AL1-model-B

AL1-model-B — Qwen3-0.6B fine-tuned for native tool-calling

A compact (0.6B-parameter) language model fine-tuned to call tools reliably while staying a capable general-chat assistant. It takes the Apache-2.0 Qwen/Qwen3-0.6B base and applies a single LoRA supervised fine-tune (SFT) targeting native, Hermes-style <tool_call> emission over a small set of file/shell tools — then merges the adapter back into the base for a self-contained model.

Part of the Zynthetix AL-1 two-model lab (Model A = from-scratch NumPy LM; Model B = improve an open-source small model, this repo). Code: https://github.com/GruheshKurra/AL1-model-B

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Results — before → after

Tool-calling is scored by exact match against a held-out 12-case set: name_acc = correct tool name chosen, full_acc = correct name and arguments. Both evaluated with greedy decoding, fp32, on the same device (no blending with chat scores).

Why the base scored only 0.5: it refused to call run_bash and edit_file (0/2 each) and mis-handled one list_dir — its arguments were fine when it chose to call. The SFT data therefore over-weighted edit_file + run_bash + explicit anti-refusal examples, which closed the entire gap (name_acc → 1.0 = a valid tool is now always selected).

The merged fp16 model reproduces the adapter score exactly (full_acc 0.917) — merging lost nothing.

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The tools

Five file/shell tools, described to the model via the OpenAI-style tools= schema and the Qwen3 native chat template (Hermes <tool_call> tags):

The model emits tool name + arguments; wiring those to a real executor (or an MCP client) is a serving-layer concern, deliberately out of scope for the model itself.

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Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

MODEL = "karthik-2905/AL1-model-B"
tok = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.float16).to("mps")  # or "cuda"/"cpu"

TOOLS = [{"type": "function", "function": {
    "name": "read_file",
    "description": "Read and return the contents of a file.",
    "parameters": {"type": "object",
        "properties": {"path": {"type": "string"}}, "required": ["path"]}}}]  # + the other 4

msgs = [{"role": "user", "content": "Show me what's in config.py"}]
text = tok.apply_chat_template(msgs, tools=TOOLS, tokenize=False,
                              add_generation_prompt=True, enable_thinking=False)
ids = tok(text, return_tensors="pt").to(model.device)
out = model.generate(**ids, max_new_tokens=128, do_sample=False)
print(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True))
# -> <tool_call>{"name": "read_file", "arguments": {"path": "config.py"}}</tool_call>

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How it was built

LR sweep (10% data, 1 epoch) picked 3e-4 (eval_loss: 1e-4=1.099, 2e-4=0.999, 3e-4=0.975). The full 3-epoch run overfits by eval_loss after ~epoch 1 (0.91 → 0.99), but generation quality and tool exact-match were best at 3 epochs — the task metric beat the proxy loss, so 3 epochs shipped.

Data

• 1,423 train / 74 val examples, chat-formatted to the Qwen3 template, ~2:1 tool:chat ratio.
• Tool examples teach the Hermes <tool_call> format across all 5 tools (over-weighting the two the base refused); chat examples guard against chat regression.
• Train/val split is dedup'd and leakage-checked against the eval set.

Evaluation

• Tool track (trust anchor): exact-match over a 12-case set, scored by mlb/eval_tools.py.
• Chat track (regression guard): 10 probes, currently eyeballed for coherence (LLM-judge = future work).

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Limitations

• Small model (0.6B): capable within its size, not a substitute for a large instruct model.
• Chat quality is eyeballed, not yet LLM-judge scored — no formal regression number.
• No general-capability guard (MMLU/GSM8K) was run; broad-knowledge regression is unmeasured.
• 4-bit quantization untested — a 0.6B model can degrade more than the usual −1–2% at 4-bit.

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License

Apache-2.0, inherited from the Qwen/Qwen3-0.6B base.