Qwen2.5-1.5B TechWriter (merged)

Merged QLoRA fine-tune of Qwen/Qwen2.5-1.5B-Instruct for semiconductor and data-center interconnect technical-marketing and documentation style: product briefs, datasheets, application notes, and user-guide CLI sections.

This is the inference repo. Load it with AutoModelForCausalLM or pipeline("text-generation").

This is an unofficial specialist checkpoint trained on a private mix of extracted vendor PDFs plus cleaned synthetic docs. It will still invent SKUs if you ask it to write a brief for a product that was never in the gold data.

Adapter-only (smaller download, resume / compose):

Shankarblr/Qwen2.5-1.5B-TechWriter-LoRA

What it is for

• Product briefs, datasheet feature lists, application notes
• Host-adapter / switch CLI user-guide sections
• Spec extraction and short grounded QA over a pasted excerpt
• Internal draft generation that should stay on-genre and internally consistent (one process node, one primary throughput, one form factor)

Not for:

• Authoritative datasheet numbers without a human / source check
• Legal, safety, or customer-facing specs shipped as-is
• Languages other than English
• General chat outside semiconductor interconnect / storage / DPU content

Load and run

Use the Qwen2.5 Instruct chat template. Do not hand-roll ChatML if the tokenizer already has a template.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

REPO = "Shankarblr/Qwen2.5-1.5B-TechWriter-Instruct"

tokenizer = AutoTokenizer.from_pretrained(REPO)
model = AutoModelForCausalLM.from_pretrained(
    REPO,
    torch_dtype=torch.float16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": (
            "You are a technical marketing and documentation writer for semiconductor "
            "and data-center interconnect products. Write clear, structured content. "
            "Match the requested document type. Keep specifications internally consistent: "
            "one process node, one primary throughput, and one form factor unless the "
            "source explicitly lists options. Do not invent conflicting SKUs or CLI syntax."
        ),
    },
    {
        "role": "user",
        "content": "Draft a product brief covering a 40/50/100GbE converged network adapter series.",
    },
]

prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
out = model.generate(
    **inputs,
    max_new_tokens=1024,
    do_sample=False,
    pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Pipeline

from transformers import pipeline, AutoTokenizer

REPO = "Shankarblr/Qwen2.5-1.5B-TechWriter-Instruct"
tok = AutoTokenizer.from_pretrained(REPO)

pipe = pipeline(
    "text-generation",
    model=REPO,
    tokenizer=tok,
    max_new_tokens=1024,
    do_sample=False,
)

prompt = tok.apply_chat_template(
    [
        {"role": "system", "content": "You are a technical marketing and documentation writer for semiconductor and data-center interconnect products."},
        {"role": "user", "content": "For an enterprise 1/10/25GbE Ethernet switch series, author the Key Features portion of the product brief."},
    ],
    tokenize=False,
    add_generation_prompt=True,
)
print(pipe(prompt, return_full_text=False)[0]["generated_text"])

If you see Both max_new_tokens and max_length(=20), the shipped generation_config.json still has a leftover max_length. Prefer passing only max_new_tokens at generate time, or edit that file before upload.

Training

Eval (teacher-forced next token, not open generation)

Mean train loss over the full run: 0.3846 (early epochs are higher; late-epoch train batches sit around 0.12–0.14). Grad norms stayed small (~0.07–0.14). Cosine LR decayed from ~5e-5 at epoch 2.02 to ~6e-9 at the last step.

Read token accuracy correctly. 94.9% is “the model assigned the gold next token” on the held-out ChatML strings. It is not factual accuracy on silicon SKUs, and it is not a win-rate against a human editor.

Dataset mix

6,765 rows after dropping dirty synthetic gold (multi-node, multi-throughput, grammar doubles, stubs).

Doc types: user_guide 2,772 · product_brief 2,528 · datasheet 613 · application_note 305 · technology_brief 229 · white_paper 222 · competitive_report 96.

Extracted PDFs cover converged network adapters, Ethernet switch silicon, host-adapter CLI, storage / fabric features, and related interconnect material. A large share of generate gold is still synthetic house-style prose, so the model can emit invented series names if the prompt does.

Intended prompt style

The train-time system prompts were:

1. Writer — semiconductor / interconnect house style; one process node / throughput / form factor.
2. Editor — revise copy; do not add contradictory specs.
3. Product specialist — answer only from the pasted excerpt; otherwise say you cannot determine it.

Match those at inference. Grounded QA quality collapses if you drop the specialist system prompt.

Limitations

• 1.5B parameters. Long, consistent datasheets still drift.
• Synthetic SKUs in the pre-clean pool taught a habit of inventing product families. The recommended set drops the worst contradictions; it does not delete every invented family.
• Extracted CLI gold is the reliable part. Treat generated flags and identifiers as drafts.
• No DPO / GRPO on this published checkpoint (those datasets exist separately).
• Eval is teacher-forced loss/accuracy only.

Files to upload (merged repo)

Do not upload checkpoint-*, optimizer.pt, rng_state.pth, or the 4-bit train-time weights.

Related

• Base: Qwen/Qwen2.5-1.5B-Instruct
• Method: QLoRA (NF4) + PEFT LoRA, TRL SFT
• Adapter: Shankarblr/Qwen2.5-1.5B-TechWriter-LoRA
• Sister run (same data/recipe): Shankarblr/Llama-3.2-3B-TechWriter-Instruct

License

Apache 2.0, same family as the Qwen2.5-1.5B Instruct weights. This checkpoint is an unofficial style model and is not affiliated with any semiconductor vendor.