language:

• en
• th tags:
• qwen3.5
• qwen3_5
• gdn
• linear-attention
• hybrid
• sft
• lora
• fine-tuned
• sme
• tool-calling
• agent
• business
• multimodal license: apache-2.0 base_model: hotdogs/Qwen3.8-27B-abliterated datasets:
• hotdogs/sme-sft-docdata-qwen38 pipeline_tag: text-generation

Qwen3.8-27B-Abliterated-SME-Preview

An SME (small & medium enterprise) business assistant built on top of the Qwen3.8-27B abliterated (λ=1.2) hybrid reasoning model — fine-tuned with LoRA on a curated document-analysis + tool-calling dataset. It is designed to reason over CSV/office documents, call tools correctly, and answer SME business questions, while preserving the base model's general capability.

• Base: hotdogs/Qwen3.8-27B-abliterated (λ=1.2, Qwen3_5ForConditionalGeneration)
• Method: LoRA SFT (r=32, α=64, BF16), 2 epochs
• Training data: hotdogs/sme-sft-docdata-qwen38 (8,827 rows, 30% tool-call, 100% think-tagged, 0% duplicates)
• Format: full BF16 merged safetensors (MTP head preserved), multimodal wrapper

⚡ Quick Results (A/B vs base — same harness, same prompts)

Measured with lm-eval HF backend on the same 7× RTX 3090 machine, identical flags both sides (no chat-template on MC tasks — required for thinking models).

How to read this

• ARC / MMLU Δ ≈ 0 → the SME fine-tune does not degrade general knowledge or science reasoning — capability is preserved at ~100%.
• GSM8K +21 pts → math / step-by-step reasoning improved significantly, thanks to the think-tagged, reasoning-heavy training data.

Sanity check: the base model's own ARC (0.5667) and MMLU (0.8477) match the known-good baseline for this model family, confirming the harness was correct (not a below-chance artifact).

KL divergence (base vs merged)

Low KL (0.04–0.15) + argmax agreement on all prompts = the merge is faithful; no catastrophic shift from fine-tuning.

🛠️ Tool-Calling (validated)

Given a document/table query, the model reasons first, then emits a correctly formatted <tool_call>. Verified output:

User: Which rows in /reports/expenses_2025.csv have department = Sales?

<tool_call>
<function=csv_filter>
<parameter=path>
/reports/expenses_2025.csv
</parameter>
<parameter=column>
department
</parameter>
<parameter=op>
eq
</parameter>
<parameter=value>
Sales
</parameter>
</function>
</tool_call>

• ✅ Correct <tool_call> / <function> / <parameter> structure
• ✅ No infinite loop — exactly one tool call per decision
• ✅ Reasoning precedes each tool call

🧠 Architecture

• Class: Qwen3_5ForConditionalGeneration (multimodal wrapper, text-only use here)
• 64 hidden layers = 48 linear-attention (GDN) + 16 full-attention (pattern: 3 GDN + 1 full, repeated)
• MTP head preserved (mtp_num_hidden_layers=1, 15 MTP tensors)
• BF16 — required for GDN (linear_attn) layers (FP16 → NaN grad norms)

🔬 Fine-Tuning Details

Dataset composition (hotdogs/sme-sft-docdata-qwen38):

• 8,827 rows (7,799 train / 1,028 valid)
• 30% tool-call rows, 70% QA rows
• 100% assistant messages have reasoning (think-tagged)
• 82.7% evidence-chained answers, 0% byte-exact duplicates
• Tools: csv_aggregate, csv_filter, csv_head, csv_stats, doc_compare, doc_metadata, doc_read, doc_search, doc_summarize, and more

🚀 Usage

Transformers (Python)

import torch
from transformers import AutoTokenizer, AutoModelForImageTextToText

MODEL = "hotdogs/Qwen3.8-27B-abliterated-sme-preview"
tok = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    MODEL, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)

prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Which rows in expenses_2025.csv have department = Sales?"}],
    tokenize=False, add_generation_prompt=True
)
ids = tok(prompt, return_tensors="pt").to("cuda")["input_ids"]
out = model.generate(ids, max_new_tokens=256, do_sample=False,
                     pad_token_id=tok.pad_token_id, eos_token_id=tok.eos_token_id)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))

Note: this is a multimodal model (vision wrapper present). To use it as a plain text assistant, load with AutoModelForImageTextToText and pass text-only messages as above. Do not call tokenizer(text) on the raw processor with a plain string in a way that routes to the vision path — use apply_chat_template.

GGUF / llama.cpp

A f16 GGUF with the MTP head intact is available. Convert instructions for custom quants (Q4_K_M / IQ3_M etc.):

PYTHONPATH=/path/to/llama.cpp/gguf-py python3 /path/to/llama.cpp/convert_hf_to_gguf.py \
  <HF_download_dir> --outfile model-f16.gguf --outtype f16
# then quantize, e.g.:
/path/to/llama.cpp/llama-quantize model-f16.gguf model-Q4_K_M.gguf Q4_K_M

Requires a llama.cpp build that supports the qwen35 architecture (GDN + linear-attention). Keep MTP (do not pass --no-mtp — the model has MTP tensors, block_count must be 64 + nextn).

📁 Files

⚠️ Notes & Limitations

• Preview release — validated on ARC/MMLU/GSM8K + tool-call smoke tests, not yet on full agentic benchmarks (e.g. Terminal-Bench / SWE-Bench).
• Abliterated base: safety-tuning refusals are reduced by design (λ=1.2 kept capability while cutting refusals 98%→39%). Exercise judgment for harmful use.
• Document tools (csv_*, doc_*) are emitted as structured calls — you must wire them to a runtime (e.g. a function-calling agent loop) to actually execute them.
• 27B BF16 needs a multi-GPU setup (or a quantized GGUF) for practical inference.

📜 License

Apache-2.0. Base model: hotdogs/Qwen3.8-27B-abliterated (λ=1.2 abliteration); fine-tune method follows the Train-Studio (unsloth + HF Trainer) recipe.

Built with ❤️ on 7× RTX 3090 — SFT via unsloth + HF Trainer, merged with MTP preserved, benchmarked A/B against base.