Instructions to use morningstarxcdcode/adaption-no-robots-instructions-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use morningstarxcdcode/adaption-no-robots-instructions-model with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("togethercomputer/gpt-oss-120b-bf16")
model = PeftModel.from_pretrained(base_model, "morningstarxcdcode/adaption-no-robots-instructions-model")

Transformers

How to use morningstarxcdcode/adaption-no-robots-instructions-model with Transformers:

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

pipe = pipeline("text-generation", model="morningstarxcdcode/adaption-no-robots-instructions-model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("morningstarxcdcode/adaption-no-robots-instructions-model")
model = AutoModelForCausalLM.from_pretrained("morningstarxcdcode/adaption-no-robots-instructions-model", 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 morningstarxcdcode/adaption-no-robots-instructions-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "morningstarxcdcode/adaption-no-robots-instructions-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "morningstarxcdcode/adaption-no-robots-instructions-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/morningstarxcdcode/adaption-no-robots-instructions-model

SGLang

How to use morningstarxcdcode/adaption-no-robots-instructions-model 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 "morningstarxcdcode/adaption-no-robots-instructions-model" \
    --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": "morningstarxcdcode/adaption-no-robots-instructions-model",
		"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 "morningstarxcdcode/adaption-no-robots-instructions-model" \
        --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": "morningstarxcdcode/adaption-no-robots-instructions-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use morningstarxcdcode/adaption-no-robots-instructions-model with Docker Model Runner:
```
docker model run hf.co/morningstarxcdcode/adaption-no-robots-instructions-model
```

adaption-no-robots-instructions-model

File size: 1,942 Bytes

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---
language:
- en
license: apache-2.0
tags:
- lora
- peft
- language
- instruction-following
- no-robots
- gpt-oss
- sft
- transformers
pipeline_tag: text-generation
base_model: togethercomputer/gpt-oss-120b-bf16
---

# Adaption No Robots Instructions SFT 120B

LoRA adapter fine-tuned on the No Robots instruction-following dataset using Adaption's AutoScientist platform.

## Model Details

- **Base model:** `togethercomputer/gpt-oss-120b-bf16` (120B parameter MoE, 128 experts, 4 per token)
- **Adapter:** LoRA rank 4, alpha 8, targeting `q_proj` and `v_proj`
- **Training data:** 10,000 human-written instruction-response pairs (No Robots dataset)
- **Training:** 1 epoch, 22 steps, loss 2.22 → 1.40
- **Eval loss:** 1.93 → 1.41

## Training Results

| Metric | Before | After |
|--------|--------|-------|
| Quality | 7.0 | 7.5 (+7.1%) |
| Grade | C | B |
| General Win Rate | 41% | 59% |
| Dataset Win Rate | 54% | 46% |

## How to Use

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "togethercomputer/gpt-oss-120b-bf16",
    torch_dtype="bfloat16",
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "morningstarxcdcode/adaption-no-robots-instructions-model")
tokenizer = AutoTokenizer.from_pretrained("morningstarxcdcode/adaption-no-robots-instructions-model")

inputs = tokenizer("Write a short story about a robot learning to cook.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Configuration

- Optimizer: AdamW
- Learning rate: 1e-4 with cosine decay
- Batch size: 1
- Max grad norm: 1.0
- Warmup steps: 4

## Team

Sourav Rajak, Priyanshu Tomar, Roshan G, Vivek Rajput

Part of the AutoScientist Challenge — Healthcare, Finance, Language, Legal, and Marketing tracks.