Update README.md

README.md

@@ -1,24 +1,78 @@
 ---
-base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
+base_model: meta-llama/Llama-3.3-70B-Instruct-Reference
 library_name: peft
-license: apache-2.0
----
-
+license: other
 tags:
-- quantum-computing
-- openqasm
-- compiler
-- lora
-- auto-scientist
-- adaption-labs
+  - lora
+  - peft
+  - adapter
+  - adaption
+pipeline_tag: text-generation
 datasets:
 - jay2219/quantum-circuit-routing
-pipeline_tag: text-generation
 ---
 
 # ⚛️ Q-Route-70B: Quantum Hardware Compiler Model Card
 
 **Q-Route-70B** is a domain-adapted frontier AI compiler model fine-tuned on `Llama-3.3-70B-Instruct` via the **Adaption Labs AutoScientist** platform. It compiles abstract OpenQASM 2.0 quantum circuits to strictly adhere to physical target QPU connectivity graphs (Line, Ring, Star, Grid, and IBM Heavy-Hex hardware topologies).
+---
+
+## Model Training
+
+A LORA adapter for `meta-llama/Llama-3.3-70B-Instruct-Reference`. This model was trained with SFT using [Adaption](https://adaptionlabs.ai)'s AutoScientist on the quantum_circuit_routing dataset.
+
+
+![Training metrics](training-metrics.png)
+
+### AutoScientist Config
+
+```json
+{
+  "job_id": "72858b84-6e51-44fa-b887-56fd54517a8a",
+  "training_experiment_id": "dcc1dae2-3122-4888-99a8-3c296afd1020",
+  "original_model_name": "meta-llama/Llama-3.3-70B-Instruct-Reference",
+  "trained_model_name": "adaption_quantum_circuit_routing",
+  "training_method": "sft",
+  "training_type": "lora",
+  "data_format": "chat",
+  "hyperparams": {
+    "lora": "true",
+    "lora_r": 64,
+    "n_evals": 5,
+    "n_epochs": 2,
+    "batch_size": "max",
+    "lora_alpha": 128,
+    "lora_dropout": 0,
+    "min_lr_ratio": 0.1,
+    "warmup_ratio": 0.05,
+    "weight_decay": 0.05,
+    "learning_rate": 0.0001,
+    "max_grad_norm": 1,
+    "base_model_size": "70B",
+    "train_on_inputs": "false",
+    "training_method": "sft",
+    "lr_scheduler_type": "cosine",
+    "scheduler_num_cycles": 0.5,
+    "lora_trainable_modules": "all-linear"
+  }
+}
+```
+
+## Training Data
+
+The model was trained on 14,583 rows of adapted data with the following domain distribution: code (79%), science (21%), technology (0%).
+
+## Model Evaluation
+
+The model was evaluated on an in-distribution held-out test set as well as a broader domain-specific test set to measure generalization.
+
+
+![Win rates](win-rates.png)
+
+| Domain | Win rate vs. base model |
+| --- | --- |
+| code | 50% |
+
 
 ---
 
@@ -26,9 +80,8 @@ pipeline_tag: text-generation
 
 - **Model Name:** `Q-Route-70B`
 - **Model Type:** Fine-Tuned PEFT/LoRA Causal Language Model
-- **Base Model:** `togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference` (70B parameters)
+- **Base Model:** `meta-llama/Llama-3.3-70B-Instruct-Reference` (70B parameters)
 - **Language/Domain:** OpenQASM 2.0 Spatial Graph Compiler Code
-- **License:** Apache 2.0
 
 ---
 
@@ -54,7 +107,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel
 
-base_model_id = "togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference"
+base_model_id = "meta-llama/Meta-Llama-3.3-70B-Instruct-Reference"
 adapter_id = "jay2219/Q-Route-70B"
 
 tokenizer = AutoTokenizer.from_pretrained(base_model_id)
@@ -79,8 +132,8 @@ creg c[15];
 cx q[1], q[5];
 measure q -> c;
 ```
-### Compiled Hardware-Compliant OpenQASM 2.0:"""
-
+### Compiled Hardware-Compliant OpenQASM 2.0:
+```
 prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
 inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
 
@@ -99,5 +152,5 @@ print(compiled_qasm)
 - **LoRA Alpha ($\alpha$):** 128
 - **Dropout:** 0.0
 - **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
-- **Training Dataset:** 15,000 Qiskit ground-truth pairs across 5 hardware topology families.
+- **Training Dataset:** ~15,000 Qiskit ground-truth pairs across 5 hardware topology families.
 - **Platform:** Adaption Labs AutoScientist platform.