Update README.md

README.md

@@ -1,21 +1,170 @@
 ---
-base_model: armand0e/Qwen3.5-9B-Agent
+base_model: Qwen/Qwen3.5-9B
 tags:
-- text-generation-inference
 - transformers
+- text-generation-inference
+- coding agent
+- agent
+- code
+- tools
 - unsloth
 - qwen3_5
 license: apache-2.0
 language:
 - en
+datasets:
+- TeichAI/claude-4.5-opus-high-reasoning-250x
+- armand0e/badlogicgames-pi-mono-opus-filtered
+- armand0e/kimi-k2.6-claude-code-traces
+- TeichAI/Claude-Opus-4.6-Reasoning-887x
+- armand0e/minimax-m3-claude-code-traces
+- armand0e/claude-opus-4.8-pi-traces
+---
+
+# Qwen3.5 9B Coder
+
+This is a experimental finetune on a mix of many traces from many different models. Reasoning was left untouched.
+
+Total train time: ~4 hours
+
+## Training Script
+
+<details>
+  <summary>Training Script</summary>
+  
+```py
+import os
+from unsloth import FastModel
+import torch
+from trl import SFTConfig, SFTTrainer
+from teich import mask_data, prepare_data
+
+MAX_SEQ_LEN = 32768
+MODEL_NAME = "Qwen/Qwen3.5-9B"
+OUTPUT_DIR = "/content/drive/MyDrive/Colab/outputs-qwen-tool-sft"
+HUB_REPO_ID = "armand0e/Qwen3.5-9B-Coder"
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
+CHAT_TEMPLATE_PATH = "qwen3.5-chat-template.jinja"
+
+model, tokenizer = FastModel.from_pretrained(
+    model_name=MODEL_NAME,
+    max_seq_length=MAX_SEQ_LEN,
+    load_in_4bit=False,
+    load_in_8bit=False,
+    full_finetuning=False,
+    token=HF_TOKEN,
+)
+
+if CHAT_TEMPLATE_PATH:
+    with open(CHAT_TEMPLATE_PATH, "r", encoding="utf-8") as f:
+        custom_chat_template = f.read()
+    tokenizer.chat_template = custom_chat_template
+    if hasattr(tokenizer, "tokenizer") and tokenizer.tokenizer is not None:
+        tokenizer.tokenizer.chat_template = custom_chat_template
+
+model = FastModel.get_peft_model(
+    model,
+    finetune_vision_layers     = False, # Turn off for just text!
+    finetune_language_layers   = True,  # Should leave on!
+    finetune_attention_modules = True,  # Attention good for GRPO
+    finetune_mlp_modules       = True,  # Should leave on always!
+
+    r = 32,           # Larger = higher accuracy, but might overfit
+    lora_alpha = 32,  # Recommended alpha == r at least
+    lora_dropout = 0,
+    bias = "none",
+    random_state = 3407,
+)
+
+train_dataset = prepare_data(
+    {
+        "qwen3.7-max": {
+            "source": "armand0e/qwen3.7-max", # stupid typo i made and now this model wasn't trained on the qwen3.7-max traces :(
+        },
+        "chat": {
+            "source": "TeichAI/claude-4.5-opus-high-reasoning-250x",
+        },
+        "opus-pi-agent": {
+            "source": "armand0e/badlogicgames-pi-mono-opus-filtered",
+        },
+        "kimi-k2.6-claude-code": {
+            "source": "armand0e/kimi-k2.6-claude-code-traces",
+        },
+        "chat-2": {
+            "source": "TeichAI/Claude-Opus-4.6-Reasoning-887x"
+        },
+        "minimax-m3-claude-code": {
+            "source": "armand0e/minimax-m3-claude-code-traces"
+        },
+        "more-opus": {
+            "source": "armand0e/claude-opus-4.8-pi-traces"
+        }
+    },
+    tokenizer,
+    split="train",
+    hf_token=HF_TOKEN,
+    chat_template_kwargs={"enable_thinking": False, "preserve_thinking": True},
+    max_length=MAX_SEQ_LEN,
+    oversized_policy="trim_followups",
+    tokenize=True,
+    strict=True,
+)
+
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    train_dataset=train_dataset,
+    eval_dataset=None,
+    args=SFTConfig(
+        dataset_text_field="text",
+        dataset_num_proc=1,
+        max_length=MAX_SEQ_LEN,
+        packing=False,
+        per_device_train_batch_size=1,
+        gradient_accumulation_steps=8,
+        warmup_steps= 5,
+        num_train_epochs=1,
+        learning_rate=2e-4,
+        logging_steps=1,
+        save_strategy="epoch",
+        save_total_limit=3,
+        optim="adamw_8bit",
+        weight_decay=0.01,
+        #max_grad_norm=0.3,
+        lr_scheduler_type="linear",
+        output_dir=OUTPUT_DIR,
+        seed=3407,
+        report_to="none",
+    ),
+)
+
+trainer = mask_data(
+    trainer,
+    tokenizer=tokenizer,
+    train_on_reasoning=False,
+    train_on_final_answers=True,
+    train_on_tools=True,
+)
+
+print(trainer.train_dataset.preview())
+
+trainer_stats = trainer.train(resume_from_checkpoint=False)
+
+model.push_to_hub(f"{HUB_REPO_ID}-LoRA", token=HF_TOKEN)
+tokenizer.push_to_hub(f"{HUB_REPO_ID}-LoRA", token=HF_TOKEN)
+
+model.push_to_hub_merged(HUB_REPO_ID, tokenizer, save_method="merged_16bit", token=HF_TOKEN)
+```
+</details>
+
 ---
 
-# Uploaded finetuned  model
+The data for this model was easily formatted and masked with [Teich](https://github.com/TeichAI/teich) 
 
 - **Developed by:** armand0e
 - **License:** apache-2.0
-- **Finetuned from model :** armand0e/Qwen3.5-9B-Agent
+- **Finetuned from model :** Qwen/Qwen3.5-9B
 
 This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)