sygnif-lora-v2

LoRA adapter trained on Qwen/Qwen3.5-9B for tool-calling in Hermes <tool_call>{...}</tool_call> format, with a small voice-replay slice from a SYGNIF crypto trading agent's channeler corpus.

The adapter teaches structured function-calling grammar; specific tool names are provided at inference time via the system prompt's <tools>[…]</tools> block, not learned. This is the standard Hermes-style FC convention.

Training summary

Field	Value
Base model	Qwen/Qwen3.5-9B (Apache 2.0, ungated)
Method	QLoRA (4-bit NF4 base, double-quant) + LoRA r=16
Trainable params	29,097,984 / 8,982,901,248 = 0.32 %
Target modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Effective batch	16 (per-device 1 × grad-accum 16)
Max seq	1536 tokens
Learning rate	2e-4, cosine schedule
Epochs	2
Steps	498
Hardware	1× RTX 4090 24 GB
Wall time	5 h 40 min

Final metrics

Metric	Start (step 10)	End (step 490)	Best
Train loss	1.0879	0.3600	0.3276 (step 360)
Mean token accuracy	75.85 %	89.51 %	90.01 % (step 360)

Slight uptick at the very end (loss 0.33 → 0.36 over the last 80 steps) is LR-schedule-tail noise; the model is at convergence by step ~360.

Training corpus (3,969 rows, ChatML)

Slice	Rows	Source
Single-turn FC	~2,000	lockon/xlam-function-calling-60k (CC-BY-4.0 mirror of gated Salesforce/xlam-function-calling-60k)
Multi-turn FC + tool role	~1,500	NousResearch/hermes-function-calling-v1 (Apache 2.0)
Voice replay	472	SYGNIF channeler corpus (private — your data)

All rows normalized to ChatML messages format with <tool_call>{...}</tool_call> in assistant turns and <tool_response>{...}</tool_response> in tool turns.

Files

File	Purpose
adapter_model.safetensors	PEFT LoRA weights, 56 MB
adapter_config.json	PEFT config (r, alpha, target_modules, etc.)
adapter_metadata.json	Reproducibility sidecar — full hyperparams + train timestamp
chat_template.jinja	Qwen 3.5 chat template (incl. tool role)
tokenizer.json + tokenizer_config.json	Tokenizer files
sygnif-lora-v2.gguf	Same adapter in llama.cpp GGUF format, 56 MB — for llama-server --lora

Usage

transformers + peft

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base, "gianson/sygnif-lora-v2")

messages = [
    {"role": "system", "content": "You are a tool-using assistant. Use tools when asked for live data.\n\n<tools>\n[...your tool schemas...]\n</tools>"},
    {"role": "user",   "content": "What's BTC's current price?"},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
out = model.generate(inputs, max_new_tokens=300)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=False))
# → <tool_call>{"name":"btc_ticker","arguments":{"symbol":"BTCUSDT"}}</tool_call>

llama.cpp / llama-server

llama-server \
  --model /path/to/Qwen3.5-9B-Q4_K_M.gguf \
  --lora  /path/to/sygnif-lora-v2.gguf \
  --jinja --ctx-size 4096 --port 8080 --n-gpu-layers 99

Then hit /v1/chat/completions (OpenAI-compatible) — the model emits tool_calls=[{...}] per the Hermes convention.

Verified behavior

• Emits structured tool_calls arrays, not free-form text or hallucinated tool names.
• Skips tool-calling on conversational queries ("evaluate your skills") — uses the negatives in xlam to hold the line.
• Reasoning trace via Qwen 3.5 thinking-mode in reasoning_content field.
• Final response throughput ~113 tok/s on RTX 4090, ~3 tok/s on Intel CPU (Q4 base).

Known limitations

• No prior on specific tool names. The training corpus uses generic FC tool names; for your specific tools (e.g. btc.ticker, chain.balance), provide them in the system prompt's <tools>[…]</tools> block. The model dispatches what's there.
• Trained at 4-bit base (NF4 + double-quant) — bf16 retraining at r=32 would likely add ~1–2 pp accuracy. Skipped here because no 48 GB GPU was in stock at training time.

License

Apache 2.0 — same as the base model. Trained on:

• xlam-function-calling-60k: CC-BY-4.0 (attribution to Salesforce/lockon)
• hermes-function-calling-v1: Apache 2.0 (NousResearch)
• channeler replay slice: original data (the model author)

No restrictions on commercial use, redistribution, or further fine-tuning.

Citation

If you use this adapter:

@misc{sygnif-lora-v2,
  author = {Gianson},
  title = {sygnif-lora-v2: Hermes-format function-calling LoRA on Qwen 3.5 9B},
  year  = {2026},
  url   = {https://huggingface.co/gianson/sygnif-lora-v2}
}