Upload QLoRA fine-tuned Mistral-7B model

README.md

@@ -1,97 +1,207 @@
 ---
+base_model: mistralai/Mistral-7B-Instruct-v0.2
+library_name: peft
+pipeline_tag: text-generation
 tags:
-- text-generation
+- base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2
+- lora
 - transformers
-- peft
-- qlora
-- bitsandbytes
-- mistral
-- mistral-7b
-- fine-tune
-license: apache-2.0
 ---
 
-# my-qlora-mistral7b-instruct
-
-This is a **QLoRA fine-tuned** version of the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model.
-It was fine-tuned using **Low-Rank Adaptation (LoRA)** in 4-bit precision for efficiency on consumer GPUs.
-
-## 🚀 Model Details
-- **Base model**: mistralai/Mistral-7B-Instruct-v0.2
-- **Fine-tuning method**: QLoRA with PEFT
-- **Quantization**: 4-bit (bitsandbytes)
-- **Task**: Instruction following / conversational AI
-- **Dataset**: Custom instruction-response pairs
-- **Training environment**: Google Colab Pro (T4 / A100 GPU)
-
-## 📦 How to Use
-```python
-# First, make sure you have the necessary libraries installed:
-# pip install transformers peft bitsandbytes accelerate
-
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, BitsAndBytesConfig
-from peft import PeftModel
-from accelerate import infer_auto_device_map, dispatch_model
-
-fine_tuned_model_id = "Falah/my-qlora-mistral7b-instruct"
-base_model_id = "mistralai/Mistral-7B-Instruct-v0.2"
-
-print("Loading tokenizer...")
-tokenizer = AutoTokenizer.from_pretrained(fine_tuned_model_id)
-
-print("Loading base model with quantization...")
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=torch.float16
-)
-base_model = AutoModelForCausalLM.from_pretrained(
-    base_model_id,
-    quantization_config=bnb_config,
-    device_map=None, # Load to CPU initially
-    torch_dtype=torch.float16,
-    trust_remote_code=True,
-)
-
-print("Loading PEFT adapter onto the base model...")
-model = PeftModel.from_pretrained(base_model, fine_tuned_model_id)
-
-print("Dispatching model to devices...")
-device_map = infer_auto_device_map(model, dtype=torch.float16)
-model = dispatch_model(model, device_map=device_map)
-
-# Ensure the model is in evaluation mode
-model.eval()
-
-print("Creating text generation pipeline...")
-generator = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer,
-    torch_dtype=torch.float16,
-    device_map="auto",
-)
-
-# Define a sample user prompt
-user_prompt = "Write a short story about a robot learning to love."
-
-# Format the prompt
-formatted_prompt = f"[INST] {user_prompt} [/INST]"
-
-# Generate text
-outputs = generator(
-    formatted_prompt,
-    max_new_tokens=200,
-    num_return_sequences=1,
-    do_sample=True,
-    temperature=0.7,
-    top_k=50,
-    top_p=0.95,
-)
-
-# Print the generated text
-for i, output in enumerate(outputs):
-    print(f"Generated Output {i+1}:\n{output['generated_text']}")
-```
+# 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]
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+- **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 -->
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+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+[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. -->
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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. -->
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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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+#### Metrics
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+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
+### Results
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+
+#### Summary
+
+
+
+## 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
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+[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:**
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+**APA:**
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+
+## Glossary [optional]
+
+<!-- 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]
+
+## More Information [optional]
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+[More Information Needed]
+
+## Model Card Authors [optional]
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+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.0

adapter_config.json

@@ -1,7 +1,7 @@
 {
   "alpha_pattern": {},
   "auto_mapping": null,
-  "base_model_name_or_path": null,
+  "base_model_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
   "bias": "none",
   "corda_config": null,
   "eva_config": null,

adapter_model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 27282328
+oid sha256:0880c8ed215895ee6f0c0e683f1c69aa86c485105ef0d7b2866b4332c4b4696d
+size 27280152

tokenizer.json

@@ -1,7 +1,21 @@
 {
   "version": "1.0",
-  "truncation": null,
-  "padding": null,
+  "truncation": {
+    "direction": "Right",
+    "max_length": 512,
+    "strategy": "LongestFirst",
+    "stride": 0
+  },
+  "padding": {
+    "strategy": {
+      "Fixed": 512
+    },
+    "direction": "Left",
+    "pad_to_multiple_of": null,
+    "pad_id": 2,
+    "pad_type_id": 0,
+    "pad_token": "</s>"
+  },
   "added_tokens": [
     {
       "id": 0,