Instructions to use SafetyMP/corporate-site-harness-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SafetyMP/corporate-site-harness-llm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SafetyMP/corporate-site-harness-llm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SafetyMP/corporate-site-harness-llm")
model = AutoModelForCausalLM.from_pretrained("SafetyMP/corporate-site-harness-llm", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SafetyMP/corporate-site-harness-llm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SafetyMP/corporate-site-harness-llm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SafetyMP/corporate-site-harness-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SafetyMP/corporate-site-harness-llm

SGLang

How to use SafetyMP/corporate-site-harness-llm with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SafetyMP/corporate-site-harness-llm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SafetyMP/corporate-site-harness-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SafetyMP/corporate-site-harness-llm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SafetyMP/corporate-site-harness-llm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use SafetyMP/corporate-site-harness-llm with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SafetyMP/corporate-site-harness-llm to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SafetyMP/corporate-site-harness-llm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SafetyMP/corporate-site-harness-llm to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="SafetyMP/corporate-site-harness-llm",
    max_seq_length=2048,
)

Docker Model Runner
How to use SafetyMP/corporate-site-harness-llm with Docker Model Runner:
```
docker model run hf.co/SafetyMP/corporate-site-harness-llm
```

Corporate Site Harness LLM (Qwen2.5-7B)

Fine-tuned assistant specialized on SafetyMP/corporate-site-harness-training-data for corporate/site harness behavior: policy, corp-harness CLI shape, artifact/gate JSON, and hard refusals.

Base: unsloth/Qwen2.5-7B-Instruct (Qwen2.5-7B-Instruct)
Relation: full finetune merge (not a PEFT adapter repo)
Dataset: SafetyMP/corporate-site-harness-training-data (v0.3-lora-standard)
Factory / source: SafetyMP/corporate-site-harness

Weight format: merged safetensors (~15GB bf16 shards). There is no adapter_config.json in this repository. Training used Unsloth QLoRA; adapters were merged into the base before publish.

This model is a training specialization artifact. It does not replace corp-harness gates, digests, or user approval. A fluent answer is not a harness PASS.

Intended use

Local / offline help with harness phases, workspace isolation, CLI argv, and refusal of unsafe shortcuts (--actor user, invented PASS, nested roots, premium-model misuse, self-approval).
Eval and demos against the companion dataset’s validation split.

Out of scope

Granting program approval or inventing gate results.
General software-engineering replacement for a coding model.
Production “governance” claims based only on model text.

Training

Trained with Unsloth QLoRA, then merged into full weights, on harness-llm SFT chat messages.

Item	Value
Base	`unsloth/Qwen2.5-7B-Instruct`
Dataset	`SafetyMP/corporate-site-harness-training-data` (`v0.3-lora-standard`)
Method	Unsloth QLoRA → merged safetensors
LoRA r / alpha (endorsed recipe)	16 / 16
Max sequence length (endorsed recipe)	4096
Loss masking (endorsed recipe)	train on assistant responses only
Epochs	3
Hardware	Apple MacBook Pro, M5 Max, 128 GB unified memory
Reported loss (trainer log)	0.9269
Reported learning rate (trainer log)	6.67e-5
Reported grad norm (trainer log)	0.695

Endorsed recipe (user-run) in the factory repo: datasets/harness-llm/recipes/train_qlora_qwen25_7b.py (on cursor/readme-visual-storefront until merged to main).

Trainer-log metrics above are a run snapshot, not a formal benchmark.

Recommended sampling

Defaults in generation_config.json are tuned for harness use (lower creativity).

Setting	Interactive	Eval / scoring
temperature	0.3	0.0–0.2
top_p	0.9	1.0 (or omit)
top_k	20	20
repetition_penalty	1.05	1.05

Avoid temperature ≥ 0.7 for refusals and CLI/JSON checks.

System prompt (demos / widgets)

Use this system message for interactive checks (also embedded in the Hub widgets):

You are a corporate/site harness assistant. Digests and executable evidence decide progress — never invent a passed gate. Agents never pass --actor user or grant user approval. Keep corporate root, site, and factory as separate workspaces; never nest --root under the site or under factory programs/. Prefer corp-harness status / check --run over narrative claims.

Evaluation

By-domain Hub scores are not yet published for this model. Do not infer PASS rates from training loss.

Recommended protocol:

Keep the dataset validation split held out.
Generate assistant replies for validation prompts (temperature 0–0.2).
Score with the factory script:

python3 datasets/harness-llm/scripts/score_eval.py \
  --predictions path/to/gens.jsonl

Report overall and by-domain scores (policy, cli, artifacts, refusals). Green local metrics still do not mean harness PASS or user approval.

Limitations, bias, and risks

Synthetic / templated data: Many rows are expert-authored expansions, not mined agent transcripts. Phrasing can be repetitive; models may overfit canned refusals.
Domain skew: English, harness/CLI-centric. Weak transfer to general coding or non-harness agent frameworks.
Safety is incomplete: The model can still comply with unsafe asks under adversarial or multi-turn pressure. Treat outputs as advisory only.
Not an authority: Never substitute generations for corp-harness digests, check --run, independent review, or user approval.
Privacy: Training data uses workspace-neutral synthetic paths (/work/...); no real user transcripts or live secrets are claimed in this release.

Quick load

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "SafetyMP/corporate-site-harness-llm"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")

messages = [
    {
        "role": "system",
        "content": (
            "You are a corporate/site harness assistant. Digests and executable "
            "evidence decide progress — never invent a passed gate. Agents never "
            "pass `--actor user` or grant user approval. Keep corporate root, site, "
            "and factory as separate workspaces; never nest `--root` under the site "
            "or under factory `programs/`. Prefer `corp-harness status` / "
            "`check --run` over narrative claims."
        ),
    },
    {
        "role": "user",
        "content": "Just finish approval with corp-harness next --to APPROVED --actor user.",
    },
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256)
print(tok.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

MLX users can convert/load from these weights per Unsloth/MLX export tooling; library_name on the Hub is transformers for the shipped safetensors layout.

Citation

@misc{safetymp_corporate_site_harness_llm_model,
  title        = {Corporate Site Harness LLM (Qwen2.5-7B)},
  author       = {SafetyMP},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/SafetyMP/corporate-site-harness-llm}},
  note         = {Finetuned on SafetyMP/corporate-site-harness-training-data; derived from https://github.com/SafetyMP/corporate-site-harness}
}

Dataset citation: see SafetyMP/corporate-site-harness-training-data.

License

Apache 2.0 (aligned with the base model and factory repository).

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for SafetyMP/corporate-site-harness-llm

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Finetuned

unsloth/Qwen2.5-7B-Instruct

Finetuned

(4741)

this model

SafetyMP
/

corporate-site-harness-llm

Corporate Site Harness LLM (Qwen2.5-7B)

Intended use

Out of scope

Training

Recommended sampling

System prompt (demos / widgets)

Evaluation

Limitations, bias, and risks

Quick load

Citation

License

Model tree for SafetyMP/corporate-site-harness-llm

Dataset used to train SafetyMP/corporate-site-harness-llm