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Model: FazeFlynn/mistral-7b-llm-architecture-expert
Source: Original Platform

README.md

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+---
+language: en
+license: apache-2.0
+base_model: mistralai/Mistral-7B-Instruct-v0.3
+library_name: transformers
+tags:
+  - fine-tuned
+  - qlora
+  - lora
+  - llm
+  - instruction-tuning
+  - peft
+---
+
+# Mistral-7B LLM Architecture Expert
+
+A fine-tuned version of Mistral-7B-Instruct-v0.3 trained using QLoRA on a custom dataset focused on LLM architecture concepts and internals.
+
+Topics covered include:
+
+- Attention mechanisms
+- Transformers
+- Training dynamics
+- Scaling laws
+- KV cache
+- Tokenization
+- Fine-tuning methods
+- LLM evaluation
+
+## Training Details
+
+| Parameter | Value |
+|-----------|-------|
+| Base model | mistralai/Mistral-7B-Instruct-v0.3 |
+| Method | QLoRA (NF4 4-bit + LoRA) |
+| Dataset | 500 custom instruction examples |
+| Domain | LLM Architecture |
+| LoRA Rank | 64 |
+| Trainable Parameters | 2.26% |
+| Optimizer | Paged AdamW |
+| Learning Rate Schedule | Cosine + 3% warmup |
+| Final Training Loss | 1.2629 |
+| Training Time | ~3.3 minutes |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "FazeFlynn/mistral-7b-llm-architecture-expert"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16
+)
+
+prompt = "[INST] Explain how KV cache works in transformers [/INST]"
+
+inputs = tokenizer(prompt, return_tensors="pt")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=200
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```