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  1. .gitattributes +1 -0
  2. app.py +105 -0
  3. lora_model_lto/README.md +210 -0
  4. lora_model_lto/adapter_config.json +50 -0
  5. lora_model_lto/adapter_model.safetensors +3 -0
  6. lora_model_lto/special_tokens_map.json +23 -0
  7. lora_model_lto/tokenizer.json +3 -0
  8. lora_model_lto/tokenizer_config.json +2070 -0
  9. requirements.txt +7 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ lora_model_lto/tokenizer.json filter=lfs diff=lfs merge=lfs -text
app.py ADDED
@@ -0,0 +1,105 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import gradio as gr
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ from peft import PeftModel
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+ import os
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+
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+ # Load model (will load from HF Hub or local)
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+ print("Loading LTO model...")
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+
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+ # Base model
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+ base_model_name = "unsloth/Llama-3.2-3B-bnb-4bit"
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+ tokenizer = AutoTokenizer.from_pretrained(base_model_name)
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+
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+ # Try to load with LoRA adapter
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+ try:
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+ from unsloth import FastLanguageModel
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+ model, tokenizer = FastLanguageModel.from_pretrained(
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+ model_name="./lora_model_lto", # Local path in Space
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+ max_seq_length=512,
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+ dtype=None,
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+ load_in_4bit=True,
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+ )
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+ FastLanguageModel.for_inference(model)
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+ print("Loaded with Unsloth!")
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+ except:
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+ print("Loading with transformers...")
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+ model = AutoModelForCausalLM.from_pretrained(
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+ base_model_name,
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+ torch_dtype=torch.float16,
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+ device_map="auto",
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+ load_in_4bit=True,
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+ )
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+ model = PeftModel.from_pretrained(model, "./lora_model_lto")
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+
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+ print("Model loaded!")
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+
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+ # Chat template
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+ SYSTEM_PROMPT = """You are LTO, a French member of CS City Discord. You do technical analysis on stocks and crypto (fundas are trash). You're aggressive in banter and use phrases like "on my wife", "kys", "die", "bozo", "dubai scammer", "fr", "ngl", "bcs". Keep it real and match the energy."""
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+
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+ def format_prompt(message, history):
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+ # Build conversation context
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+ context_parts = []
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+ for user_msg, bot_msg in history[-3:]: # Last 3 exchanges
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+ context_parts.append(f"[earlier] User: {user_msg}")
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+ context_parts.append(f"[earlier] LTO: {bot_msg}")
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+
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+ if context_parts:
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+ full_input = "\n".join(context_parts) + f"\nUser: {message}"
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+ else:
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+ full_input = f"User: {message}"
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+
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+ prompt = f"""<|system|>
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+ {SYSTEM_PROMPT}
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+ <|user|>
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+ {full_input}
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+ <|assistant|>
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+ """
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+ return prompt
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+
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+ def respond(message, history):
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+ prompt = format_prompt(message, history)
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+
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+
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+ with torch.no_grad():
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+ outputs = model.generate(
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+ **inputs,
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+ max_new_tokens=150,
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+ temperature=0.75,
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+ top_p=0.9,
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+ do_sample=True,
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+ repetition_penalty=1.15,
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+ pad_token_id=tokenizer.eos_token_id,
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+ )
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+
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+ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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+
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+ # Extract assistant response
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+ if "<|assistant|>" in response:
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+ response = response.split("<|assistant|>")[-1].strip()
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+
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+ # Clean up
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+ response = response.replace("<|system|>", "").replace("<|user|>", "").strip()
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+ if "\n" in response:
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+ response = response.split("\n")[0].strip()
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+
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+ return response
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+
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+ # Create Gradio interface
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+ demo = gr.ChatInterface(
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+ respond,
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+ title="🇫🇷 Chat with LTO",
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+ description="LTO from CS City Discord. He does TA, hates fundas, and says 'on my wife' a lot. Be ready for aggressive banter!",
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+ examples=[
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+ "hey",
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+ "what do you think of fundas?",
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+ "cap",
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+ "you're lying",
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+ "what crypto should I buy?",
100
+ ],
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+ theme=gr.themes.Soft(),
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+ )
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+
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+ if __name__ == "__main__":
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+ demo.launch()
lora_model_lto/README.md ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: unsloth/llama-3.2-3b-bnb-4bit
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+ library_name: peft
