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README.md

@@ -1,50 +1,20 @@
 ---
 library_name: peft
-license: llama2
-language:
-- en
-pipeline_tag: conversational
-tags:
-- legal
-datasets:
-- TuningAI/Startup_V1
 ---
-
-## Model Name: **Llama2_13B_startup_Assistant**
-
-## Description:
-Llama2_13B_startup_Assistant is a highly specialized language model fine-tuned from Meta's Llama2_13B. 
-
-It has been tailored to assist with inquiries related to Algerian tax law and Algerian startups, 
-offering valuable insights and guidance in these domains.
-
-## Training Data:
-This model was fine-tuned on a custom dataset meticulously curated with  more than 200 unique examples. 
-The dataset incorporates both manual entries and contributions from GPT3.5, GPT4, and Falcon 180B models.
-## Fine-tuning Techniques:
-Fine-tuning was performed using QLoRA (Quantized LoRA), an extension of LoRA that introduces quantization for enhanced parameter efficiency.
-The model benefits from 4-bit NormalFloat (NF4) quantization and Double Quantization techniques, ensuring optimized performance.
-
-## Use Cases:
-
-+ Providing guidance and information related to Algerian tax laws.
-+ Offering insights and advice on matters concerning Algerian startups.
-+ Facilitating discussions and answering questions on specific topics within these domains.
-
-## Performance:
-
-Llama2_13B_startup_Assistant exhibits improved performance and efficiency in addressing queries related to Algerian tax law and startups, 
-making it a valuable resource for individuals and businesses navigating these areas.
-
-## Limitations:
-
-* While highly specialized, this model may not cover every nuanced aspect of Algerian tax law or the startup ecosystem.
-* Accuracy may vary depending on the complexity and specificity of questions.
-* It may not provide legal advice, and users should seek professional consultation for critical legal matters.
-
 ## Training procedure
 
 
+The following `bitsandbytes` quantization config was used during training:
+- load_in_8bit: False
+- load_in_4bit: True
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: nf4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: float16
+
 The following `bitsandbytes` quantization config was used during training:
 - load_in_8bit: False
 - load_in_4bit: True
@@ -59,38 +29,4 @@ The following `bitsandbytes` quantization config was used during training:
 
 - PEFT 0.4.0
 
-### How to Get Started with the Model
-```
-! huggingface-cli login
-```
-
-```python
-from transformers import pipeline
-from transformers import AutoTokenizer
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForCausalLM , BitsAndBytesConfig
-import torch
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type="nf4",
-    bnb_4bit_compute_dtype=getattr(torch, "float16"),
-    bnb_4bit_use_double_quant=False)
-model = AutoModelForCausalLM.from_pretrained(
-        "meta-llama/Llama-2-13b-chat-hf",
-        quantization_config=bnb_config,
-        device_map={"": 0})
-model.config.use_cache = False
-model.config.pretraining_tp = 1
-model = PeftModel.from_pretrained(model, "TuningAI/Llama2_13B_startup_Assistant")
-
-tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-13b-chat-hf", trust_remote_code=True)
-tokenizer.pad_token = tokenizer.eos_token
-tokenizer.padding_side = "right"
-system_message = "Given a user's startup-related question in English, you will generate a thoughtful answer in English."
-while 1:
-  input_text = input(">>>")
-  prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n {input_text}. [/INST]"
-  pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=400)
-  result = pipe(prompt)
-  print(result[0]['generated_text'].replace(prompt, ''))
-```
+- PEFT 0.4.0

adapter_config.json

@@ -0,0 +1,21 @@
+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-2-13b-chat-hf",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 16,
+  "lora_dropout": 0.1,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 64,
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

@@ -0,0 +1,3 @@
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