Jmelfreich/MSFIT-9B-v4

MSFIT-9B v4

This model has been superseded by MSFIT-9B-v8. v8 uses the same prompt format but plans each spot in a <think> block before writing — with trained self-correction — and produces markedly more coherent scripts. New projects should start there.

MSFIT is a commercial (TV/online ad) script writer fine-tuned from Qwen/Qwen3.5-9B. Given a brand dossier and a campaign brief, it writes a production-ready spot in a strict three-section format — Setting, Characters, Script — with cinematic prose, bold character names, parenthetical delivery, dialogue, visual action, SFX, music cues, supers, and a complete ending.

This is the v4 merge: a rank-256 LoRA (run 4) merged into the base model at 16-bit. The repo contains both the full merged weights (safetensors) and a Q5_K_M GGUF for llama.cpp / Ollama.

What it does

• Writes original commercial scripts grounded in a brand dossier you provide.
• Honors creative parameters when supplied: format, tone, structure, character count, production devices (SFX, music, VO, supers, snaps), celebrity, mascot, and target length (15/30/60 seconds).
• Treats "Approved supers" as pre-approved legal/brand copy: uses every one verbatim and places them itself, without inventing new ones.

Prompting guide

MSFIT was trained on one strict input structure. Follow it exactly — free-form requests will underperform.

1. System prompt (always use this, verbatim)

You are MSFIT, an elite commercial script writer. When a request begins with MSFIT, write a production-ready, original commercial script tailored to the brand dossier and campaign brief. When Creative parameters are provided (format, tone, structure, character count, production devices, celebrity, mascot), honor them so the script matches the requested style. When Approved supers are provided, they are pre-approved legal/brand copy: use every one of them, reproduce each verbatim with no edits, decide the best placement yourself (the list order is arbitrary), and never invent supers that are not on the list. Return only three sections in this order: Setting, Characters, and Script. Use detailed cinematic prose, bold character names, parenthetical delivery, dialogue, visual action, SFX, music, supers, and the complete ending.

(The included Ollama Modelfile bakes this in already.)

2. User message anatomy

The user message is four blocks, in this order, separated by blank lines. Blocks 3 and 4 are optional.

BRAND DOSSIER

<markdown dossier: who the brand is, positioning, brand voice, creative territory>

MSFIT
Brand: <brand name>
Category: <product/occasion category>
Target length: <15 | 30 | 60> seconds
Write an original commercial script.

Creative parameters:
Format: <one primary format>
Tone: <one or two tones, comma-separated>
Structure: <one narrative structure>
Characters: <integer — total named characters, narrator included if there is a voiceover>
Devices: <any of: supers, SFX, music, sonic snap, voiceover, dialogue-heavy>
Celebrity: <name(s), only if you want one>
Mascot character: yes   <- only if you want a brand mascot>

Approved supers (use every one exactly as written; order here is arbitrary, placement is your call):
- <SUPER TEXT ONE>
- <SUPER TEXT TWO>

Rules:

• The dossier comes first, under the literal heading BRAND DOSSIER. It grounds every claim in the script; leave out facts you can't verify (URLs, addresses, phone numbers, prices, ratings) rather than inventing them — the model will happily use whatever you give it.
• MSFIT on its own line is the trigger. The lines after it carry only brand, category, and target length. Training used lengths of 15, 30, or 60 seconds — other values are off-distribution.
• Creative parameters: is optional, and so is every line inside it. Omit the whole block to let the model choose its own creative direction. Include only the lines you want to constrain.
• Approved supers is optional. If present, use the exact header line shown above. Every listed super will appear verbatim in the script, and the model will not invent extra ones. Only include it when Devices: includes supers.

3. Controlled vocabulary

These are the values the model saw in training. Other words will still parse, but these work best.

Field	Allowed values
Format	comedy, emotional, informative, cinematic_drama, testimonial, musical, absurdist, action_spectacle, inspirational, lifestyle
Tone (pick 1–2)	humorous, heartwarming, dramatic, irreverent, aspirational, suspenseful, nostalgic, energetic, sincere, quirky
Structure	single_scene, vignette_montage, problem_solution, day_in_life, dialogue_driven, voiceover_narration, product_showcase
Devices	supers, SFX, music, sonic snap, voiceover, dialogue-heavy

4. Reference prompt (minimal)

Just a dossier and the trigger — the model picks the creative direction:

BRAND DOSSIER

# Northstar Coffee

## Description
Northstar Coffee is a small-batch roaster in Duluth, Minnesota, founded by two
former ship engineers. Known for dark, smoky roasts and tin-can packaging.
Brand voice is rugged, warm, and a little dry-witted.