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+ pipeline_tag: text-generation
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+ tags:
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+ - base_model:adapter:unsloth/llama-3.2-3b-bnb-4bit
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+ - lora
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+ - sft
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+ - transformers
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+ - trl
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+ - unsloth
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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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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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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+ - **Developed by:** [More Information Needed]
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+ - **Funded by [optional]:** [More Information Needed]
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+ - **Shared by [optional]:** [More Information Needed]
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+ - **Model type:** [More Information Needed]
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+ - **Language(s) (NLP):** [More Information Needed]
33
+ - **License:** [More Information Needed]
34
+ - **Finetuned from model [optional]:** [More Information Needed]
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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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+ - **Repository:** [More Information Needed]
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+ - **Paper [optional]:** [More Information Needed]
42
+ - **Demo [optional]:** [More Information Needed]
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+
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+ ## Uses
45
+
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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. -->
47
+
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+ ### Direct Use
49
+
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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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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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+ <!-- 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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+
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+ [More Information Needed]
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+
60
+ ### Out-of-Scope Use
61
+
62
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
72
+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
82
+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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+ <!-- 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. -->
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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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+ <!-- 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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+
100
+
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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 -->
104
+
105
+ #### Speeds, Sizes, Times [optional]
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+
107
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+
109
+ [More Information Needed]
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+
111
+ ## Evaluation
112
+
113
+ <!-- This section describes the evaluation protocols and provides the results. -->
114
+
115
+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
118
+
119
+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
123
+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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+
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+ 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).
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+
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+ - **Hardware Type:** [More Information Needed]
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+ - **Hours used:** [More Information Needed]
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+ - **Cloud Provider:** [More Information Needed]
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+ - **Compute Region:** [More Information Needed]
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+ - **Carbon Emitted:** [More Information Needed]
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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
180
+
181
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
182
+
183
+ **BibTeX:**
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+
185
+ [More Information Needed]
186
+
187
+ **APA:**
188
+
189
+ [More Information Needed]
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+
191
+ ## Glossary [optional]
192
+
193
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
194
+
195
+ [More Information Needed]
196
+
197
+ ## More Information [optional]
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+
199
+ [More Information Needed]
200
+
201
+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
205
+ ## Model Card Contact
206
+
207
+ [More Information Needed]
208
+ ### Framework versions
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+
210
+ - PEFT 0.18.1
lora_model_lto/adapter_config.json ADDED
@@ -0,0 +1,50 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "alora_invocation_tokens": null,
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+ "alpha_pattern": {},
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+ "arrow_config": null,
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+ "auto_mapping": {
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+ "base_model_class": "LlamaForCausalLM",
7
+ "parent_library": "transformers.models.llama.modeling_llama",
8
+ "unsloth_fixed": true
9
+ },
10
+ "base_model_name_or_path": "unsloth/llama-3.2-3b-bnb-4bit",
11
+ "bias": "none",
12
+ "corda_config": null,
13
+ "ensure_weight_tying": false,
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+ "eva_config": null,
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+ "exclude_modules": null,
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+ "fan_in_fan_out": false,
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+ "inference_mode": true,
18
+ "init_lora_weights": true,
19
+ "layer_replication": null,
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+ "layers_pattern": null,
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+ "layers_to_transform": null,
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+ "loftq_config": {},
23
+ "lora_alpha": 16,
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+ "lora_bias": false,
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+ "lora_dropout": 0,
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+ "megatron_config": null,
27
+ "megatron_core": "megatron.core",
28
+ "modules_to_save": null,
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+ "peft_type": "LORA",
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+ "peft_version": "0.18.1",
31
+ "qalora_group_size": 16,
32
+ "r": 16,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "q_proj",
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+ "up_proj",
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+ "k_proj",
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+ "down_proj",
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+ "o_proj",
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+ "v_proj",
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+ "gate_proj"
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+ ],
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+ "target_parameters": null,
45
+ "task_type": "CAUSAL_LM",
46
+ "trainable_token_indices": null,
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+ "use_dora": false,
48
+ "use_qalora": false,
49
+ "use_rslora": false
50
+ }
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lora_model_lto/special_tokens_map.json ADDED
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+ "bos_token": {
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ }
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+ }
lora_model_lto/tokenizer.json ADDED
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lora_model_lto/tokenizer_config.json ADDED
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2069
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requirements.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ torch
2
+ transformers>=4.40.0
3
+ accelerate
4
+ bitsandbytes
5
+ peft
6
+ gradio
7
+ unsloth