danielcherubini
/

Qwen3.5-DeltaCoder-9B

@@ -1,210 +1,78 @@
 ---
 base_model: Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
-- lora
-- sft
-- transformers
-- trl
-- unsloth
 ---
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-### Framework versions
-- PEFT 0.18.1

 ---
+license: apache-2.0
 base_model: Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
 tags:
+  - qwen3.5
+  - code
+  - tool-calling
+  - lora
+  - sft
+  - unsloth
+datasets:
+  - togethercomputer/CoderForge-Preview
+language:
+  - en
+pipeline_tag: text-generation
 ---
+# Qwen3.5-DeltaCoder-9B
+A LoRA fine-tune of [Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2](https://huggingface.co/Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2) trained to improve structured tool-call generation (JSON formatting) for use in coding agents like OpenCode, Pi, and Cline.
 ## Training Details
+| Parameter | Value |
+|-----------|-------|
+| Base model | Qwen3.5-9B (hybrid GDN architecture) |
+| Method | LoRA (r=64, alpha=32) |
+| Dataset | CoderForge-Preview `filtered_reward1` (50K subset) |
+| Sequence length | 4096 |
+| Batch size | 2 (effective 16 with grad accum 8) |
+| Learning rate | 1e-4 (cosine schedule) |
+| Epochs | 1 |
+| Optimizer | AdamW |
+| Precision | BF16 |
+| Hardware | NVIDIA H200 140GB |
+| Training time | ~10 hours |
+| Framework | Unsloth 2026.3.10 + HuggingFace Transformers 5.3.0 |
+### LoRA Target Modules
+All major weight matrices are adapted:
+- **Full Attention** (8/32 layers): `q_proj`, `k_proj`, `v_proj`, `o_proj`
+- **Gated Delta Net** (24/32 layers): `in_proj_qkv`, `in_proj_z`, `in_proj_b`, `in_proj_a`, `out_proj`
+- **MLP** (all 32 layers): `gate_proj`, `up_proj`, `down_proj`
+### Training Loss
+Final training loss: ~0.94 (average: 1.268), decreasing steadily over training.
+## Usage
+### With PEFT
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2",
+    trust_remote_code=True,
+)
+model = PeftModel.from_pretrained(base_model, "danielcherubini/Qwen3.5-DeltaCoder-9B")
+tokenizer = AutoTokenizer.from_pretrained("danielcherubini/Qwen3.5-DeltaCoder-9B")
+```
+### GGUF (Ollama / llama.cpp / LM Studio)
+Pre-quantized GGUF files available at [danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF](https://huggingface.co/danielcherubini/Qwen3.5-DeltaCoder-9B-GGUF).
+## Intended Use
+This model is designed for AI coding agents that rely on structured tool calls (JSON function calling). It improves the base model's ability to generate well-formed tool-call responses in multi-turn agent trajectories.
+## Acknowledgements
+- [Unsloth](https://unsloth.ai) for Qwen3.5 training support
+- [Together AI](https://together.ai) for the CoderForge dataset
+- [Jackrong](https://huggingface.co/Jackrong) for the base model