## Competition & Positioning
Competes with third-wave cafes and grocery-store premium brands. Differentiator
is provenance: beans roasted dockside on Lake Superior, built for cold mornings.

### Elevator Pitch
Northstar Coffee makes the dark, honest cup that gets working people through
northern winters.

MSFIT
Brand: Northstar Coffee
Category: Coffee
Target length: 30 seconds
Write an original commercial script.

5. Reference prompt (fully specified)

Every optional block in use:

BRAND DOSSIER

# Owl Cafe

## Description
The Owl Cafe (the Owl Bar & Cafe) is a historic roadside diner in San Antonio,
New Mexico, at the crossroads of I-25 and US-380. Open since 1945, it is
celebrated as the home of one of the world's first and finest green chile
cheeseburgers — a flat-top smashed patty crowned with roasted Hatch green
chile — and for the legend that Manhattan Project scientists cooled off at its
bar before and after the Trinity test. The brand voice is warm, wry, and
unhurried Americana: proud of its history, unimpressed by fads, and certain
that some things — a hot griddle, real green chile, a cold beer — never need
improving.

## Competition & Positioning
The Owl Cafe competes with interstate fast-food chains and with New Mexico's
other green chile burger institutions. Its differentiators are provenance and
patience. Creative territory can explore "the detour worth making" and the
idea that the middle of nowhere is exactly where the best burger in America
would hide.

### Elevator Pitch
The Owl Cafe serves the legendary green chile cheeseburger that scientists,
ranchers, and road-trippers have detoured for since 1945.

MSFIT
Create a strong commercial for Owl Cafe.
Category: Restaurant / Roadside Diner
Target length: 30 seconds

Creative parameters:
Format: action_spectacle
Tone: energetic, dramatic
Structure: vignette_montage
Characters: 2
Devices: supers, SFX, music, voiceover
Celebrity: Dwayne Johnson

Approved supers (use every one exactly as written; order here is arbitrary, placement is your call):
- 60 MILES FROM ANYWHERE
- WORTH EVERY ONE OF THEM
- OWL CAFE - SAN ANTONIO, NEW MEXICO

6. What the output looks like

The model may first reason inside a <think>...</think> block, then emit exactly three sections:

Setting

<one paragraph describing the world of the spot>

Characters

**NAME** — <description>
**NARRATOR** — <description, present when there is a voiceover>

Script

[MUSIC: <cue>]
[SFX: <effect>]
[SONIC SNAP]
[SUPER: <on-screen text>]

**NAME** (<delivery>): "<dialogue line>"
**NARRATOR** (V.O.): "<voiceover line>"

<visual action in prose>

Sampling defaults it was tuned around: temperature 0.85, top_p 0.95.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Jmelfreich/MSFIT-9B-v4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},  # section 1 of the prompting guide
    {"role": "user", "content": USER_PROMPT},      # sections 2-5: dossier + MSFIT trigger + parameters
]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=2048, temperature=0.85, top_p=0.95)
print(tokenizer.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Ollama (GGUF)

The repo includes msfit-Q5_K_M.gguf (text-only). You can run it directly from the Hub:

ollama run hf.co/Jmelfreich/MSFIT-9B-v4

Or download the GGUF and register it with the included Modelfile, which bakes in the system prompt and sampling defaults (temperature 0.85, top_p 0.95, 8192 context):

ollama create msfit-writer -f Modelfile
ollama run msfit-writer

Training details

• Base model: Qwen/Qwen3.5-9B
• Method: supervised fine-tuning with a bf16 LoRA via Unsloth (Qwen3.5's maintainers advise against 4-bit QLoRA for this architecture)
• LoRA: rank 256, alpha 512, dropout 0.05, on q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Schedule: 2 epochs, lr 1e-4, cosine decay, warmup ratio 0.03, effective batch size 8, sequence length 4096 with packing
• Merge: LoRA merged into the base at 16-bit (merged_16bit), then converted with llama.cpp and quantized to Q5_K_M

The training data consists of commercial scripts reconstructed from reference TV/online ads: each ad's visuals, dialogue, sound design, supers, and ending were reconstructed by a video-understanding model, normalized into the exact Setting / Characters / Script schema, audited for language and completeness, deduplicated, and bucketed into 15-, 30-, and 60-second spots, paired with grounded brand dossiers.

Notes and limitations

• English only, and specialized for the commercial-script format; it is not a general assistant.
• The base architecture is multimodal, but MSFIT was fine-tuned as a text-only script writer; the GGUF is text-only.
• Scripts are creative fiction: verify any factual, legal, or brand claims before production. Supers you pass as "Approved supers" are reproduced verbatim, so make sure they are actually approved.

License

Apache 2.0, inherited from Qwen3.5-9B.