step": 75
+    },
+    {
+      "epoch": 0.49,
+      "eval_loss": 1.431817889213562,
+      "eval_runtime": 5.1946,
+      "eval_samples_per_second": 6.738,
+      "eval_steps_per_second": 0.963,
+      "step": 75
+    },
+    {
+      "epoch": 0.65,
+      "grad_norm": 14.868287086486816,
+      "learning_rate": 1.2939698492462313e-05,
+      "loss": 1.3783,
+      "step": 100
+    },
+    {
+      "epoch": 0.65,
+      "eval_loss": 1.3400715589523315,
+      "eval_runtime": 5.2323,
+      "eval_samples_per_second": 6.689,
+      "eval_steps_per_second": 0.956,
+      "step": 100
+    },
+    {
+      "epoch": 0.81,
+      "grad_norm": 11.457832336425781,
+      "learning_rate": 9.798994974874372e-06,
+      "loss": 1.3071,
+      "step": 125
+    },
+    {
+      "epoch": 0.81,
+      "eval_loss": 1.2986036539077759,
+      "eval_runtime": 5.2307,
+      "eval_samples_per_second": 6.691,
+      "eval_steps_per_second": 0.956,
+      "step": 125
+    },
+    {
+      "epoch": 0.97,
+      "grad_norm": 9.498678207397461,
+      "learning_rate": 6.658291457286432e-06,
+      "loss": 1.2695,
+      "step": 150
+    },
+    {
+      "epoch": 0.97,
+      "eval_loss": 1.272000789642334,
+      "eval_runtime": 5.2257,
+      "eval_samples_per_second": 6.698,
+      "eval_steps_per_second": 0.957,
+      "step": 150
+    }
+  ],
+  "logging_steps": 25,
+  "max_steps": 200,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 2,
+  "save_steps": 25,
+  "total_flos": 1036180979712000.0,
+  "train_batch_size": 2,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f3570b6bcd75899de732bfe539d8b7f8c5ac3c04662435e8d448cf08d2b5759
+size 4856